untitled.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/interim/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bureau_114</th>\n",
       "      <th>bureau_39</th>\n",
       "      <th>onus_attribute_35</th>\n",
       "      <th>bureau_237</th>\n",
       "      <th>bureau_enquiry_3</th>\n",
       "      <th>bureau_350</th>\n",
       "      <th>onus_attribute_26</th>\n",
       "      <th>bureau_155</th>\n",
       "      <th>bureau_123</th>\n",
       "      <th>bureau_45</th>\n",
       "      <th>...</th>\n",
       "      <th>bureau_205</th>\n",
       "      <th>bureau_396</th>\n",
       "      <th>bureau_166</th>\n",
       "      <th>bureau_420</th>\n",
       "      <th>bureau_157</th>\n",
       "      <th>onus_attribute_18</th>\n",
       "      <th>bureau_452</th>\n",
       "      <th>bureau_389</th>\n",
       "      <th>bureau_360</th>\n",
       "      <th>bad_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>-0.467700</td>\n",
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       "      <td>-1.379545</td>\n",
       "      <td>-0.801743</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1893</td>\n",
       "      <td>0.182924</td>\n",
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       "      <td>-0.252035</td>\n",
       "      <td>0.134016</td>\n",
       "      <td>-0.234124</td>\n",
       "      <td>1.824346</td>\n",
       "      <td>-0.147489</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.725115</td>\n",
       "      <td>1.113889</td>\n",
       "      <td>-0.419383</td>\n",
       "      <td>-0.481929</td>\n",
       "      <td>0.395134</td>\n",
       "      <td>-0.268109</td>\n",
       "      <td>-0.467700</td>\n",
       "      <td>-0.205377</td>\n",
       "      <td>0.857111</td>\n",
       "      <td>1.438194</td>\n",
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       "      <td>0.191691</td>\n",
       "      <td>-0.558160</td>\n",
       "      <td>-0.147489</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.952455</td>\n",
       "      <td>-0.688752</td>\n",
       "      <td>-0.419383</td>\n",
       "      <td>-0.481929</td>\n",
       "      <td>-0.929449</td>\n",
       "      <td>-0.268109</td>\n",
       "      <td>2.138123</td>\n",
       "      <td>-0.205377</td>\n",
       "      <td>-0.551449</td>\n",
       "      <td>-0.801743</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1893</td>\n",
       "      <td>-0.984468</td>\n",
       "      <td>-0.098221</td>\n",
       "      <td>-0.549764</td>\n",
       "      <td>-0.252035</td>\n",
       "      <td>0.134016</td>\n",
       "      <td>0.726412</td>\n",
       "      <td>-0.558160</td>\n",
       "      <td>-0.147489</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.805383</td>\n",
       "      <td>-0.688752</td>\n",
       "      <td>-0.419383</td>\n",
       "      <td>-0.481929</td>\n",
       "      <td>-0.929449</td>\n",
       "      <td>-0.268109</td>\n",
       "      <td>-0.467700</td>\n",
       "      <td>-0.205377</td>\n",
       "      <td>1.648744</td>\n",
       "      <td>-0.801743</td>\n",
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       "      <td>-0.1893</td>\n",
       "      <td>1.850579</td>\n",
       "      <td>-0.098221</td>\n",
       "      <td>-0.549764</td>\n",
       "      <td>-0.252035</td>\n",
       "      <td>0.134016</td>\n",
       "      <td>-0.849664</td>\n",
       "      <td>-0.558160</td>\n",
       "      <td>-0.147489</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.952455</td>\n",
       "      <td>1.294163</td>\n",
       "      <td>-0.419383</td>\n",
       "      <td>2.074994</td>\n",
       "      <td>0.395134</td>\n",
       "      <td>3.729824</td>\n",
       "      <td>2.138123</td>\n",
       "      <td>-0.205377</td>\n",
       "      <td>1.056631</td>\n",
       "      <td>1.776454</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1893</td>\n",
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       "      <td>1.957235</td>\n",
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       "      <td>1.853151</td>\n",
       "      <td>-0.147489</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>92992</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>...</td>\n",
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       "      <th>92994</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>92995</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92996 rows × 277 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       bureau_114  bureau_39  onus_attribute_35  bureau_237  bureau_enquiry_3  \\\n",
       "0        0.952455  -0.688752          -0.419383   -0.481929         -0.929449   \n",
       "1        1.725115   1.113889          -0.419383   -0.481929          0.395134   \n",
       "2        0.952455  -0.688752          -0.419383   -0.481929         -0.929449   \n",
       "3       -0.805383  -0.688752          -0.419383   -0.481929         -0.929449   \n",
       "4        0.952455   1.294163          -0.419383    2.074994          0.395134   \n",
       "...           ...        ...                ...         ...               ...   \n",
       "92991         NaN        NaN                NaN         NaN               NaN   \n",
       "92992         NaN        NaN                NaN         NaN               NaN   \n",
       "92993         NaN        NaN                NaN         NaN               NaN   \n",
       "92994         NaN        NaN                NaN         NaN               NaN   \n",
       "92995         NaN        NaN                NaN         NaN               NaN   \n",
       "\n",
       "       bureau_350  onus_attribute_26  bureau_155  bureau_123  bureau_45  ...  \\\n",
       "0       -0.268109          -0.467700   -0.205377   -1.379545  -0.801743  ...   \n",
       "1       -0.268109          -0.467700   -0.205377    0.857111   1.438194  ...   \n",
       "2       -0.268109           2.138123   -0.205377   -0.551449  -0.801743  ...   \n",
       "3       -0.268109          -0.467700   -0.205377    1.648744  -0.801743  ...   \n",
       "4        3.729824           2.138123   -0.205377    1.056631   1.776454  ...   \n",
       "...           ...                ...         ...         ...        ...  ...   \n",
       "92991         NaN                NaN         NaN         NaN        NaN  ...   \n",
       "92992         NaN                NaN         NaN         NaN        NaN  ...   \n",
       "92993         NaN                NaN         NaN         NaN        NaN  ...   \n",
       "92994         NaN                NaN         NaN         NaN        NaN  ...   \n",
       "92995         NaN                NaN         NaN         NaN        NaN  ...   \n",
       "\n",
       "       bureau_205  bureau_396  bureau_166  bureau_420  bureau_157  \\\n",
       "0         -0.1893    0.182924   -0.098221    1.985530   -0.252035   \n",
       "1         -0.1893    0.857987   -0.098221   -0.549764   -0.252035   \n",
       "2         -0.1893   -0.984468   -0.098221   -0.549764   -0.252035   \n",
       "3         -0.1893    1.850579   -0.098221   -0.549764   -0.252035   \n",
       "4         -0.1893    1.520169   -0.098221    1.957235   -0.252035   \n",
       "...           ...         ...         ...         ...         ...   \n",
       "92991         NaN         NaN         NaN         NaN         NaN   \n",
       "92992         NaN         NaN         NaN         NaN         NaN   \n",
       "92993         NaN         NaN         NaN         NaN         NaN   \n",
       "92994         NaN         NaN         NaN         NaN         NaN   \n",
       "92995         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "       onus_attribute_18  bureau_452  bureau_389  bureau_360  bad_flag  \n",
       "0               0.134016   -0.234124    1.824346   -0.147489       0.0  \n",
       "1               0.134016    0.191691   -0.558160   -0.147489       NaN  \n",
       "2               0.134016    0.726412   -0.558160   -0.147489       0.0  \n",
       "3               0.134016   -0.849664   -0.558160   -0.147489       0.0  \n",
       "4               0.134016   -0.175450    1.853151   -0.147489       0.0  \n",
       "...                  ...         ...         ...         ...       ...  \n",
       "92991                NaN         NaN         NaN         NaN       0.0  \n",
       "92992                NaN         NaN         NaN         NaN       0.0  \n",
       "92993                NaN         NaN         NaN         NaN       0.0  \n",
       "92994                NaN         NaN         NaN         NaN       0.0  \n",
       "92995                NaN         NaN         NaN         NaN       0.0  \n",
       "\n",
       "[92996 rows x 277 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('data/raw/Dev_data_to_be_shared.csv')\n",
    "test_df = pd.read_csv('data/raw/validation_data_to_be_shared.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import yaml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning\n",
    "\n",
    "1216 features (We are fucked !)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/raw/Dev_data_to_be_shared.csv')\n",
    "val_df = pd.read_csv('data/raw/validation_data_to_be_shared.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account_number</th>\n",
       "      <th>bad_flag</th>\n",
       "      <th>onus_attribute_1</th>\n",
       "      <th>transaction_attribute_1</th>\n",
       "      <th>transaction_attribute_2</th>\n",
       "      <th>transaction_attribute_3</th>\n",
       "      <th>transaction_attribute_4</th>\n",
       "      <th>transaction_attribute_5</th>\n",
       "      <th>transaction_attribute_6</th>\n",
       "      <th>transaction_attribute_7</th>\n",
       "      <th>...</th>\n",
       "      <th>bureau_enquiry_47</th>\n",
       "      <th>bureau_enquiry_48</th>\n",
       "      <th>bureau_enquiry_49</th>\n",
       "      <th>bureau_enquiry_50</th>\n",
       "      <th>onus_attribute_43</th>\n",
       "      <th>onus_attribute_44</th>\n",
       "      <th>onus_attribute_45</th>\n",
       "      <th>onus_attribute_46</th>\n",
       "      <th>onus_attribute_47</th>\n",
       "      <th>onus_attribute_48</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>221000.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>86000.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>96801</th>\n",
       "      <td>96802</td>\n",
       "      <td>0</td>\n",
       "      <td>156000.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96802</th>\n",
       "      <td>96803</td>\n",
       "      <td>0</td>\n",
       "      <td>46000.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>96803</th>\n",
       "      <td>96804</td>\n",
       "      <td>0</td>\n",
       "      <td>95000.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96804</th>\n",
       "      <td>96805</td>\n",
       "      <td>0</td>\n",
       "      <td>88000.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96805</th>\n",
       "      <td>96806</td>\n",
       "      <td>0</td>\n",
       "      <td>94000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96806 rows × 1216 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       account_number  bad_flag  onus_attribute_1  transaction_attribute_1  \\\n",
       "0                   1         0               NaN                      NaN   \n",
       "1                   2         0          221000.0                      0.0   \n",
       "2                   3         0           25000.0                      0.0   \n",
       "3                   4         0           86000.0                      0.0   \n",
       "4                   5         0          215000.0                      0.0   \n",
       "...               ...       ...               ...                      ...   \n",
       "96801           96802         0          156000.0                      0.0   \n",
       "96802           96803         0           46000.0                      0.0   \n",
       "96803           96804         0           95000.0                      0.0   \n",
       "96804           96805         0           88000.0                      0.0   \n",
       "96805           96806         0           94000.0                      0.0   \n",
       "\n",
       "       transaction_attribute_2  transaction_attribute_3  \\\n",
       "0                          NaN                      NaN   \n",
       "1                          0.0                      0.0   \n",
       "2                          0.0                      0.0   \n",
       "3                          0.0                      0.0   \n",
       "4                          0.0                      0.0   \n",
       "...                        ...                      ...   \n",
       "96801                      0.0                      0.0   \n",
       "96802                      0.0                      0.0   \n",
       "96803                      0.0                      0.0   \n",
       "96804                      0.0                      0.0   \n",
       "96805                      0.0                      0.0   \n",
       "\n",
       "       transaction_attribute_4  transaction_attribute_5  \\\n",
       "0                          NaN                      NaN   \n",
       "1                          0.0                      0.0   \n",
       "2                          0.0                      0.0   \n",
       "3                          0.0                      0.0   \n",
       "4                          0.0                      0.0   \n",
       "...                        ...                      ...   \n",
       "96801                      0.0                      0.0   \n",
       "96802                      0.0                      0.0   \n",
       "96803                      0.0                      0.0   \n",
       "96804                      0.0                      0.0   \n",
       "96805                      0.0                      0.0   \n",
       "\n",
       "       transaction_attribute_6  transaction_attribute_7  ...  \\\n",
       "0                          NaN                      NaN  ...   \n",
       "1                          0.0                      0.0  ...   \n",
       "2                          0.0                      0.0  ...   \n",
       "3                          0.0                      0.0  ...   \n",
       "4                          0.0                      0.0  ...   \n",
       "...                        ...                      ...  ...   \n",
       "96801                      0.0                      0.0  ...   \n",
       "96802                      0.0                      0.0  ...   \n",
       "96803                      0.0                      0.0  ...   \n",
       "96804                      0.0                      0.0  ...   \n",
       "96805                      0.0                      0.0  ...   \n",
       "\n",
       "       bureau_enquiry_47  bureau_enquiry_48  bureau_enquiry_49  \\\n",
       "0                    0.0                0.0                0.0   \n",
       "1                    0.0                0.0                2.0   \n",
       "2                    0.0                0.0                0.0   \n",
       "3                    0.0                0.0                0.0   \n",
       "4                    0.0                0.0                0.0   \n",
       "...                  ...                ...                ...   \n",
       "96801                0.0                0.0                0.0   \n",
       "96802                0.0                0.0                0.0   \n",
       "96803                0.0                0.0                0.0   \n",
       "96804                0.0                1.0                0.0   \n",
       "96805                0.0                0.0                0.0   \n",
       "\n",
       "       bureau_enquiry_50  onus_attribute_43  onus_attribute_44  \\\n",
       "0                    1.0                NaN                NaN   \n",
       "1                    3.0                0.0                0.0   \n",
       "2                    8.0                NaN                NaN   \n",
       "3                   30.0                NaN                NaN   \n",
       "4                    1.0                NaN                NaN   \n",
       "...                  ...                ...                ...   \n",
       "96801                1.0                NaN                NaN   \n",
       "96802                3.0                0.0                1.0   \n",
       "96803               20.0                NaN                NaN   \n",
       "96804                8.0                NaN                NaN   \n",
       "96805               25.0                NaN                NaN   \n",
       "\n",
       "       onus_attribute_45  onus_attribute_46  onus_attribute_47  \\\n",
       "0                    NaN                NaN                NaN   \n",
       "1                    0.0                0.0                0.0   \n",
       "2                    NaN                NaN                NaN   \n",
       "3                    NaN                NaN                NaN   \n",
       "4                    NaN                NaN                NaN   \n",
       "...                  ...                ...                ...   \n",
       "96801                NaN                NaN                NaN   \n",
       "96802                0.0                1.0                0.0   \n",
       "96803                NaN                NaN                NaN   \n",
       "96804                NaN                NaN                NaN   \n",
       "96805                NaN                NaN                NaN   \n",
       "\n",
       "       onus_attribute_48  \n",
       "0                    NaN  \n",
       "1                    0.0  \n",
       "2                    NaN  \n",
       "3                    NaN  \n",
       "4                    NaN  \n",
       "...                  ...  \n",
       "96801                NaN  \n",
       "96802                0.0  \n",
       "96803                NaN  \n",
       "96804                NaN  \n",
       "96805                NaN  \n",
       "\n",
       "[96806 rows x 1216 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def drop_null(threshold, df, val_df):\n",
    "    # Identify columns to retain\n",
    "    valid_cols = df.columns[df.isnull().mean() <= threshold]\n",
    "    \n",
    "    # Log details\n",
    "    dropped_cols = set(df.columns) - set(valid_cols)\n",
    "    \n",
    "    # Subset dataframes\n",
    "    df = df.loc[:, valid_cols]\n",
    "    val_df = val_df.loc[:, valid_cols.drop('bad_flag')]\n",
    "    \n",
    "    return df, val_df\n",
    "\n",
    "df , val_df = drop_null(0.25, df, val_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account_number</th>\n",
       "      <th>bad_flag</th>\n",
       "      <th>bureau_1</th>\n",
       "      <th>bureau_2</th>\n",
       "      <th>bureau_3</th>\n",
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       "      <th>bureau_8</th>\n",
       "      <th>...</th>\n",
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       "      <th>bureau_enquiry_42</th>\n",
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       "      <th>bureau_enquiry_44</th>\n",
       "      <th>bureau_enquiry_45</th>\n",
       "      <th>bureau_enquiry_46</th>\n",
       "      <th>bureau_enquiry_47</th>\n",
       "      <th>bureau_enquiry_48</th>\n",
       "      <th>bureau_enquiry_49</th>\n",
       "      <th>bureau_enquiry_50</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96801</th>\n",
       "      <td>96802</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96802</th>\n",
       "      <td>96803</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96803</th>\n",
       "      <td>96804</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96804</th>\n",
       "      <td>96805</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96805</th>\n",
       "      <td>96806</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96806 rows × 521 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       account_number  bad_flag  bureau_1  bureau_2  bureau_3  bureau_4  \\\n",
       "0                   1         0       0.0       0.0       0.0       0.0   \n",
       "1                   2         0       0.0       1.0       0.0       0.0   \n",
       "2                   3         0       0.0       2.0       0.0       0.0   \n",
       "3                   4         0       0.0       4.0       0.0       0.0   \n",
       "4                   5         0       1.0       0.0       4.0       0.0   \n",
       "...               ...       ...       ...       ...       ...       ...   \n",
       "96801           96802         0       0.0       1.0       0.0       0.0   \n",
       "96802           96803         0       0.0       0.0       0.0       0.0   \n",
       "96803           96804         0       0.0       0.0       1.0       0.0   \n",
       "96804           96805         0       2.0       2.0       0.0       0.0   \n",
       "96805           96806         0       0.0       0.0       0.0       0.0   \n",
       "\n",
       "       bureau_5  bureau_6  bureau_7  bureau_8  ...  bureau_enquiry_41  \\\n",
       "0           0.0       0.0      17.0       0.0  ...                3.0   \n",
       "1           0.0      22.0       6.0      22.0  ...                6.0   \n",
       "2           0.0       0.0       2.0       0.0  ...               11.0   \n",
       "3           0.0       7.0       8.0       7.0  ...               38.0   \n",
       "4           0.0       0.0      18.0       5.0  ...                1.0   \n",
       "...         ...       ...       ...       ...  ...                ...   \n",
       "96801       4.0       1.0       3.0       5.0  ...                5.0   \n",
       "96802       0.0       0.0       1.0       0.0  ...                5.0   \n",
       "96803       0.0       0.0      12.0       1.0  ...               37.0   \n",
       "96804       2.0       0.0       6.0       4.0  ...               16.0   \n",
       "96805       0.0       0.0       9.0       0.0  ...               31.0   \n",
       "\n",
       "       bureau_enquiry_42  bureau_enquiry_43  bureau_enquiry_44  \\\n",
       "0                    0.0                3.0                0.0   \n",
       "1                    2.0                4.0                0.0   \n",
       "2                    0.0               11.0                0.0   \n",
       "3                    0.0               38.0                0.0   \n",
       "4                    0.0                1.0                0.0   \n",
       "...                  ...                ...                ...   \n",
       "96801                0.0                5.0                0.0   \n",
       "96802                0.0                5.0                0.0   \n",
       "96803                0.0               37.0                0.0   \n",
       "96804                3.0               13.0                2.0   \n",
       "96805                0.0               31.0                0.0   \n",
       "\n",
       "       bureau_enquiry_45  bureau_enquiry_46  bureau_enquiry_47  \\\n",
       "0                    0.0                0.0                0.0   \n",
       "1                    1.0                0.0                0.0   \n",
       "2                    2.0                0.0                0.0   \n",
       "3                    6.0                0.0                0.0   \n",
       "4                    0.0                0.0                0.0   \n",
       "...                  ...                ...                ...   \n",
       "96801                1.0                0.0                0.0   \n",
       "96802                1.0                0.0                0.0   \n",
       "96803                9.0                0.0                0.0   \n",
       "96804                4.0                0.0                0.0   \n",
       "96805                0.0                0.0                0.0   \n",
       "\n",
       "       bureau_enquiry_48  bureau_enquiry_49  bureau_enquiry_50  \n",
       "0                    0.0                0.0                1.0  \n",
       "1                    0.0                2.0                3.0  \n",
       "2                    0.0                0.0                8.0  \n",
       "3                    0.0                0.0               30.0  \n",
       "4                    0.0                0.0                1.0  \n",
       "...                  ...                ...                ...  \n",
       "96801                0.0                0.0                1.0  \n",
       "96802                0.0                0.0                3.0  \n",
       "96803                0.0                0.0               20.0  \n",
       "96804                1.0                0.0                8.0  \n",
       "96805                0.0                0.0               25.0  \n",
       "\n",
       "[96806 rows x 521 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_constant_features(df, val_df,threshold):\n",
    "    \"\"\"Removing constant and quasi-constant features\"\"\"\n",
    "    from sklearn.feature_selection import VarianceThreshold\n",
    "    selector = VarianceThreshold(threshold=threshold)\n",
    "    selector.fit_transform(df)\n",
    "    selected_features = df.columns[selector.get_support()]\n",
    "    df = pd.DataFrame(df, columns=selected_features)\n",
    "    val_df = pd.DataFrame(val_df, columns=selected_features.drop('bad_flag'))\n",
    "    return df , val_df\n",
    "\n",
    "df , val_df = remove_constant_features(df, val_df, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account_number</th>\n",
       "      <th>bureau_1</th>\n",
       "      <th>bureau_2</th>\n",
       "      <th>bureau_3</th>\n",
       "      <th>bureau_5</th>\n",
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       "      <th>bureau_10</th>\n",
       "      <th>...</th>\n",
       "      <th>bureau_enquiry_40</th>\n",
       "      <th>bureau_enquiry_41</th>\n",
       "      <th>bureau_enquiry_42</th>\n",
       "      <th>bureau_enquiry_43</th>\n",
       "      <th>bureau_enquiry_44</th>\n",
       "      <th>bureau_enquiry_45</th>\n",
       "      <th>bureau_enquiry_46</th>\n",
       "      <th>bureau_enquiry_48</th>\n",
       "      <th>bureau_enquiry_49</th>\n",
       "      <th>bureau_enquiry_50</th>\n",
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       "  </thead>\n",
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       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>51.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100003</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>18.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41787</th>\n",
       "      <td>141788</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41788</th>\n",
       "      <td>141789</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41790</th>\n",
       "      <td>141791</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41791</th>\n",
       "      <td>141792</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>41792 rows × 472 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       account_number  bureau_1  bureau_2  bureau_3  bureau_5  bureau_6  \\\n",
       "0              100001       0.0       1.0       0.0       0.0       0.0   \n",
       "1              100002       0.0       0.0       1.0       0.0       0.0   \n",
       "2              100003       3.0       2.0       0.0       0.0       0.0   \n",
       "3              100004       0.0       0.0       0.0       0.0       1.0   \n",
       "4              100005       0.0       3.0       0.0       0.0       0.0   \n",
       "...               ...       ...       ...       ...       ...       ...   \n",
       "41787          141788       2.0       1.0       0.0       1.0       0.0   \n",
       "41788          141789       0.0       1.0       0.0       1.0       0.0   \n",
       "41789          141790       0.0       1.0       0.0       1.0       0.0   \n",
       "41790          141791       0.0       1.0       0.0       0.0       0.0   \n",
       "41791          141792       0.0       0.0       0.0       2.0       0.0   \n",
       "\n",
       "       bureau_7  bureau_8  bureau_9  bureau_10  ...  bureau_enquiry_40  \\\n",
       "0           4.0       1.0      11.0        0.0  ...               23.0   \n",
       "1           2.0       4.0       3.0        4.0  ...                0.0   \n",
       "2          16.0       3.0      31.0        3.0  ...                9.0   \n",
       "3           2.0       1.0       2.0        1.0  ...                1.0   \n",
       "4           4.0       0.0      21.0        0.0  ...               18.0   \n",
       "...         ...       ...       ...        ...  ...                ...   \n",
       "41787       3.0       3.0       5.0        3.0  ...                7.0   \n",
       "41788       2.0       2.0       2.0        1.0  ...               19.0   \n",
       "41789       7.0       1.0      20.0        1.0  ...                5.0   \n",
       "41790       2.0       0.0       6.0        0.0  ...                1.0   \n",
       "41791      10.0       2.0      15.0        1.0  ...                9.0   \n",
       "\n",
       "       bureau_enquiry_41  bureau_enquiry_42  bureau_enquiry_43  \\\n",
       "0                   51.0                1.0               50.0   \n",
       "1                    0.0                0.0                0.0   \n",
       "2                   16.0                2.0               14.0   \n",
       "3                    1.0                0.0                1.0   \n",
       "4                   29.0                0.0               29.0   \n",
       "...                  ...                ...                ...   \n",
       "41787               12.0                2.0               10.0   \n",
       "41788               24.0                1.0               23.0   \n",
       "41789                5.0                0.0                5.0   \n",
       "41790                6.0                0.0                6.0   \n",
       "41791               19.0                0.0               19.0   \n",
       "\n",
       "       bureau_enquiry_44  bureau_enquiry_45  bureau_enquiry_46  \\\n",
       "0                    0.0               10.0                0.0   \n",
       "1                    0.0                0.0                0.0   \n",
       "2                    0.0                1.0                0.0   \n",
       "3                    0.0                0.0                0.0   \n",
       "4                    0.0                2.0                0.0   \n",
       "...                  ...                ...                ...   \n",
       "41787                2.0                0.0                0.0   \n",
       "41788                0.0                2.0                0.0   \n",
       "41789                0.0                0.0                0.0   \n",
       "41790                0.0                1.0                0.0   \n",
       "41791                0.0                4.0                0.0   \n",
       "\n",
       "       bureau_enquiry_48  bureau_enquiry_49  bureau_enquiry_50  \n",
       "0                    0.0                0.0               32.0  \n",
       "1                    0.0                0.0                0.0  \n",
       "2                    2.0                0.0               12.0  \n",
       "3                    0.0                0.0                1.0  \n",
       "4                    0.0                0.0               18.0  \n",
       "...                  ...                ...                ...  \n",
       "41787                0.0                0.0               10.0  \n",
       "41788                1.0                0.0               19.0  \n",
       "41789                0.0                0.0                5.0  \n",
       "41790                0.0                0.0                2.0  \n",
       "41791                0.0                0.0               10.0  \n",
       "\n",
       "[41792 rows x 472 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer, KNNImputer\n",
    "\n",
    "def impute_data(df, val_df, technique, knn_neighbors=5):\n",
    "    \n",
    "    # Separate features and target from the training dataset\n",
    "    X = df.drop(columns=['bad_flag'])\n",
    "    y = df['bad_flag']\n",
    "    \n",
    "    # Select the appropriate imputation technique\n",
    "    if technique == 'mean':\n",
    "    \n",
    "        imputer = SimpleImputer(strategy='mean')\n",
    "    elif technique == 'median':\n",
    "        imputer = SimpleImputer(strategy='median')\n",
    "    elif technique == 'mode':\n",
    "        imputer = SimpleImputer(strategy='most_frequent')\n",
    "    elif technique == 'knn':\n",
    "        imputer = KNNImputer(n_neighbors=knn_neighbors)\n",
    "    else:\n",
    "        raise ValueError('Invalid imputation technique')\n",
    "    \n",
    "    # Fit and transform the training features\n",
    "    X_imputed = imputer.fit_transform(X)\n",
    "    X_imputed_df = pd.DataFrame(X_imputed, columns=X.columns)\n",
    "    \n",
    "    # Combine imputed features with the target column\n",
    "    df_imputed = pd.concat([X_imputed_df, y], axis=1)\n",
    "    \n",
    "    # Ensure 'val_df' contains only the same features as 'X'\n",
    "    val_df = val_df[X.columns]  # Exclude any column not in training features\n",
    "    val_df_imputed = imputer.transform(val_df)\n",
    "    val_df_imputed_df = pd.DataFrame(val_df_imputed, columns=X.columns, index=val_df.index)\n",
    "    \n",
    "    return df_imputed, val_df_imputed_df\n",
    "\n",
    "df, val_df = impute_data(df, val_df, 'mode')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account_number</th>\n",
       "      <th>bureau_1</th>\n",
       "      <th>bureau_2</th>\n",
       "      <th>bureau_3</th>\n",
       "      <th>bureau_5</th>\n",
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       "      <th>bureau_9</th>\n",
       "      <th>bureau_10</th>\n",
       "      <th>...</th>\n",
       "      <th>bureau_enquiry_41</th>\n",
       "      <th>bureau_enquiry_42</th>\n",
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       "      <th>bureau_enquiry_48</th>\n",
       "      <th>bureau_enquiry_49</th>\n",
       "      <th>bureau_enquiry_50</th>\n",
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       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
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       "      <td>17.0</td>\n",
       "      <td>7.0</td>\n",
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       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>96802</th>\n",
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       "    <tr>\n",
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       "      <td>1.0</td>\n",
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       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>96804</th>\n",
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       "      <td>20.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>3.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>96805</th>\n",
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       "      <td>...</td>\n",
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       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96806 rows × 473 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       account_number  bureau_1  bureau_2  bureau_3  bureau_5  bureau_6  \\\n",
       "0                 1.0       0.0       0.0       0.0       0.0       0.0   \n",
       "1                 2.0       0.0       1.0       0.0       0.0      22.0   \n",
       "2                 3.0       0.0       2.0       0.0       0.0       0.0   \n",
       "3                 4.0       0.0       4.0       0.0       0.0       7.0   \n",
       "4                 5.0       1.0       0.0       4.0       0.0       0.0   \n",
       "...               ...       ...       ...       ...       ...       ...   \n",
       "96801         96802.0       0.0       1.0       0.0       4.0       1.0   \n",
       "96802         96803.0       0.0       0.0       0.0       0.0       0.0   \n",
       "96803         96804.0       0.0       0.0       1.0       0.0       0.0   \n",
       "96804         96805.0       2.0       2.0       0.0       2.0       0.0   \n",
       "96805         96806.0       0.0       0.0       0.0       0.0       0.0   \n",
       "\n",
       "       bureau_7  bureau_8  bureau_9  bureau_10  ...  bureau_enquiry_41  \\\n",
       "0          17.0       0.0      22.0        0.0  ...                3.0   \n",
       "1           6.0      22.0      10.0       22.0  ...                6.0   \n",
       "2           2.0       0.0       4.0        0.0  ...               11.0   \n",
       "3           8.0       7.0      17.0        7.0  ...               38.0   \n",
       "4          18.0       5.0      18.0        5.0  ...                1.0   \n",
       "...         ...       ...       ...        ...  ...                ...   \n",
       "96801       3.0       5.0      29.0        5.0  ...                5.0   \n",
       "96802       1.0       0.0       2.0        0.0  ...                5.0   \n",
       "96803      12.0       1.0      16.0        1.0  ...               37.0   \n",
       "96804       6.0       4.0      20.0        4.0  ...               16.0   \n",
       "96805       9.0       0.0      37.0        0.0  ...               31.0   \n",
       "\n",
       "       bureau_enquiry_42  bureau_enquiry_43  bureau_enquiry_44  \\\n",
       "0                    0.0                3.0                0.0   \n",
       "1                    2.0                4.0                0.0   \n",
       "2                    0.0               11.0                0.0   \n",
       "3                    0.0               38.0                0.0   \n",
       "4                    0.0                1.0                0.0   \n",
       "...                  ...                ...                ...   \n",
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       "96804                3.0               13.0                2.0   \n",
       "96805                0.0               31.0                0.0   \n",
       "\n",
       "       bureau_enquiry_45  bureau_enquiry_46  bureau_enquiry_48  \\\n",
       "0                    0.0                0.0                0.0   \n",
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       "3                    6.0                0.0                0.0   \n",
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       "\n",
       "       bureau_enquiry_49  bureau_enquiry_50  bad_flag  \n",
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       "\n",
       "[96806 rows x 473 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\sarth\\anaconda3\\envs\\convolve\\lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\sarth\\anaconda3\\envs\\convolve\\lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\sarth\\anaconda3\\envs\\convolve\\lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\sarth\\anaconda3\\envs\\convolve\\lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\sarth\\anaconda3\\envs\\convolve\\lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\sarth\\anaconda3\\envs\\convolve\\lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Users\\sarth\\anaconda3\\envs\\convolve\\lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import RobustScaler, PowerTransformer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.utils import resample\n",
    "from scipy.stats import chi2_contingency\n",
    "import statsmodels.api as sm\n",
    "\n",
    "def downsample_data(data, target_column):\n",
    "    \"\"\"Downsample majority class to balance classes.\"\"\"\n",
    "    majority = data[data[target_column] == 0]\n",
    "    minority = data[data[target_column] == 1]\n",
    "    majority_downsampled = resample(majority, replace=False, n_samples=len(minority), random_state=42)\n",
    "    return pd.concat([majority_downsampled, minority])\n",
    "\n",
    "def select_significant_columns(data, target_column, alpha=0.05):\n",
    "    \"\"\"Select significant categorical features using chi-squared test.\"\"\"\n",
    "    significant_columns = []\n",
    "    for col in data.select_dtypes(include=['int']).columns:\n",
    "        if col == target_column:\n",
    "            continue\n",
    "        \n",
    "        contingency_table = pd.crosstab(data[col], data[target_column])\n",
    "        chi2, p, _, _ = chi2_contingency(contingency_table)\n",
    "        if p < alpha:\n",
    "            significant_columns.append(col)\n",
    "    return significant_columns\n",
    "\n",
    "def logistic_regression_filter(data, target_column, alpha=0.05):\n",
    "    \"\"\"Select significant continuous features using logistic regression.\"\"\"\n",
    "    results = []\n",
    "    for col in data.select_dtypes(include=['float']).columns:\n",
    "        if col == target_column:\n",
    "            continue\n",
    "        X = data[[col]]\n",
    "        y = data[target_column]\n",
    "        X = sm.add_constant(X)\n",
    "        try:\n",
    "            model = sm.Logit(y, X).fit(disp=False)\n",
    "            if model.pvalues[col] < alpha:\n",
    "                results.append(col)\n",
    "        except:\n",
    "            continue\n",
    "    return results\n",
    "\n",
    "def scale_and_transform(data):\n",
    "    \"\"\"Apply robust scaling and power transformation to continuous features.\"\"\"\n",
    "    robust_scaler = RobustScaler()\n",
    "    gaussian_transformer = PowerTransformer(method='yeo-johnson')\n",
    "    filled_data = data.fillna(data.median())\n",
    "    robust_scaled = pd.DataFrame(robust_scaler.fit_transform(filled_data), columns=data.columns)\n",
    "    transformed_data = pd.DataFrame(gaussian_transformer.fit_transform(robust_scaled), columns=data.columns)\n",
    "    return transformed_data\n",
    "\n",
    "target_column = 'bad_flag'\n",
    "\n",
    "# Process `Onus` subset\n",
    "Onus = df.loc[:, df.columns.str.contains('onus') | (df.columns == target_column)]\n",
    "Onus = downsample_data(Onus, target_column)\n",
    "Onus_float_f = logistic_regression_filter(Onus, target_column)\n",
    "Onus_int_f = select_significant_columns(Onus, target_column)\n",
    "\n",
    "# Process `bureau` subset\n",
    "bureau = df.loc[:, df.columns.str.contains(r'\\bbureau_(?!.*enquiry)', regex=True) | (df.columns == target_column)]\n",
    "bureau = downsample_data(bureau, target_column)\n",
    "Burr_float_f = logistic_regression_filter(bureau, target_column)\n",
    "\n",
    "# Process `bureau_enquiry` subset\n",
    "bureau_eqr = df.loc[:, df.columns.str.contains('bureau_enquiry') | (df.columns == target_column)]\n",
    "bureau_eqr = downsample_data(bureau_eqr, target_column)\n",
    "burr_Eqr_float_f = logistic_regression_filter(bureau_eqr, target_column)\n",
    "\n",
    "# Combine selected columns\n",
    "selected_columns = Onus_float_f + Onus_int_f + Burr_float_f + burr_Eqr_float_f + ['account_number', 'bad_flag']\n",
    "gf = df[selected_columns]\n",
    "\n",
    "# Separate continuous and categorical columns\n",
    "categorical_columns = Onus_int_f + ['account_number', 'bad_flag']\n",
    "continuous_columns = list(set(selected_columns) - set(categorical_columns))\n",
    "\n",
    "gf_continuous = gf[continuous_columns]\n",
    "gf_continuous = scale_and_transform(gf_continuous)\n",
    "\n",
    "gf_categorical = gf[categorical_columns]\n",
    "gf_combined = pd.concat([gf_continuous, gf_categorical], axis=1)\n",
    "\n",
    "# Split into train and test sets\n",
    "features = gf_combined.drop(columns=['bad_flag'])\n",
    "target = gf_combined['bad_flag']\n",
    "train_features, test_features, train_target, test_target = train_test_split(\n",
    "    features, target, test_size=0.2, random_state=42\n",
    ")\n",
    "train_df = pd.concat([train_features, train_target], axis=1).reset_index(drop=True)\n",
    "test_df = pd.concat([test_features, test_target], axis=1).reset_index(drop=True)\n",
    "\n",
    "# Process validation data\n",
    "val_df_continuous = val_df[continuous_columns]\n",
    "val_df_continuous = scale_and_transform(val_df_continuous)\n",
    "val_df_categorical = val_df[list(set(categorical_columns) - {'bad_flag'})]\n",
    "val_df = pd.concat([val_df_continuous, val_df_categorical], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/processed/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>onus_attribute_2</th>\n",
       "      <th>onus_attribute_26</th>\n",
       "      <th>onus_attribute_17</th>\n",
       "      <th>onus_attribute_23</th>\n",
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       "      <td>1.258045</td>\n",
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       "      <td>-0.711215</td>\n",
       "      <td>0.505248</td>\n",
       "      <td>1.396848</td>\n",
       "      <td>1.315432</td>\n",
       "      <td>-0.113909</td>\n",
       "      <td>-0.702699</td>\n",
       "      <td>0.426667</td>\n",
       "      <td>861.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77443</th>\n",
       "      <td>1.714358</td>\n",
       "      <td>2.138123</td>\n",
       "      <td>1.163492</td>\n",
       "      <td>1.295573</td>\n",
       "      <td>1.294618</td>\n",
       "      <td>-0.334555</td>\n",
       "      <td>-0.238679</td>\n",
       "      <td>-0.349896</td>\n",
       "      <td>1.692559</td>\n",
       "      <td>-0.419383</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.312811</td>\n",
       "      <td>0.212549</td>\n",
       "      <td>1.686870</td>\n",
       "      <td>-0.241395</td>\n",
       "      <td>-0.229071</td>\n",
       "      <td>-0.113909</td>\n",
       "      <td>1.121273</td>\n",
       "      <td>0.426667</td>\n",
       "      <td>15796.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77444 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       onus_attribute_2  onus_attribute_26  onus_attribute_17  \\\n",
       "0             -0.157467          -0.467700          -0.640470   \n",
       "1              0.656522          -0.467700           0.119309   \n",
       "2             -0.436695           2.138123           1.466881   \n",
       "3             -0.815233          -0.467700          -0.920311   \n",
       "4              1.754213           2.138123           2.083792   \n",
       "...                 ...                ...                ...   \n",
       "77439         -0.527477           2.138123          -0.752289   \n",
       "77440         -0.815985          -0.467700          -0.034543   \n",
       "77441         -0.384968          -0.467700          -0.223922   \n",
       "77442         -0.815917          -0.467700          -0.928204   \n",
       "77443          1.714358           2.138123           1.163492   \n",
       "\n",
       "       onus_attribute_23  onus_attribute_20  bureau_439  bureau_450  \\\n",
       "0              -0.811798          -0.811594    0.069370   -0.010905   \n",
       "1               0.263806           0.263458    0.768328    0.759409   \n",
       "2               1.258045           1.257115    1.485753    1.345870   \n",
       "3              -0.945217          -0.944947   -0.982608   -1.059947   \n",
       "4               1.384953           1.383935   -0.806127   -0.849073   \n",
       "...                  ...                ...         ...         ...   \n",
       "77439          -0.816734          -0.816527    0.018441    1.910927   \n",
       "77440           0.089129           0.088872   -0.973885   -1.008071   \n",
       "77441          -0.350331          -0.350360   -0.938543   -0.963230   \n",
       "77442           0.267197           0.266847   -0.551765   -0.607144   \n",
       "77443           1.295573           1.294618   -0.334555   -0.238679   \n",
       "\n",
       "       onus_attribute_29  onus_attribute_32  onus_attribute_35  ...  \\\n",
       "0              -0.349896          -0.560162          -0.419383  ...   \n",
       "1              -0.349896          -0.560162          -0.419383  ...   \n",
       "2              -0.349896           1.866639          -0.419383  ...   \n",
       "3              -0.349896          -0.560162          -0.419383  ...   \n",
       "4              -0.349896           1.866639          -0.419383  ...   \n",
       "...                  ...                ...                ...  ...   \n",
       "77439          -0.349896           1.692559          -0.419383  ...   \n",
       "77440          -0.349896          -0.560162          -0.419383  ...   \n",
       "77441          -0.349896          -0.560162          -0.419383  ...   \n",
       "77442          -0.349896          -0.560162          -0.419383  ...   \n",
       "77443          -0.349896           1.692559          -0.419383  ...   \n",
       "\n",
       "       bureau_426  bureau_429  bureau_enquiry_11  bureau_66  bureau_115  \\\n",
       "0       -1.082091   -0.505972          -1.171275  -0.467739   -1.229734   \n",
       "1        0.229040    0.216754           0.995752   2.507463    1.234801   \n",
       "2       -1.140856   -1.013304           0.782588   1.635639    2.197395   \n",
       "3        1.424903    1.950946          -1.171275  -0.807359   -1.650448   \n",
       "4        0.297530    0.652953           0.124461   0.076181    0.232065   \n",
       "...           ...         ...                ...        ...         ...   \n",
       "77439   -0.596588   -0.120885          -0.422635  -0.802353    1.053356   \n",
       "77440   -1.300047   -1.533982           0.124461  -0.802353   -0.853227   \n",
       "77441    1.235225    0.670062          -0.422635  -0.726846    0.020659   \n",
       "77442    0.611131   -0.711215           0.505248   1.396848    1.315432   \n",
       "77443   -0.312811    0.212549           1.686870  -0.241395   -0.229071   \n",
       "\n",
       "       onus_attribute_31  bureau_enquiry_25  bureau_108  account_number.1  \\\n",
       "0              -0.113909          -0.702699   -0.921248           47642.0   \n",
       "1              -0.113909           1.121273    1.295786           95032.0   \n",
       "2              -0.113909           1.483524   -0.921248           40510.0   \n",
       "3              -0.113909          -0.702699   -0.921248           51692.0   \n",
       "4              -0.113909          -0.702699   -0.921248           27459.0   \n",
       "...                  ...                ...         ...               ...   \n",
       "77439          -0.113909          -0.702699    0.994812            6266.0   \n",
       "77440          -0.113909          -0.702699   -0.921248           54887.0   \n",
       "77441          -0.113909          -0.702699   -0.921248           76821.0   \n",
       "77442          -0.113909          -0.702699    0.426667             861.0   \n",
       "77443          -0.113909           1.121273    0.426667           15796.0   \n",
       "\n",
       "       bad_flag  \n",
       "0             0  \n",
       "1             0  \n",
       "2             0  \n",
       "3             0  \n",
       "4             0  \n",
       "...         ...  \n",
       "77439         0  \n",
       "77440         0  \n",
       "77441         0  \n",
       "77442         0  \n",
       "77443         0  \n",
       "\n",
       "[77444 rows x 42 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "null_percentage = train_df.isnull().mean() * 100\n",
    "\n",
    "# Define bins for null percentage ranges\n",
    "bins = [0, 10, 20,30,40,50,60,70,80,90,100]\n",
    "labels = ['0-10%', '10-20%', '20-30%', '30-40%', '40-50%', '50-60%', '60-70%', '70-80%', '80-90%', '90-100%']\n",
    "\n",
    "# Bin the features based on null percentage\n",
    "categories = pd.cut(null_percentage, bins=bins, labels=labels, include_lowest=True)\n",
    "\n",
    "# Count the number of features in each bin\n",
    "feature_counts = categories.value_counts().sort_index()\n",
    "\n",
    "# Plot the graph\n",
    "plt.figure(figsize=(8, 5))\n",
    "feature_counts.plot(kind='bar', color='skyblue', edgecolor='black')\n",
    "plt.title('Distribution of Features by Null Percentage')\n",
    "plt.xlabel('Null Percentage Range')\n",
    "plt.ylabel('Number of Features')\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the percentage of null values for each feature\n",
    "null_percentage = train_df.isnull().mean() * 100\n",
    "\n",
    "# Define thresholds for null percentages\n",
    "thresholds = np.arange(0, 105, 5)\n",
    "\n",
    "# Count features exceeding each threshold\n",
    "feature_counts = [sum(null_percentage > t) for t in thresholds]\n",
    "\n",
    "# Plot the graph\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(thresholds, feature_counts, marker='o', linestyle='-', color='skyblue', label='Features Exceeding Threshold')\n",
    "plt.title('Number of Features with Null Values Greater than Threshold')\n",
    "plt.xlabel('Threshold Percentage')\n",
    "plt.ylabel('Number of Features')\n",
    "plt.xticks(thresholds)\n",
    "plt.grid(axis='both', linestyle='--', alpha=0.7)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     695\n",
       "False    521\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NULL_PERCENTAGE = 0.25\n",
    "(train_df.isnull().mean() > NULL_PERCENTAGE).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 695 features with more than 25 percent null values while only 25 features with more than 30 percent null values. SO most of the features have about null values between 25 and 20 percent\n",
    "\n",
    "#### Only 31 features with no null value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = train_df.loc[:, train_df.isnull().mean()  <= NULL_PERCENTAGE]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bureau_450           0.090346\n",
       "bureau_145           0.088166\n",
       "bureau_439           0.071979\n",
       "bureau_443           0.061174\n",
       "bureau_452           0.051371\n",
       "                       ...   \n",
       "onus_attribute_37    0.000000\n",
       "onus_attribute_38    0.000000\n",
       "onus_attribute_39    0.000000\n",
       "onus_attribute_40    0.000000\n",
       "account_number       0.000000\n",
       "Length: 521, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().mean().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Maximum 9 percent of null value is present in a column which can be imputed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of binary features: 224\n"
     ]
    }
   ],
   "source": [
    "cnt =0\n",
    "binary_features = []\n",
    "for col in df.columns:\n",
    "    if df[col].dropna().nunique() == 2:\n",
    "        cnt+=1\n",
    "        binary_features.append(col)\n",
    "\n",
    "print(f'Number of binary features: {cnt}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of constant features: 48\n"
     ]
    }
   ],
   "source": [
    "cnt =0\n",
    "for col in df.columns:\n",
    "    if df[col].dropna().nunique() == 1:\n",
    "        cnt+=1\n",
    "\n",
    "print(f'Number of constant features: {cnt}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There are 48 constant features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fPnz22Wdcf/31rvkPP/wwCQkJfPzxxzz22GMAdOnShalTp5bbY+CUU06hU6dOrFq1ikGDBrFr1y5+/vlntyvDW7du5bHHHmPr1q2EhYXRpk0b11Xh4sexory8/vrrLFy4kPT0dGJjY2nfvj316tUrkRPn1XQns9lx3aGwsJDMzEzsdrvrA5fSZGRksGfPnjIzXvSKfXU4u8cXbWPxY1K0nVlZWRQWFvLqq6/y6quvltheaGgoALfeeisNGjRgxYoVPPfcczz77LOceuqpPPTQQ263cJR2nJyv7/zZjIuLc1smKCiIqKioUn82netER0e7TS++DRGRQKQCW0QkQDVs2JBp06Yxbtw45s+fX+oyNpvN7f9FByaqruzsbOx2u1vxl5aW5vqjOSIighYtWjBjxoxS109KSqr0azkL8bS0tBL3tR45csR1Vc+bIiIiqF+/Pm+99Vap85s3b87evXsZN24cF1xwAQsXLnRdaX733Xcr1dW1Ku9PZGQkAG+++aarkC+qSZMmgGME72nTpvHoo4+yfft2PvvsM1599VUaNmzoKo5L26fOnTvz6aefEhwcTHp6OpdffrlrfkhICGPGjGHMmDEcPHiQdevWMW/ePCZPnsynn35abruHDBnC448/zrFjx/jwww+Ji4ujT58+ABw/fpxbb72Vtm3bsnr1alq3bo3ZbObrr7/m888/r/CYFLVq1SqeffZZJk+ezNChQ115nDBhQpnPkC9NeHg4JpPJbVAwgPz8fDZt2kTHjh2JiIige/fu3HfffaVuw1eP0mvQoAEmk4mbbrqp1A9inAWz2Wzmhhtu4IYbbuDo0aN8/fXXLFiwgLvuuqvSV+CdP5tHjhxx+5kuKCgo9R55wDXt6NGjbo/Sc354JCISyNRFXEQkgF144YVcdtllvPLKKyUKgfDwcFJTU92m/fe///Xaa+fn57vuwwTIycnhq6++4uyzzwYc93OmpKQQExNDhw4dXP82bdrEa6+9VuKRTeXp1KkTISEhrFq1ym365s2bOXjwYJlXmauje/funDhxArvd7tZ+52PSrFYrv/76K3l5edx+++1u3bidxbXzKp/zqmNRVX1/unXrBkB6erpbuzIyMpg9ezYZGRn8+OOPnHvuufzyyy+YTCZOO+007r77bk499dQSr1nc5Zdfzvr161m9ejWdO3emRYsWgGO09Isuusg1aniTJk244YYbGDhwYIXbBMcV9+DgYL788ks+++wzrrzySteAcjt37iQjI4Mbb7yRU045xXW81q9fDziuKFfWli1biIiIYPTo0a7iOicnhy1btni0nQYNGnDaaafx5Zdfuk3/z3/+w+jRo0lNTaV79+7s2rWLli1bur0XH3/8McuWLfMo494UHh7O6aefzs6dO93adcopp/DSSy+5um1fe+21PPnkk4DjCvjgwYO54YYbyM7O5vjx45V6re7duwOU+Nl03mPvvL2gqC5duhAWFsZnn33mNt15a4WISCDTFWwRkQA3ZcoUvv32W9LS0tym9+3bl4ULF7JgwQI6d+7MV199VeqjtqoqODiYhx56iEmTJhEeHs4rr7xCbm4uY8eOBWDw4MG88847jBo1ijvuuIPExES++eYbXn31VYYPH16ie3l5GjVqxOjRo3nppZcIDg7mggsuYP/+/cyZM4c2bdqUOYJ1dfTu3Ztu3boxduxYxo4dS+vWrfnll1948cUXOf/884mOjqZ9+/YEBQUxffp0br75ZvLz81m5ciVfffUV8M8VaedV59WrV9OpUyeSk5Pp06cPn3zyCR07dqRly5b861//Ys+ePRW269RTT+Xyyy9nypQpHDhwgDPOOINdu3Yxa9YskpKSaNGiBVarlbCwMO677z7uuusuYmNj+eabb9i2bRs33nhjudsfOHAgzzzzDJ988gkPP/ywa3pYWBjt27d3vQdt27Zl165d/Otf/+Kiiy6qsN3h4eFcfPHFvPbaa+zevds1sBZAy5YtCQ8PZ8GCBQQFBREUFMTnn3/O8uXLAUdX68rq2LEj7733Hs8++yx9+/bl8OHDLFq0iLS0NNfV1soaP348Y8aMYeLEiQwePJhjx47xwgsv0LdvX0477TSio6P56KOPuOmmm7j55puJiopizZo1LF261DUomK84B8GbPHkyl19+OTabjcWLF/Pzzz+7BiPr1q0bixcvJjY2li5dunDo0CFef/11unfvTnR0dKV6VLRp04arrrqKl156idzcXM4++2y2bdvmepRa0ZHjnRo0aMDYsWOZPXs29erVo0ePHnz99dcqsEWkVlCBLSIS4Bo1asS0adO488473abffvvtHDt2jMWLF1NQUECfPn146qmnXH9cV1fDhg259957mTFjBkeOHKFTp0688847ri6f9evX59133+WFF15g+vTpZGdn07RpUyZPnszNN9/s8es5C8V33nmHZcuW0ahRIy6++GImTpzolftcizObzbzyyivMmTOHhQsXcvToUeLj47npppsYN24c4OhS/cILL/DSSy8xZswYGjZsSOfOnXn77bcZMWIEmzdvpm3btgwYMICPPvqIBx54gKFDhzJt2jQefPBBrFYr06dPJygoiEsvvZTJkyfzyCOPVNi2Z555hoULF/L++++TmppKTEwMl156KRMnTsRisWCxWFi8eLFrNPisrCxatGjB448/XuGHEY0aNaJ37958/fXXrpHRnR5//HFmz57N4sWLOXLkCDExMQwdOpQJEyZU6pgOHTqUf/3rX3Tr1o2WLVu6pkdERDBv3jyef/55JkyY4Lp6/M4773DbbbexefPmSj/X/KqrrmL//v2sWLGCJUuWEB8fT+/evbn++uuZMmUKf/31l2u0+Yo4P6R68cUXGTduHFFRUVxyySWu/Y2Pj+f999/nhRdeYNq0aeTl5dGiRQu3Efh95fzzz2fRokW89NJLrsfutW/fntdff901gOGECRMICQlhxYoVvPzyy0RERNCvXz8mT57s0Ws99dRTNG/enBUrVrBo0SIaN27MiBEjGDduXKm9N8Bxfqpfvz5vvvkmb775Jl26dOH+++9n2rRp1dxzERHfMtm9MQKLiIiIiIiISB2ne7BFREREREREvEAFtoiIiIiIiIgXqMAWERERERER8QIV2CIiIiIiIiJeoAJbRERERERExAtUYIuIiIiIiIh4gZ6DXcyPP/6I3W4nODjY100RERERERERHysoKMBkMtGlS5cKl9UV7GLsdjuB8GjwwsJCXzdBajllTIykfImRlC8xmjImRlK+/I8nNaKuYBfjvHLdoUMHH7ekbHl5eezbt4/k5GRCQ0N93RyphZQxMZLyJUZSvsRoypgYSfnyT1u3bq30srqCLSIiIiIiIuIFKrADUFBQEImJiQQFqQOCGEMZEyMpX2Ik5UuMpoyJkZSvwKd3LgBZLBYaNGjg62ZILaaMiZGULzGS8iVGU8bESMpX4NMV7ABktVpJT0/HarX6uilSSyljYiTlS4ykfInRlDExkvIV+FRgByCbzcbRo0ex2Wy+borUUsqYGEn5EiMpX2I0ZUyMpHwFPp8X2EePHuXee++lR48edOnShdGjR/PXX3+55j/44IO0bdvW7V+vXr1c8wsLC5k7dy49e/akU6dO3HzzzezZs8cXuyIiIiIiIiJ1mM8L7DFjxrBv3z5effVVli9fTlhYGDfddBMnT54E4I8//uCOO+7gP//5j+vfhx9+6Fp/3rx5vP/++zz55JN88MEHmEwmbrvtNvLz8320RyIiIiIiIlIX+bTATk9PJykpiSeeeIIOHTrQunVrxo4dy5EjR/jzzz+x2Wz89ddfdOjQgbi4ONe/6OhoAPLz81m8eDF33XUXvXv3pl27dsyaNYtDhw6xdu1aX+6aiIiIiIiI1DE+LbCjoqKYOXMmp5xyCgBpaWksWrSIhIQE2rRpw+7du8nLy6N169alrr99+3ZycnLo0aOHa1pkZCSnn346P/zwQ43sgy+YzWbCw8Mxm33eAUFqKWVMjKR8iZGULzGaMiZGUr4Cn988pmvKlCksXbqUkJAQ5s+fT/369dmxYwcmk4k333yT9evXYzab6d27NxMnTiQiIoLU1FQAEhMT3bbVuHFjUlJSqtWevLw8t/+bzWaCg4MpLCykoKCgxPKhoaGA46q63W53mxcUFITFYsFms5UYEdBkMhESEoLdbi+1W3tISAgmk4mCggIKCwtd06OiojCZTADlbre0fQEIDg7GbDaX2C44Hg8QFBRU4b6Wt12r1VpicAZvHMPytlvRMazqe1PRvnp6DCu7XV8fw6ioKAoLC8nLy6v2MfQ03/DPMfRVvkvbV6Pfm9p2DMvabnBwMLGxsdhsNrdldI7wbLu+PkcU5W/niOjoaIKDgwGdIwLxHBEI+Xb+jrTZbDpHEHjnCH/+O8Jisbj9DVaZ7dbWY+hP+faE3xTYI0eO5JprruG9995j3LhxLFmyhD///BOz2UzTpk1ZsGABe/bs4bnnnmPHjh28+eabrvu0nW+uU2hoKJmZmVVuS2FhIfv27XObFhERQXx8PDabrcQ8gDZt2gBw+PBhcnNz3ebFx8cTERHB8ePHOXLkiNu8+vXr06RJE+x2e6nbbdmyJRaLhbS0NHJycgBcgYiNjSUqKoqTJ0+6PmxwCg0NJTk5GYD9+/eXCFGzZs0ICQkhPT2drKwst3lRUVHExMSQl5fHgQMH3OYFBQXRokULAFJSUkr8sDVt2pR69eqRmZlJenq627zIyEgaN26M1Wotsa8mk8nVU+HQoUMlfqASEhIIDw/n+PHjpKWluc1r0KABiYmJpb5vAK1atcJkMpGWlsaJEyfc5sXFxdGwYUNOnDjBoUOH3OaFhYWRlJQEUOp2mzdvjtls5tixY2RnZ7vNi46OJjo6mtzcXA4ePOg2Lzg4mObNmwNw8ODBEj/ESUlJhIWFkZGRQUZGhtu8hg0bEhcXR0FBQYk2mc1mWrVqBUBqamqJk2hiYiINGjQgOzubo0ePus0LDw8nISHBle+ieSn63hw5csT1c+fUuHFjIiMjycnJ4fDhw27z6tWrR9OmTYHSj2GLFi0ICgri6NGjHD9+3G1eTEwMUVFR5ObmlvjALCQkhGbNmgFw4MCBEifu5ORkQkNDycjIKHEuaNSoEbGxseTn57N//363eRaLhZYtWwKOfBc/OTdp0oT69euTlZXFsWPH3Ob50znCKTY2lkaNGvndOSIsLIxjx46RmZnp+qAQdI5wCoRzRHH+dI6w2+2uY2gymXSOCMBzhL//HVH0WDRu3FjnCALrHAH+/XeE1Wpl7969AG6/I3WOcPDVOcITJnvxo+FjhYWFDBo0iI4dO/L000+TnZ1NZGSka/5PP/3ENddcw9KlS0lNTWX8+PH8/PPPhIWFuZaZMGEC+fn5zJ8/3+PX37p1KwCnnnqq23R/+uQ5Ly+PlJQUkpOTqV+/vj5V0tUpF28dQ2fGEhMTCQ0N1SfPxbarq1PVO0cUFBSwZ88eEhISXMuCzhGebrc2nWe9me+8vDxSU1Np3rw5oaGhOkcE4DnC3/Nd9HdkgwYNdI4gsM4RRbcL/vd3xMmTJ9m7d6/rb7DKbLe2HkN/yvf27dsB6NChQ4ntFufTK9hHjx5l06ZNXHLJJVgsFsCxA61bt+bw4cOYTCa34hr+KXxTU1NdXcMPHz7s+hTK+f927dpVq21FA12U2Wwucx6UvJpelMVice1ncSaTqdztOru6lbat8rYLZe9LadstqqJ9LW9eUFAQQUGlx6s6x7C87VZ0DKv63kDdPIYWi4XQ0FC316mpfFd2u2Dce1PVY+gv54jKbhd8k2/nuqUto3NE5bbr63OEp9utyXwX7c6nc4RDoJ0j/D3fzt+R+lvMXaCcI4ryx3NEaX+DVWa7te0Y+lu+K8und88fPnyYyZMn8/3337umFRQU8Pvvv9O6dWsmT57MLbfc4raO8wpzmzZtaNeuHeHh4Xz33Xeu+VlZWfz+++907dq1ZnZCREREREREBB9fwW7Xrh3nn38+jz32GE8++SSRkZEsWLCArKwsbrrpJv744w/GjBnD/PnzGThwILt27eLxxx/nsssuc/WhHz58ODNmzCA6OpqmTZsyffp0EhIS6N+/vy93TUREREREROoYnxbYJpOJ2bNn88ILLzBx4kSys7Pp2rUr7777Lk2aNKFJkybMmTOHBQsWsGDBAiIiIhg0aBATJ050bWP8+PFYrVYeeeQRcnNz6datG4sWLSr30r+IiIiIiIiIt/ndIGe+5uyCXpkb2H2l+AjPIt6mjImRlC8xkvIlRlPGxEi+yJfNBhs2QEoKJCZCz55Qzi3XXl2/uq9dUzypEf3mMV1SeTqZi9GUMTGS8iVGUr7EaMqYlKf6xaapRgvVlSthwgQo+sSxpCSYMwcGDzZ2/eq+tr/y6SBnUjX5+fkcOHCg1KH2RbxBGRMjKV9iJOVLjKaMSVlWroQWLaBvX7j+esfXFi0c0/1x3ZUrYehQ9wIX4MABx3Qj16/ua/szXcEOQHa7nZMnT5Z4fpuItyhjYiTlS4ykfInRlLHarTpXgocOheKxcBaMy5eXfVXWF+vabI6rx6XF2G4HkwnGjYM2bUrff5sNxo6t2vqVWXfiRLjiCv/sLl4RFdgiIiIiIlLnVbXLcmWK1bIKRqPXHT8eWraEw4fh4EHHv5QU+PnnklePi6+fmgqdOpW9THmqs77dDvv2OT7o6NOnaq/vSyqwRURERESkTqvKleC8PEcRuXp1xcXqvn1w8cWQkOA+LzXV2HUPHIAzzyx7mYpEREBoaMnpeXmQnV219Su7bkpK5drob1Rgi4iIiIiIX6nJkakruhIMMGoUfPSRo6hNSXFcCT561LN9+uILz5b31roREY6r2E2aOI5HkyZw/LjjynxFPv649KvIX33luNe7KutXdt3ExIqX8Ud6TFcxgfCYLpvNRk5ODg0aNMASiDcmiN9TxsRIypcYSfkSoyljxqvJkantdnjvPbjhhqq1NSQEoqLg0KGKlx0zxnFPclF//QXz5xu77rp1JYtcm80xENqBA6V/sGAyOY7brl1l34Nd1fWr+9q+4EmNqAK7mEAosEVERERE/F1VrkKX1VXb+XS0qgz45Vx32TLo0gW2bHH827wZ/vtfSE+v3P5ccw1ccsk/V4GbNHEU14WFvik2q1uoOo8XuK9fmWNd3fWr+9o1TQV2NQRCga1PTsVoypgYSfkSIylfYrSqZqwmuzx7a93qrF+Vq9DOgrGs+4pNJkdRu3Vr6cXmGWc4um6XxWQqvRANCgKrtdzdAUq/Euzkq2LTG0Vy8fcpORlmz676c7Aru351X7smqcCuhkAosPPy8ti3bx/JycmEljbqgEg1KWNiJOVLjKR8idGqkrGa7PLsrXWrs35FV5I/+ADOPfefEa2do1tv2QJr1lTcruoICoLOneGssxz/unaFdu3g1FOr32XZV8VmdQtVX30I443XrikqsKtBBbaIMibGUr7ESMqXlMcbhcSXX+bz22/HaN8+mgsuCPFpl2ej1q3q+lar49h27ep4LJQ/eustGDGi5HRvdVmubrHpab688bpSMRXY1aACW0QZE2MpX2Ik5avmBFqXZ19cza1Ml+eK7q+t6XUrsz5AZCQMG+YY3Mt5BfrwYcf9yJVhNruPap2Y6Hh80+uvV7zu559Dr17u09avh4suqnjdirp5+7rLss5h/smTGlGP6RIRERERjwRal+eqPOPYk/WXLYN+/dy7O6ekwPffV+4Zx0FV+IvcV+s6ZWXBokUlp5d1n3Nxb71VcuRumw3Wrq24q/YFF5T8YOCCCxzzKlq3Z8+y2zR4MFxxha4ES/WowA5AJpOJevXqYXL2WRHxMmVMjKR81X6+vLK5fr2ZHTuiOfVUM/36BcZ9hIG2bnWKVV+sW1AAd95Z/jOOb74Ztm93XFUtrrAQnn++/PWdXYvrmqFDYcCAf0bUbtIEfv0VLryw4nWbNi05zWJxfFgydGjJQt35K2P27NJzWp11i2+nrCvcNUG/I2sBu7j55Zdf7L/88ouvmyEiIlItVqvdvm6d3b5kieOr1Voz669YYbcnJdntjj9vHf+SkhzT/XXdQG23L9a1WkuuV/SfyWS3JyeXnhcj1wW7PTrabn/sMbt9zBi7/cor7fbu3R3rmM3lr+fNf1FRdnv79nb7hRfa7TfeaLdfd13l1lu50m4/dMj938qVvlnXk/XXrSv7vTKZPH+fy8tncnLVs13ZdUXK4kmNqHuwiwmEe7CLvmX6dEuMoIyJkZQv4/nryMG1aTCnurBuXt4/XZ6dXzdtgvfeK327RdWvX7L7sdUKJ04Yu2519O4NrVuXnP733/D11xWv/8YbMHKk+zRfPeO4us9H9vXzlZ1tqO0jU5dGvyP9k+7BruXy8/M1+IEYShkTIylflVeVPxKNvte0vG64d91V+h/jzmm33uoo0op3wy0shClTan5dX762P6977bUQHg7p6SWXqazqFMPVWbdPHzj//H+6Kycmwp49cPXVFa87bVrpXYO/+gr69q14/ebNS07zVZfn6naXru76gwc7zhWlfVBX2QHDqtNV29fdvKtDvyMDn65gFxMIV7A1uqAYTRkTIylflVPTIxZXZn2A6GiYOBFSU90HdDp40LG+1C6hoe6jPBcWwr/+VfF6b78NZ5/tPu2770p/PJI31y1thGhfX82FwH0+sq+fr1wX6Xekf9JjuqpBBbaIMibGUr4qVtkuvFar47E4zkJ33TqYNavi7cfFQVhYyem5uXDkSPXbX57u3R1/oBe1b59jtOWaXteXr+3v6z73nOMqeFTUP7mDwO3yXN0uy4Hc5dkbz/5WkVxz9DvSP3lUIxpyF3gAC4RBznJzc+1//vmnPTc319dNkVpKGRMjKV/lq8xgTsHBdntCQs0O3lT0X+/edvvDD9vtL79st//rX3b7t9/a7R98UPVBkdat8826vnztQFzXacUKx0BVxQexck4rbzApX63rXL86g19p8CypCfod6Z80yFk16Aq2iDImxlK+ylfZ+z2dzGZISHB04Q0OdgxCVZEFC6Br15LTN2+GO+6oeH1vd8MN1MGc6tq6RQVql2dvXM398st8fvvtGO3bR3PBBSG6mitepd+R/kldxKshEApsu92OzWbDYrFodEExhDImRlK+yvbbbzB5Mnz+ecXLPvWU49m9cXH/FAi+vte0Ot1ofbVuoLbbl8fLKVC7PFeXzmFiJOXLP6mLeDUEQhdxEREJHBU9T/rECbv9zTft9vPO86ybdlldeL3RjdZX3XB9+ezbQGy3nhUsIlIz1EW8GgLhCnZBQQFHjx4lJiaG4OBgXzdHaiFlTIwUiPmq6hWz8kYCb9cOXnkF3nrrn8chWSwwaBD85z9w9GjVu/AG8sjB1V133Torf/55nFNOCadv36AaG8yprq1blwXiOUwCh/Lln9RFvBoCocDWvRliNGVMjBRo+arK47Kc65U2EnhpmjeH225zdPlOTPT9iMXeWN9XAi1fEniUMTGS8uWfPKkRg4xujIiISKAqq0g+cMAxvaxC12ZzFOUVFdeXX+4YVGzAAPfidfBgx7ZLK+wrexXZYik5EJknqru+iIhIXaQCW0REar2qXI0tr0i22x1Xk8eOdXw9dMjxHGrn86h37HAvjMty991lF7GDB8MVVwTmVWQREZG6SgW2iIjUalXt4r1hQ/lFst3uKKwrczW5LCkp5c/XVWQREZHAogI7AFksFmJiYrDoMoYYRBkTI9Vkvjzp4p2aClu2OJ4FvWWLo8CujJYtoUMHx3OomzRxXGlOS4MHH6x43cREz/ZHKqbzlxhNGRMjKV+BT4OcFRMIg5yJiEjFnM90Lu8qdGQk9O7tKKgPHqza66xbV/Iqc3WfJy0iIiL+w5Ma0Wx0Y8T7bDYbOTk52Gw2XzdFaillTIxUU/mqqIs3QFYWrFrlKK5NJjj9dBgxwjGQ2NdfQ9Om/4zcXZzJ5Hh0Vc+eJedZLI4u6M7liq8HjtdQce19On+J0ZQxMZLyFfhUYAcgq9VKSkoKVqvV102RWkoZEyPVVL4qur/ZacQIRzGelQW//eZ4LvWECdCrF8yd61imKkWycyTwpk3dpyclVe4xW1I1On+J0ZQxMZLyFfh0D7aIiASEyo4EnpfnuPf6uecqt92bb4bzzy99XnUfl6WRwEVEROoWFdgiIuL3KjMS+I4d8Oqr8MYbjkHGKuK8D7q0Lt5FVbdI1kjgIiIidYcKbBERqTE2G3z9tYnffgunfXsTF1xQcaFa0Ujgd98NP/7oGGzMqWlTuPVWx6jed9zhmFZ0fU/vg1aRLCIiIpWhAjsAmUwmQkJCMJU18o5INSljYoR/rkKHAAlAxc+jttkc65Q2Erdz2syZjq8mE1x6Kdx+O1xyCQT97zdcbGzVu3hL4NH5S4ymjImRlK/Ap8d0FaPHdImIeF9ZV6Gdfz+UNuiX3Q4ffwxXXlnx9keMgCefhGbNSp9f2fu3RURERIrzpEbUFWwREfGIp8VqZa5C33wzfPklpKY6HpmVkuL4l59fuTZdcknZxTWoi7eIiIjUDBXYASgvL48DBw7QtGlTQkNDfd0cqYWUMSlLZQYbK8puh/ffr/h51JmZMG9e1duVmFj1daV20flLjKaMiZGUr8CnAjtAFRYW+roJUsspY1JcRYONLVsGnTvDli3u/zIyKrf9K6+ECy90DEzWpImjaI6Lg1NPdbxGaVfAKzsSuNQtOn+J0ZQxMZLyFdhUYIuISIUq08172LDS5wcFgdVa8WtMmFB6N+45cxwFvMlUvZHARURERIxm9nUDRETE/23YUHE3b7vdUUx37ep4NNarr8J//wtZWY6rzGUNiGoyQXJy2VehBw92DILWtKn79KSk0gdHExEREfEVXcEWEamDKjtQ2e7dsGoVvPJK5ba7eLFjRO/iqnsVevBguOIKjQQuIiIi/k0FdgAKDg4mOTmZ4OBgXzdFaillrHYrb6CyK6+E7793FNWrVsH/nkpRacnJpU93XoWuzvOoNRK4VIbOX2I0ZUyMpHwFPj0Huxg9B1tEarOyBipziox0dOl2MpvhvPNg4EBHIXzoUPmDje3aVfEju3QVWkRERAKJnoNdyxUUFJCRkUGjRo306ZYYQhnzf1UpVMsbqMwpKwsiIhzPlR40yPE1JsYx75RTqj/YmMUC5533T74sFuVLvEvnLzGaMiZGUr4CnwY5C0CFhYVkZmZqCH8xjDLm31auhBYtoG9fuP56x9cWLRzTiysogJ9+gkWL4KqrKh6oDGDFCvjgAxg+/J/iGrw32JjyJUZSvsRoypgYSfkKfLqCLSISQCp6FvWMGdCw4T/PoP75Z8jL8+w10tLKnqfBxkRERETKpgJbRCRAVOZZ1JMnl5zXsCGcdRbExTmuTFckMbH8+RpsTERERKR0KrBFRAJEZZ5FDdC5M/Tv7yiqzzoLWrd23Cdts8HGjY6r3eUNVFbW86hFREREpHwqsAOQxWL53+BA6pMpxlDG/FNKSuWWu+8+uO66ktMtluo/j9oblC8xkvIlRlPGxEjKV+DTIGcBKCgoiNjYWIKC9PmIGEMZ8z9pafDuu5Vbtrwu3t4aqKw6lC8xkvIlRlPGxEjKV+DTOxeACgsLyc/PJyQkBLNZn5GI9ylj/qOwEF57DR58EI4dK3/Zynbx9vVAZcqXGEn5EqMpY2Ik5Svw6V0LQAUFBezfv5+CggJfN0VqKWXMP2zeDD16wO23O4rrDh3giScchbSzS7eTp128nQOVXXed42tN9kRTvsRIypcYTRkTIylfgU8FtoiIj9hs8NVX8N57jq82m2P6sWMwZgx07w4//AAREY7C+b//hUce8X0XbxEREREpnbqIi4j4wMqVjkduFR0VPCkJBg2CpUvh6FHHtOHD4fnn3e+r9nUXbxEREREpnQpsEZEatnKlYyTv4o/K2r8f5s93fN++Pbz8MvTuXfo29CxqEREREf+jAjtAaeh+MZoyZgybzXHlurTnUDs1auS4/zosrMaaVeOULzGS8iVGU8bESMpXYDPZ7eX9mVf3bN26FYAOHTr4uCUiUht99RX07VvxcuvW6Qq1iIiIiD/wpEbUIGciIjUkOxveeadyy6akGNsWEREREfE+FdgBKD8/nz179pCfn+/rpkgtpYxVXlkjgRf1449wxx3QpAksWlS57RYd1Ky2Ub7ESMqXGE0ZEyMpX4FP92AHILvdTkFBAerdL0ZRxiqnrJHA58yBAQPg/fdh4ULH/dROp5wChw9DVlbp92GbTI5t9OxpfPt9RfkSIylfYjRlTIykfAU+FdgiIlVQ1kjgBw7AkCGOAcpycx3TgoMdj9a6/XbHfdX/+pdjXZPJfX2TyfF19mw9cktEREQkEKmLuIiIh8obCdw5LTcXWrd2PMP6wAHH1ey+fR1F9ODBsHw5NG3qvm5SkmP64MHG74OIiIiIeJ/PC+yjR49y77330qNHD7p06cLo0aP566+/XPO3bdvG8OHD6dy5M3369GFRsRsYCwsLmTt3Lj179qRTp07cfPPN7Nmzp6Z3Q0TqkA0b3LuFl+WVV+DeeyEuruS8wYNh927HaOFLlji+7tql4lpEREQkkPm8wB4zZgz79u3j1VdfZfny5YSFhXHTTTdx8uRJ0tPTGTVqFC1atGDFihXcddddzJkzhxUrVrjWnzdvHu+//z5PPvkkH3zwASaTidtuu61WDwwQHBxMkyZNCA4O9nVTpJZSxsq3c2flljt0qPz5Foujy/h11zm+1pVu4cqXGEn5EqMpY2Ik5Svw+fQe7PT0dJKSkhgzZgynnHIKAGPHjuWKK67gzz//ZNOmTYSEhDBt2jSCgoJo3bo1e/bs4dVXX2XIkCHk5+ezePFi7r33Xnr37g3ArFmz6NmzJ2vXrmXgwIG+3D3DmM1m6tev7+tmSC2mjJUuKwtefBGefbZyy9fmkcCrQ/kSIylfYjRlTIykfAU+n17BjoqKYubMma7iOi0tjUWLFpGQkECbNm3YvHkz3bp1Iyjon88BevTowa5duzh69Cjbt28nJyeHHj16uOZHRkZy+umn88MPP9T4/tQUq9XKsWPHsFqtvm6K1FLKmLusLHj6aWjZEh55BI4fh6ByPp40mSA5uXaPBF4dypcYSfkSoyljYiTlK/D5zSjiU6ZMYenSpYSEhDB//nzq169Pamoqp556qttyjRs3BuDgwYOkpqYCkFjsMlHjxo1JSUmpmYb7gM1m49ixYzRo0MDtwwcRb6lrGbPZHPdVp6Q4rjr37Onorp2d7bhi/cILcOyYY9m2bWHqVAgJgauvdkzTSOCeqWv5kpqlfInRlDExkvIV+PzmXRs5ciTXXHMN7733HuPGjWPJkiXk5uYSEhLitlxoaCgAeXl5nDx5EqDUZTIzM6vVnry8PLf/m81mgoODKSwspKCgoMTyznbl5+eXeG5dUFAQFosFm81W4tMok8lESEgIdru91PvGQ0JCMJlMFBQUUFhY6GqbzWbDZrMBlLvd0vYFHPd3mM1mt+06WSwWgoKCKtzX8rZrtVpd7XPyxjEsb7sVHcOqvjcV7aunx7Cy2/XlMXRmzNm+6h5DT/Lt5DyGlXlvbDb4z39MpKaaSEiw07dvEMHBlXtvPvzQzOTJQRw4YHIt06QJ9Opl4//+z8yxY47pp5xSyJQpcP31Zux2xzF8772S6yYl2Zk+3crAgYUUfXtr8hxRlWNYnJHnCHAMUGnEeVbniMA8ht7Md15entuyRuW7tH01+r2pK+cIf8930d+ROke4bzcQzhFFtwv+eY4o+jdYZbZbW4+hP+XbE35TYLdp0waAJ554gp9++ol33nmHsLCwEkFwHuj69esTFhYGOA6S83vnMvXq1atyWwoLC9m3b5/btIiICOLj47HZbCXmFW3/4cOHyXU+/PZ/4uPjiYiI4Pjx4xw5csRtXv369WnSpAl2u73U7bZs2RKLxUJaWho5OTmu9uXm5pKTk0P9+vU5efKk62q+U2hoKMnJyQDs37+/RIiaNWtGSEgI6enpZGVluc2LiooiJiaGvLw8Dhw44DYvKCiIFi1aAJCSklLih61p06bUq1ePzMxM0tPT3eZFRkbSuHFjrFZriX01mUy0bt0agEOHDpX4gUpISCA8PJzjx4+TlpbmNq9BgwYkJiaW+r4BtGrVCpPJRFpaGidOnHCbFxcXR8OGDTlx4gSHio1IFRYWRlJSEkCp223evDlms5ljx46RnZ3tNi86Opro6Ghyc3M5ePCg27zg4GCaN28OOHpiFP8hTkpKIiwsjIyMDDIyMtzmNWzYkLi4OAoKCkq0yWw206pVKwBSU1NL/OwkJibSoEEDsrOzOXr0qNu88PBwEhISXPl2Ziw1NRWz2ex6b44cOeL6YMupcePGREZGkpOTw+HDh93m1atXj6b/exZVacewRYsWBAUFcfToUY4fP+42LyYmhqioKHJzc0v0SAkJCaFZs2YALF6czuOPx5Ca+s9gIE2b2pk7F3r2zCjxYVujRo2IjY0lPz+fRYvSueuuhBKP2zp4EN5/33H5uWXLfMaNO8Zllx0nObkJFkt9jh3L4tixY5x1Fnz5JWzeXI/s7HDatWtIjx5W9u/fQ/HdrclzhFNsbCyNGjXyu3OE8xejM19OOkc4BMI5ojh/OkcU//DmwIEDJf64S05OJjQ0lIyM8s8R+4s9LsBisdCyZUvAke/if8A1adKE+vXrk5XlOEcU5U9/Rzj56znC3/+OKPo7Mj4+XucIAuscAe5/R/jjOaLo32BOOkc4+Ooc4QmTvfjRqEFHjx5l06ZNXHLJJViK9KUcP348OTk5mM1mGjVqxPTp013zvvnmG0aNGsU333zDgQMHGDZsGGvXrnX9kABcd911tGvXjkcffdTjNm3duhWgRNd0f/rkOS8vj5SUFJKTk6lfv74+VdLVKRdvXsFOSUkhMTGR0NBQv/3keeVKGDrU/r8C2VRkvh0w8f77Vq64wlbqdvPyCmnZ0oTjd66JkuxER8OuXfn877Dq6lSx7Vb1HFFQUMCePXtISEhwLQs6R3i63dp0nvX2FezU1FSaN29OaGioX16d0jkisP+OKPo7skGDBjpHEFjniKLbBf+7gn3y5En27t3r+husMtutrcfQn/K9fft2ADp06FBiu8X59Ar24cOHmTx5MjExMZxzzjkAFBQU8Pvvv9OvXz9iY2N5//33sdlsrgJ806ZNtGzZkpiYGCIiIggPD+e7775zFdhZWVn8/vvvDB8+vFptKxroosxmc5nzoGR39aIsFovbBwlFmUymcrdbdKh+5wcPzmnlbRfK3pfi2y2uon0tb15QUFCZ941U5xiWt92KjmFV3xuoe8fQmbF69eq57V9N5Luy27XZYMIEsNtLFsd2uwmTCcaNCyIjI4hDhxxXpQ8edNxn7fhqptj5vnirOXYM/vvfUPr0cZ9j1HtT08fQqabzbTabadiwYYl8VXa7Okc41KbzrDfz7cyX88qPUe9NVY+hzhGV264/57vo70jncdMxdAiEc0Rx/naOCAoKKvVvsMpst7YdQ3/Ld2X5tMBu164d559/Po899hhPPvkkkZGRLFiwgKysLG666SZCQ0N57bXXePjhh7n11lv55ZdfePPNN3nssccAx8EZPnw4M2bMIDo6mqZNmzJ9+nQSEhLo37+/L3fNUMHBwcTHx/u6GVKLBULGNmyAYj2z3NjtkJYGt99evdepxeMl+kwg5EsCl/IlRlPGxEjKV+DzaYFtMpmYPXs2L7zwAhMnTiQ7O5uuXbvy7rvv0qRJEwBee+01nnrqKa666iri4uK47777uOqqq1zbGD9+PFarlUceeYTc3Fy6devGokWLyv1kItA5Bz+wWCwe33QvUhmBkLHKFr6dO0PXro7RwZs0cfxLTITdu/8ZBbw8epa19wVCviRwKV9iNGVMjKR8BT6f3oPtj5z3YFemf72v5OXlsW/fPtcADCLe5u8ZO3wY7roLli6teNl16yjRxRscXcxbtIADB9wfs+VkMkFSEuzapcdteZu/50sCm/IlRlPGxEjKl3/ypEbUxyIiEjAOH4Z774WWLSsurk0mSE52PNO6NBYLzJnzz7LF1wU9y1pEREREPKMCW0T8gs0GX30F773n+Fp0AMcjR+C++xyF9YwZcOIEdOsGDz/sKIarWiAPHgzLl8P/ngDikpTkmD54sBd2TERERETqDL95DraI1F0rVzpGBC86aFlSEjz+OGzfDi+95CiqwXE/9WOPwSWXOArpM88sfd3ZsytXIA8eDFdc4Rg0LSXFcc91z566ci0iIiIinlOBLSI+5XiWdcn7oPfvh5tv/uf/XbvCtGlw6aXuV6y9USBbLKXfpy0iIiIi4gkNclZMIAxyJlJbOAcaK+9xW8HBsGwZXH55ya7gIiIiIiJG0yBnIhIQKnqWNUBBATRsqOJaRERERPyfCuwAlJ+fz/79+8nPz/d1U6SWqqmM7dhRueUq+8xrCQw6h4mRlC8xmjImRlK+Ap/uwQ5Adrud3Nxc1LtfjGJ0xo4dgxdegJkzK7d8YqIhzRAf0TlMjKR8idGUMTGS8hX4VGCLiNfYbOUPNnbsmKOonjsXsrMd04KDHd3AS2MyOUYEL+tZ1iIiIiIi/kQFtoh4RVmP2pozB/r2dRTWc+b8U1h36uQYFdxmg2HDHNOKflhb2WdZi4iIiIj4CxXYIlJtZT1q68ABGDIE6tWDkycd0zp2dBTWV1wB5v+NArF8efWeZS0iIiIi4g9UYAegoKAg4uPjCQrS2yfG8CRjNpujOC7tViHntJMnoUMHR2F95ZX/FNZO3niWtQQOncPESMqXGE0ZEyMpX4FP71wAslgsRERE+LoZUot5krHKPGoLHFej+/Ur7zWhT59KvaQEOJ3DxEjKlxhNGRMjKV+BT4/pCkA2m43MzExsNpuvmyK1lCcZO3Cgcts8dKiajZJaQ+cwMZLyJUZTxsRIylfgU4EdgKxWK0eOHMFqtfq6KVJLVSZj+/fD44/D3XdXbpt61JY46RwmRlK+xGjKmBhJ+Qp86iIuIm5sNvj6axO//RZO+/YmLrjgn3uhbTb4/HNYuBBWr4bCQsd0s/mf74vTo7ZEREREpK5QgS0iLv88aisESAAcxfGjjzoGH3vtNdi795/le/WC2293FNjXX++YpkdtiYiIiEhdpQJbRICyH7W1fz/cdts//4+OhpEjHdNOO+2f6SEhetSWiIiIiNRtKrADkMlkon79+piclwdFqqm8R205hYTAq6/C1VdDWFjJ+XrUllSWzmFiJOVLjKaMiZGUr8CnAjsAhYSE0KRJE183Q2qRyjxqKz8fmjUrvbh20qO2pDJ0DhMjKV9iNGVMjKR8BT6NIh6A7HY7hYWF2Mu73Cjigco+aislxdh2SN2gc5gYSfkSoyljYiTlK/CpwA5A+fn57Ny5k/z8fF83RWqB3bvhuecqt6wetSXeoHOYGEn5EqMpY2Ik5SvwqcAWqaPsdnjnHejUCbZu/WfE79KYTJCcrEdtiYiIiIiURwW2SB2Ung7XXQcjRkBWFpxzDrz0kqOQLl5o61FbIiIiIiKVowJbpI758kvo0AE++MBRMD/+OKxfD2PHwvLl0LSp+/JJSY7petSWiIiIiEj5NIq4SC1js5X+qKzcXHj4YZg507HcKac4uoh37/7Pus5HbX35ZT6//XaM9u2jueCCEF25FhERERGpBJNdQ9S52bp1KwAdOnTwcUvK5hxd0Gw26xl54mblSsfzrIs+cispCSZNgtdfd9xrDXDHHTBjBjRoUPp2lDExkvIlRlK+xGjKmBhJ+fJPntSIuoIdgEwmExZdUpRiVq6EoUMdg5cVtX+/o8AGiIuDRYtg0KDyt6WMiZGULzGS8iVGU8bESMpX4NM92AGooKCAlJQUCgoKfN0U8RM2m+PKdXn9UcLC4KefKi6uQRkTYylfYiTlS4ymjImRlK/ApwI7ABUWFpKTk0NhYaGvmyJ+YsMG927hpcnNhR07Krc9ZUyMpHyJkZQvMZoyJkZSvgKfCmyRWiAlxbvLiYiIiIiI51Rgi9QCiYneXU5ERERERDynAlukFjjvPAgPL3u+yQTJyY5HdomIiIiIiDFUYAcgi8VCbGysRhgUAPLyYMQIOH689PnOJzzMnk2ln2etjImRlC8xkvIlRlPGxEjKV+BTgR2AgoKCaNSoEUFBespaXZeVBQMHwgcfQHAwTJzoeO51UUlJsHw5DB5c+e0qY2Ik5UuMpHyJ0ZQxMZLyFfj0zgUgm83GyZMnqVevnj7dqsNSU+HSS+HHHx3dw1euhP79YcYMx6jiKSmOe6579qz8lWsnZUyMpHyJkZQvMZoyJkZSvgKfrmAHIKvVSmpqKlar1ddNER/56y/Hfdc//ghxcfDVV47iGhzFdJ8+cN11jq9VOTcrY2Ik5UuMpHyJ0ZQxMZLyFfhUYIsEmC1b4NxzYedOaNUKvvkGzjrL160SEREREREV2CIBZO1ax1XpI0egSxfYuBHatPF1q0REREREBHQPtohfstlK3kf9wQdw001QUAAXXOC45zoy0tctFRERERERJxXYAchkMhEaGorJ+fwlqVVWroQJE2D//n+mNWwImZmO76++Gt56C0JDjWuDMiZGUr7ESMqXGE0ZEyMpX4HPZLfb7b5uhD/ZunUrAB06dPBxS6QuWrkShg6Fsn4qL70UVq0Cs27uEBERERGpEZ7UiPozXcRP2GyOK9flfeS1dWv580VERERExHcq1UX8hx9+8Gij3bp1q1JjpHLy8vLYv38/SUlJhBrZT1hq1IYN7t3CS7Nvn2O5Pn2MbYsyJkZSvsRIypcYTRkTIylfga9SBfaIESPKvA/A2cO86Pxt27Z5oWlSHvXsr31SUry7XHUpY2Ik5UuMpHyJ0ZQxMZLyFdgqVWC/9dZbru8PHjzIlClTGDJkCJdccglxcXFkZGTw73//m/fff5/HH3/csMaK1FZ2O/z2W+WWTUw0ti0iIiIiIlI1lSqwu3fv7vp+xIgR3HTTTUyePNltmTPPPJOwsDBef/11Lr30Uu+2UqQW++03GDcOvv66/OVMJkhKcjyyS0RERERE/I/Hg5z98ssvnHPOOaXO69KlCzt27Kh2o0TqguxsuOce6NzZUVzXqwfXX+8opIvfkeH8/+zZYLHUdEtFRERERKQyPC6wExIS+Oqrr0qd99lnn9GsWbPqtkkqEBwcTLNmzQgODvZ1U6QcNht89RW8957jq83mmG63wwcfQLt28MILYLXClVfC77/Du+/C8uXQtKn7tpKSHNMHD66ZtitjYiTlS4ykfInRlDExkvIV+CrVRbyoUaNGMW3aNI4cOUK/fv2Ijo4mLS2Nzz77jK+++oqZM2ca0U4pwmw2ExIS4utmSDlWrnQ8cqvoqOBJSXDvvfDRR/DvfzumtW4Nc+c6nm/tNHgwXHGFY7TwlBTHPdc9e9bslWtlTIykfImRlC8xmjImRlK+Ap/JXoVh6t555x3mz5/P0aNHXdMSExO55557GDhwoFcbWNM8eYi4rxQUFJCenk5UVJQ+3fJDK1fC0KHlP686LAweeshRcIeF1VzbKksZEyMpX2Ik5UuMpoyJkZQv/+RJjejxFWyA4cOHM3z4cHbu3ElmZiZRUVG0aNGiKpuSKigsLCQrK4uGDRv6uilSjM3muHJdUXG9dSu0aVNz7fKUMiZGUr7ESMqXGE0ZEyMpX4HP43uwnTIzM9m1axfbt28nMjKSnTt36pltUudt2ODeLbw0ubkVLyMiIiIiIoGnSlew58+fz8KFC8nNzcVkMtGxY0dmzZpFRkYGixcvJjIy0tvtFAkIKSneXU5ERERERAKHx1ew33nnHV588UVGjRrF0qVLXVetR44cyb59+5gzZ47XGykSKBITvbuciIiIiIgEDo8L7LfffpvRo0czYcIE2rdv75res2dPJk6cyL+dwyOLYSwWC1FRUVj0QGS/07MnxMaWPd9kguRkx3L+TBkTIylfYiTlS4ymjImRlK/A53EX8YMHD9K9e/dS57Vq1Yq0tLRqN0rKFxQURExMjK+bIaXYvh2OHy99nsnk+Dp7ds0+cqsqlDExkvIlRlK+xGjKmBhJ+Qp8Hl/BTkxM5Mcffyx13q+//kqi+r4arrCwkJMnT1JYWOjrpkgRaWkwaJBjELPTT4emTd3nJyXB8uWO51z7O2VMjKR8iZGULzGaMiZGUr4Cn8cF9tChQ1mwYAGLFi1i9+7dAJw4cYLPP/+chQsXctVVV3m7jVJMQUEBBw4coKCgwNdNkf/Jz3c8+3rXLmjVCtavhz17YN06WLLE8XXXrsAorkEZE2MpX2Ik5UuMpoyJkZSvwOdxF/HbbruN/fv3M2PGDGbMmAHAjTfeCMCgQYO4/fbbvdtCET9nt8Odd8LXX0NEBHz8MTh79vTp49OmiYiIiIhIDfK4wDaZTDz++OOMGjWK7777joyMDCIiIujevTunnHKKEW0U8Wtz58KrrzrusX7/fSgy9p+IiIiIiNQhHhfYixcvZtCgQbRs2ZKWLVsa0SaRgPH55zBpkuP76dPh0kt92x4REREREfEdj+/Bnj17Nn369OGWW27h448/5uTJk0a0SyoQFOTxZyPiZdu3wzXXQGEhjBr1T6FdWyhjYiTlS4ykfInRlDExkvIV2Ex2u93uyQrZ2dl89tlnfPLJJ3z//feEhobSv39/rrzySs455xxMzmcRBaitW7cC0KFDBx+3RPzZsWNw9tnw119w/vnwxRcQGurrVomIiIiIiLd5UiN6XGAXdeTIET755BPWrFnD1q1biY2N5bLLLuP++++v6iZ9TgW2VKSgAC6+GP79b2jeHL7/Hho39nWrRERERETECJ7UiB53ES8qLi6Om266ienTp3PDDTdw7Ngx3njjDY+2kZGRwdSpU+nVqxdnnnkm1113HZs3b3bNf/DBB2nbtq3bv169ernmFxYWMnfuXHr27EmnTp24+eab2bNnT3V2y+/l5eWxe/du8vLyfN2UOmnCBEdxHR4Oq1bVzuJaGRMjKV9iJOVLjKaMiZGUr8BX5Q7+Bw8eZM2aNaxZs4Zt27YRFxfHjTfeyBVXXOHRdiZNmsTRo0eZOXMm0dHRLFmyhFtuuYWVK1fSunVr/vjjD+644w6GDx/uWsdisbi+nzdvHu+//z7PPPMM8fHxTJ8+ndtuu43Vq1cTEhJS1d3ze1ar1ddNqJNefhnmz3eMGP7uu1CbOzooY2Ik5UuMpHyJ0ZQxMZLyFdg8LrDffvttPvnkE37++WfCwsK48MILmTRpEueeey5ms2cXxPfs2cPGjRt57733OPPMMwF4+OGHWb9+PatXr+bOO+/kr7/+YuzYscTFxZVYPz8/n8WLF3PvvffSu3dvAGbNmkXPnj1Zu3YtAwcO9HT3RNzYbLBhA6SkwMGDcN99junPPAOXX+7btomIiIiIiH/xuMB+5plnOPvss3nmmWcYMGAA9evXr/KLR0VF8corr3DGGWe4pplMJux2O5mZma7uEa1bty51/e3bt5OTk0OPHj1c0yIjIzn99NP54YcfVGBLtaxc6egOvn+/+/Tevf8ptEVERERERJw8LrDXrVtHfHy8V148MjLSdeXZ6dNPP2Xv3r2cf/757NixA5PJxJtvvsn69esxm8307t2biRMnEhERQWpqKgCJiYlu22jcuDEpKSnValvx+x7MZjPBwcEUFhZSUFBQYvnQ/w0hnZ+fT/Fx44KCgrBYLNhsthJdPkwmEyEhIdjtdvLz80tsNyQkBJPJREFBAYWFha622Ww2bDYbQLnbLW1fAIKDgzGbzW7bdbJYLAQFBVW4r+Vt12q1utrn5I1jWN52KzqGnrw3H35o5rrrgrDbi4+Kb2f9eli61MqwYZYqHcPKvje+PIbOjDnbV5VjWHRfPcm3k/MY+irfpe2r0e9NbTuGZW0XHONnGHGeralzBBh3DAPhHFGcP50j8vLy3JbVOSLwzhH+nu+ivyN1jnDfbiCcI4puF/zzHFH0b7DKbLe2HkN/yrcnPC6w4+PjOXbsGK+//jrfffcdWVlZREVF0bVrV2666SZiYmI83aTLli1beOihh7jgggvo168fc+fOxWw207RpUxYsWMCePXt47rnn2LFjB2+++abrGdzF77UODQ0lMzOzyu0oLCxk3759btMiIiKIj4/HZrOVmAfQpk0bAA4fPkxubq7bvPj4eCIiIjh+/DhHjhxxm1e/fn2aNGmC3W4vdbstW7bEYrGQlpZGTk4OgCsQubm51K9fn5MnT7o+bHAKDQ0lOTkZgP3795cIUbNmzQgJCSE9PZ2srCy3eVFRUcTExJCXl8eBAwfc5gUFBdGiRQsAUlJSSvywNW3alHr16pGZmUl6errbvMjISBo3bozVai2xryaTydVT4dChQyV+oBISEggPD+f48eOkpaW5zWvQoAGJiYmlvm8ArVq1wmQykZaWxokTJ9zmxcXF0bBhQ06cOMGhQ4cAR7fwiRObU/r4+ibAzt13wxVX2AgLM3Ps2DGys7PdloqOjiY6Oprc3FwOHjzoNi84OJjmzZsDjrEMiv8QJyUlERYWRkZGBhkZGW7zGjZsSFxcHAUFBSX21Ww206pVKwBSU1NLnEQTExNp0KAB2dnZHD161G1eeHg4CQkJrnw783Lo0CG39+bIkSOunzunxo0bExkZSU5ODocPH3abV69ePZo2bQpQ6nvTokULgoKCOHr0KMePH3ebFxMTQ1RUFLm5uSU+MAsJCaFZs2YAHDhwoMSJOzk5mdDQUDIyMkqcCxo1akRsbCz5+fnsL9Y9wWKx0LJlS8CR7+In5yZNmlC/fn2ysrI4duyY2zx/Okc4xcbG0qhRI787R4SGhhIREeHKl1OgnCOcwsLCSEpKAkrPd/PmzTGba+c5ojh/OkfY7XaCgoJcH+boHBF45wh//zui6O/Ixo0b6xxBYJ0jwL//jnAWc8V/R+oc4eCrc4QnPH5MV2pqKtdccw3Hjh2jc+fOxMXFceTIEX788UeioqJYvnx5la5wf/HFF9xzzz106tSJhQsXEhYWht1uJzs7m8jISNdyP/30E9dccw1Lly4lNTWV8ePHu+4Hd5owYQL5+fnMnz/f43Y4h2A/9dRT3abrk+eS++pPnyp581PTr782cdFFFQ+Q9+WXhfTrp0+ea2u+dXVK54ii29XVKZ0jnHSOcN9XnSN0jii6XZ0jdI4ovq+15Ryxfft2wKDnYE+ePJmffvqJN954w/WpBTg+Vbr55ps566yzePbZZz3ZJO+88w5PPfUU/fv3Z8aMGeWO/n3ixAm6dOnC3LlzSUxMZNiwYaxdu9b1KRTAddddR7t27Xj00Uc9agcExnOwrVYrmZmZNGzYkKCgKg8EL2V47z24/vqKl1uyBK67zvj2+IIyJkZSvsRIypcYTRkTIylf/snQ52D/5z//Yfz48W7FNTi6UowbN47169d7tL0lS5bwxBNPcMMNNzB79my34nry5Mnccsstbss7d65Nmza0a9eO8PBwvvvuO9f8rKwsfv/9d7p27erprgUMm81Genp6iU9XxDuK3dJf7eUCkTImRlK+xEjKlxhNGRMjKV+Bz+OPRWw2G1FRUaXOi46OLnH/Q3l27drF008/Tf/+/bn99tvd7ucICwvjsssuY8yYMcyfP5+BAweya9cuHn/8cS677DJXH/rhw4czY8YMoqOjadq0KdOnTychIYH+/ft7umsigKNwNpko4x5sx7ykJOjZs2bbJSIiIiIi/s3jArtt27Z89NFH9OrVq8S8Dz/8sMS9y+X5/PPPKSgoYO3ataxdu9Zt3lVXXcWzzz7LnDlzWLBgAQsWLCAiIoJBgwYxceJE13Ljx4/HarXyyCOPkJubS7du3Vi0aFG53cxFypKZCVdd9U9xXbzQdo41MXs2WCw13jwREREREfFjHhfYY8eO5ZZbbiEjI4NBgwYRGxtLWloaq1at4ptvvmHu3LmV3tYdd9zBHXfcUe4yF110ERdddFGZ8y0WC/feey/33ntvpV9XpDQ2m+Oe6m3boGlTeOwxmDbN/TnYSUmO4nrwYF+1UkRERERE/JXHBfZ5553Hc889x/Tp09m4caNremxsrKu7txjLbDYTGRnp8TPZpHz33Qeffgr16sFHH8FZZ8FNN8GGDZCS4ug63rNn3bhyrYyJkZQvMZLyJUZTxsRIylfg83gUcSe73c7OnTtdo9w5nxEY6AJhFPHa7vDPP/P1PfdwaMsWzCEhtBgwgD4zZ1I/NpYfX36ZLbNnk5OSQoPERM6cMIEz77wTgNfbtydrzx63bRXk5NDz6ac5+8EHy33NRYvg1lsd3y9dCsOGGbJrIiIiIiISYDypEas89ntOTg716tWjXr16AG4PcG/SpElVNyuVUFhYiNVqJSgoqNZ9ulVw8iQrLrmEjrfdxuBPPiE/O5s1N97IZ6NG0Wn0aDZOmcLQtWtJOOssUn74gQ969SK2fXua9e3LqN9+c9vWf6ZMYefq1XS5665yX3P9ehgzxvH9o4+quIbanTHxPeVLjKR8idGUMTGS8hX4PC6wt2/fzr333stff/1V5jLbtm2rVqOkfAUFBezbt4/k5GTXA9Nri+y9e4nr1Ilzpk7FbLFQLyaGTrffzpoRIxi8ahWj9+whJCKCQquVk2lpYDIR2qhRie3sXbeOLbNmceOPPxISHl7m6+3aBUOGQEGBo7CeOtXAnQsgtTlj4nvKlxhJ+RKjKWNiJOUr8HlcYE+dOpX09HTuu+8+GpVS2IhUR3Tbtgz99FO3aTuWLyf+rLMACImI4Ngff/B6+/bYbTbOmjSJ+C5d3JYvtNlYe8cd9JgyhahTTinztbKz4fLLIS0NzjwT3ngD9EGhiIiIiIhUlccF9o4dO3j22We5+OKLjWiPiIvdbmfjlCn8vWoV165f75resFUrJp48yZGff+bDK66gfuPGnH3//a7525YsoeD4cc4cP77MbdtscP318OuvjsHLPvoI6tc3dHdERERERKSW8/h6XXJyMoWFhUa0RcQlLyuLj4cO5fd33uHa9euJKzKggCU4GEtwMAldu3LmhAlsX7LEbd1fXnmFjqNHE/y/8QFK89BDsHo1hIXBhx86Hr8lIiIiIiJSHR4X2JMmTWLOnDl8//335OXlGdEmqYTaMGJ7WTL+/pt3unUjPyuL4Zs3u4rrzbNmseqaa9yWteXlERYd7fp/zqFDHNi4kdNHjChz+2+9Bc8/7/h+8WLo3t37+1Ab1OaMie8pX2Ik5UuMpoyJkZSvwOZxF/GWLVtit9sZOXJkqfNNJhO///57tRsmZQsNDaV169a+boYhctPT+aBfP5r168fFixZhKnJTdFKvXmx44AG2L11K26FDObhpE/+dM4cL5s1zLXNg40bCmzShUatWrmk22z/Psj56FCZNckx/+GG47roa27WAUpszJr6nfImRlC8xmjImRlK+Ap/HBfaDDz5Ieno6V199NXFxcUa0SeqwX19/ney9e/lj6VJ2LFvmNm/C8eNcvnw5/3nkEf7v1luJbN6cvnPm0O7qq13LZO7cSXjTpq7/r1wJEybA/v3ur9O9Ozz+uKG7IiIiIiIidYzJbrfbPVmhU6dOPPPMM1x66aVGtcmnPHmIuK/k5+dz6NAh4uPjCQkJ8XVz/NbKlTB0KJSWcJMJli+HwYNrvl2BQBkTIylfYiTlS4ymjImRlC//5EmN6PE92I0bN6ZeOYNHifHsdjt5eXl4+NlInWKzOa5cl3eIJk50LCclKWNiJOVLjKR8idGUMTGS8hX4PC6wR48ezezZs9m9e7cBzRHxjg0bSnYLL8puh337HMuJiIiIiIh4g8f3YH/++eccOHCASy65hMjISMLDw93mm0wmvvjiC681UKQqUlK8u5yIiIiIiEhFPC6w4+Li6N+/vxFtEfGaxETvLiciIiIiIlIRjwvsZ555xoh2iAeCgoJISEggKMjjt6/OyMkpf77JBElJ0LNnzbQn0ChjYiTlS4ykfInRlDExkvIV+Kr8zv39999s3LiRw4cPM2LECPbt20e7du1KdBkX77NYLDrO5fjuOyjy5C5MJvfBzkwmx9fZs8FiqdGmBQxlTIykfImRlC8xmjImRlK+Ap/Hg5zZbDYeeeQRLrvsMp5++mkWLVpEWloaL7/8MldeeSWpqalGtFOKsFqtZGRkYLVafd0Uv/PHHzBwIJw4AQMGwAcfQJHHYgOOK9d6RFf5lDExkvIlRlK+xGjKmBhJ+Qp8HhfY8+fPZ9WqVTz55JNs3LjRNYT8/fffT2FhIbNmzfJ6I8WdzWYjLS0Nm58+Y+rwzz+zrH9/XoqOZl5CAmtuvJETaWkA7Fixgjc7d2ZuZCSvtGjBN489hr2w0LXujhUreP2MM5jdoAGvtWnD1sWLK/26Bw44iuqjR6FbN1ixwnEle/duWLcOlixxfN21S8V1Rfw9YxLYlC8xkvIlRlPGxEjKV+DzuMBesWIF48ePZ8iQITRq1Mg1vV27dowfP56NGzd6s30SYApOnmTFJZfQ5NxzGZOayqjffuPk0aN8NmoUqVu2sGbECM5/8knuyshgyKef8usbb7D5fx/K7F23jk9vuone06cz4fhxBrz6Kl+MHUvKDz9U+LoZGXDxxbB3L5xyCnzyCTh711gs0KcPXHed46u6hYuIiIiIiBE8LrDT0tI47bTTSp0XHx9PVlZWtRslgSt7717iOnXinKlTsYSEUC8mhk63387+9evJ2r2bTnfcQevLLsNkNhNz2mmcctVV7F+/HoDNM2dy5vjxtLrkEkwmE8369mX45s00at263Nc8eRIuvxx+/dUxKvj//R/ExdXE3oqIiIiIiPzD4wK7efPmfP3116XO+/7772nevHm1GyWBK7ptW4Z++inmIpeJdyxfTvxZZ3HqkCH0nTnTNb3g5El2fvIJ8WedBUDq999TLyaGFQMH8lJMDG927kzGX39RLzq6zNezWuH662HDBoiMhM8+gxYtDNs9ERERERGRMnk8ivjIkSOZOnUqBQUF9O3bF5PJxJ49e/juu+9YvHgxDzzwgBHtlCLMZjMNGjTAbPb485EaZbfb2ThlCn+vWsW1/7tK7ZSfnc3HQ4cSVK8eXe++G4DcY8f4Yfp0rli5koRu3fjr449Zfe21XPv11ySefXYp24exY+HDDyE0FD7+GDp2rIk9q/0CJWMSmJQvMZLyJUZTxsRIylfgM9ntRR9gVDkLFy5kwYIF5ObmugY5Cw4O5tZbb2XChAleb2RN2rp1KwAdOnTwcUsCW15WFp+NGsWhLVu4atUq4oocz2N//MFHQ4bQID6ey5cvJywqCoA54eGcNXEi5z/5pGvZFQMHEtu+Pb2ff77Ea0ydCk88AWYzLFumgctERERERMT7PKkRq/Qc7Ntvv50bbriBH3/8kYyMDCIjI+nUqZPboGdiHLvdTmFhIWazGZPzoc5+JOPvv1lx6aVENmvG8M2bqR8b65q3c80aVl93HR1vu41ezz6LOeifCMacfjrWvDy3bdltNkr7DOjllx3FNcC8eSquvc3fMyaBTfkSIylfYjRlTIykfAW+Kvc9CA8Pp2fPngwaNIjevXvTqFEjCgsLeeutt7zZPilFfn4+u3btIj8/39dNKSE3PZ0P+vWjybnnMvTzz92K64PffstHV11F31mz6DNjhltxDdBpzBh+mjePPV98gb2wkB0rVrBv3TpOvfo6vvoK3nsPvvrK8Wzru+5yrPPYY3D77TW3f3WFP2dMAp/yJUZSvsRoypgYSfkKfJW+gv2f//yHFStWAHDllVfSu3dvt/k//PADTzzxBH/++Sc33nijd1spAePX118ne+9e/li6lB3LlrnNa9avH7aCAv49fjz/Hj/eNb1pz54M/fRTOowahclsZt3dd5O5ezeRzZsTO/F9zh58Jvv3l3ytMWNgyhSj90hERERERKRyKlVgr1mzhkmTJhESEkJwcDCfffYZc+fOpX///qSnp/PUU0/xySefYLFYGDVqlNFtFj/WddIkuk6aVOX1zxg5kjNGjgRg5UoYOtQxmFlp+vUD9ZwRERERERF/Uaku4m+88QadOnXim2++YdOmTQwaNIiXX36ZnTt3ctVVV7F69WrOP/98Vq1axX333Wd0m6UOsNlgwoSyi2uTCSZNciwnIiIiIiLiDypVYO/cuZORI0cSHh5OSEgId955J3/88Qd33nknVquVF198kVdffZWWLVsa3V6pIzZsoNRu4U52O+zb51hORERERETEH1Sqi3hOTg6JiYmu/yckJGC32wkKCuLjjz8mOjrasAZKSSEhIbRq1apWjyyYkuLd5cQzdSFj4jvKlxhJ+RKjKWNiJOUr8FWqwLbb7VgsFtf/nd9PmDBBxbUPmEymWv9DV+TzHK8sJ56pCxkT31G+xEjKlxhNGRMjKV+Br8qP6QLHlWypefn5+Rw8eLBWD9/fsyckJZU9iJnJBMnJjuXE++pCxsR3lC8xkvIlRlPGxEjKV+CrVoGtT1d8w263c+LECexljQBWC1gsMGdO6YOcOWM3e7ZjOfG+upAx8R3lS4ykfInRlDExkvIV+Cr9HOxp06YRHh4O4HrDp0yZQoMGDdyWM5lMvPnmm15sotRVV13luEq9b5/79KQkR3E9eLBPmiUiIiIiIlKqShXY3bp1A3D7JKW0aaX9X6Sq1q93FNf16sHSpZCd7bjnumdPXbkWERERERH/U6kC++233za6HSIlzJvn+DpiBFx2mW/bIiIiIiIiUpFq3YMtvhEUFERcXBxBQZXu4R9wUlJg5UrH92PH+rYtdVFdyJj4jvIlRlK+xGjKmBhJ+Qp8eucCkMVioWHDhr5uhqFefRWsVjjvPOjUydetqXvqQsbEd5QvMZLyJUZTxsRIylfg0xXsAGSz2cjOzsZms/m6KYYoKICFCx3f6+q1b9T2jIlvKV9iJOVLjKaMiZGUr8CnAjsAWa1WDh06hNVq9XVTDLFqFRw8CI0bw5Ahvm5N3VTbMya+pXyJkZQvMZoyJkZSvgKfCmzxOy+/7Ph6660QGurbtoiIiIiIiFRWle7B3rdvH3l5ebRp04bMzExmz55NSkoKF198MVdeeaWXmyh1ybZt8O9/g9kMo0f7ujUiIiIiIiKV5/EV7PXr13PJJZewYsUKAKZNm8bSpUs5dOgQDz74IMuWLfN6I6XuWLDA8fWyy6B5c9+2RURERERExBMeF9jz5s3j/PPPZ9y4cWRnZ7N27VpGjx7Nv/71L0aPHs1bb71lRDulCJPJRFhYGCaTyddN8arjx+GNNxzfjxvn06bUebU1Y+IflC8xkvIlRlPGxEjKV+DzuMDevn07I0eOJDw8nA0bNmCz2bjooosAOO+889izZ4/XGynuQkJCSEpKIiQkxNdN8aolSyArC9q0gQsv9HVr6rbamjHxD8qXGEn5EqMpY2Ik5SvweVxgh4aGuka127BhAzExMbRr1w6AtLQ0IiMjvdtCqRPs9n8GNxszxnEPtoiIiIiISCDxuIw566yzWLx4MatXr+bTTz9lwIABAPz666+89NJLnHnmmV5vpLjLy8vjr7/+Ii8vz9dN8ZpvvoFffoF69WDUKF+3RmpjxsR/KF9iJOVLjKaMiZGUr8DncYH94IMPcujQIe655x6SkpIYM2YMALfffjv5+fncc889Xm+k1H7z5jm+XncdREX5ti0iIiIiIiJV4fFjupKTk/nkk084evQosbGxrukvv/wyp59+uu4XEI8dPgzOwefHjvVtW0RERERERKqqSs/BNplMBAcH8+WXX3L48GEuuugiIiMjCQ4O9nb7pA547TUoKICzz4azzvJ1a0RERERERKqmSgX2/PnzWbhwIbm5uZhMJjp27MisWbPIyMhg8eLFGuhMKs1m++fZ17p6LSIiIiIigczje7DfeecdXnzxRUaNGsXSpUux2+0AjBw5kn379jFnzhyvN1LcBQcH07x581rRY+CTT2DfPoiJgauv9nVrxKk2ZUz8j/IlRlK+xGjKmBhJ+Qp8HhfYb7/9NqNHj2bChAm0b9/eNb1nz55MnDiRf//7315toJRkNpsJDg7GXAueZeUc3OyWWyAszLdtkX/UpoyJ/1G+xEjKlxhNGRMjKV+Bz+N37uDBg3Tv3r3Uea1atSItLa3ajZLyFRQUcOjQIQoKCgx7jcM//8yy/v15KTqaeQkJrLnxRk78771N+e473jn7bOaEh/NKy5ZsXbTItZ7dbmfTE0/wSsuWzI2M5I2OHflj+fJSX+PPP+Hzz8FkgttvN2xXpApqImNSdylfYiTlS4ymjImRlK/A53GBnZiYyI8//ljqvF9//ZXExMRqN0rKV1hYSHZ2NoWFhYZsv+DkSVZccglNzj2XMampjPrtN04ePcpno0aRm57Oiksvpf2NN3JXRgYXL1rEurvvJuX77wH475w5/Pr66wxZs4a7MjPp+dRTfDpihGt+Uc57ry+5BFq1MmRXpIqMzpjUbcqXGEn5EqMpY2Ik5SvweVxgDx06lAULFrBo0SJ2794NwIkTJ/j8889ZuHAhV111lbfbKDUse+9e4jp14pypU7GEhFAvJoZOt9/O/vXr2bFiBfViYugybhzmoCCa9evHaTfcwI8vvwxAbno650ydSsxpp2EymWg9aBDRp53GgY0b3V7jxAl4/XXH9+PG1fQeioiIiIiIeJ/Ho4jfdttt7N+/nxkzZjBjxgwAbrzxRgAGDRrE7errG/Ci27Zl6Kefuk3bsXw58WedxdHffiO2Qwe3eTGnn+7qJn7eY4+5zTu6bRtHf/uN+GLP33r/fUhPh5Yt4aKLDNgJERERERGRGuZxgW2323n88ce5+eab+fbbb8nIyCAiIoLu3btzyimnGNFG8SG73c7GKVP4e9Uqrl2/nv/OmUNwgwZuywTXr0/B8eMl1j22YwcrLr2U04YPJ7lXryLbhP9d8OaOO8BiMXQXREREREREaoTHBfbQoUMZN24cF1xwAS1atDCgSVIRi8VCdHQ0FoMr07ysLD4bNYpDW7Zw7fr1xHXoQHCDBuRmZLgtV3DiBCEREW7T/l61ik9HjqT9qFH0+V9PB6cffoD//hdCQ+Hmmw3dBamimsqY1E3KlxhJ+RKjKWNiJOUr8HlcYO/bt4/w8HAj2iKVFBQURHR0tKGvkfH336y49FIimzVj+ObN1I+NBSD2jDPY/X//57bs0d9/J/aMM1z/3/TEE3z//PMMWLiQ066/vsS2nVevr7kG/rdZ8TM1kTGpu5QvMZLyJUZTxsRIylfg83iQs4EDB7Jw4UL27dtnRHukEgoLCzlx4oRhowvmpqfzQb9+NDn3XIZ+/rmruAY4ZfBgclJT2TJ7NraCAvauW8e2d9/ljP9dit48cyabX3iBa9evdyuubTb46it45RV47z3HtLFjDWm+eIHRGZO6TfkSIylfYjRlTIykfAU+j69g7969m82bNzNgwADCwsJKfMJiMpn44osvvNZAKamgoICDBw+SnJxMaGio17f/6+uvk713L38sXcqOZcvc5k04fpxha9fy7wkT2Dh1KvXi4ug3dy7N+vZ1PAP78ccpyMnh/Z49XetYbbAx5CH+lfWQa1pwMOzfD2ef7fXmixcYnTGp25QvMZLyJUZTxsRIylfg87jATkxMZNCgQUa0RfxE10mT6DppUpnzE7p25fpij90Cx4crdxW7P3vlShg6FOy57ssWFMCwYbB8OQwe7I1Wi4iIiIiI+JbHBfYzzzzj1QZkZGQwc+ZMvvrqK44fP07btm2ZPHkyXbt2BWDbtm089dRT/PrrrzRq1IgRI0Zwyy23uNYvLCzkpZdeYtmyZWRlZXHWWWfx6KOP0rx5c6+2Uzxns8GECY5Rw8sycSJccYVGEhcRERERkcDn8T3Y3jZp0iR+/vlnZs6cyfLly2nfvj233HILf//9N+np6YwaNYoWLVqwYsUK7rrrLubMmcOKFStc68+bN4/333+fJ598kg8++ACTycRtt91Gfn6+D/dKADZscHQDL4vdDvv2OZYTEREREREJdB5fwW7Xrh0mk6ncZbZt21apbe3Zs4eNGzfy3nvvceaZZwLw8MMPs379elavXk1YWBghISFMmzaNoKAgWrduzZ49e3j11VcZMmQI+fn5LF68mHvvvZfevXsDMGvWLHr27MnatWsZOHCgp7sXEEwmE8HBwRW+D76WkuLd5aTmBErGJDApX2Ik5UuMpoyJkZSvwOdxgT1u3LgSb3hOTg7//e9/2bt3L/fcc0+ltxUVFcUrr7zCGUUe8WQymbDb7WRmZvLrr7/SrVs3goL+aWaPHj1YuHAhR48e5cCBA+Tk5NCjRw/X/MjISE4//XR++OGHWltgh4SEBEQX+MRE7y4nNSdQMiaBSfkSIylfYjRlTIykfAU+jwvsu+66q8x5999/P7/++itDhgyp1LYiIyNdV56dPv30U/bu3cv555/PrFmzOPXUU93mN27cGICDBw+SmpoKOAZeK75MSjUvi+bl5bn932w2ExwcTGFhIQUFBSWWd47yl5+fj73YTcdBQUFYLBZsNhtWq9VtnslkIiQkBLvdXmq39pCQEEwmEwUFBSWG67dYLAQFBZW73dL2BSA4OBiz2Vzudiva1/K2a7Va6d7dRtOmIRw8CHZ7yU/hTCY7TZtC9+755OVV7hharVZsNpvbPOd7U9ExrOp7U9G+enoMK7vd8va1Ojn0xTEMxHyXtq9Gvze17RjWlXxXtK86R9TOfOscoXNE0e3qHKFzhJPOEe77WlvOEZ7wuMAuz5VXXsnEiRN59NFHq7T+li1beOihh7jgggvo168fzzzzjOuNcyp6wE+ePAlQ6jKZmZlVagM4Bk4r/pzviIgI4uPjsdlspT4DvE2bNgAcPnyY3Fz3IbPj4+OJiIjg+PHjHDlyxG1e/fr1adKkCXa7vdTttmzZEovFQlpaGjk5Oa725ebmkpiYSFxcHCdPnnR92OAUGhpKcnIyAPv37y8RombNmhESEkJ6ejpZWVlu86KiooiJiSEvL48DBw64zQsKCqJFixYApKSklPhha9q0KfXq1SMzM5P09HQefLABd96ZUGK/TCZHex54IJWDB3MwmUy0bt0agEOHDpX4gUpISCA8PJzjx4+TlpbmNq9BgwYkJiaW+r4BtGrVCpPJRFpaGidOnHCbFxcXR8OGDTlx4gSHDh1ymxcWFkZSUhJAqdtt3rw5ZrOZY8eOkZ2d7TYvOjqa6OhocnNzOXjwoNu84OBg1yeTBw8eLPFDnJSURFhYGBkZGWQUG5W9YcOGxMXFUVBQUKJNZrOZVq1aAZCamlriJJqYmEiDBg3Izs7m6NGjbvPCw8NJSEhw5duZsbCwMMxms+u9OXLkiOvnzqlx48ZERkaSk5PD4cOH3ebVq1ePpk2blnkMW7RoQVBQEEePHuX48eNu82JiYoiKiiI3N7fEB2YhISE0a9YMgAMHDpQ4cTsfbZGRkVHiXNCoUSNiY2PJz89nf7FBAiwWCy1btgQc+S5+cm7SpAn169cnKyuLY8eOuc3zp3OEU2xsLI0aNfK7c4TZbGbXrl2YzWa3X1qRkZE0btwYq9VaYl91jviHP5wjivOnc0RhYSH5+fmccsophIaG6hwRgOeIon9HFOUv54iivyPj4+N1jiCwzhHg339HnDhxgp07d7r+BnPSOcLBV+cIT5jsxY9GNbz33nvMmDGDLVu2eLzuF198wT333EOnTp1YuHAhYWFhDBo0iF69enHvvfe6lvvrr78YOHAg//rXv9i3bx/jx4/n559/JiwszLXMhAkTyM/PZ/78+R63Y+vWrQAlrpz706dKeXl5pKSkkJycTP369f3+U6Vzzgnixx/dhwlPSrIzY4aVK6/857X99VPTiva1Nn7y7MxYYmIioaGh+uS52Hb1yXP1zhEFBQXs2bOHhIQEt2d8BuqVlfL2tbaeI4rzp3NEXl4eqampNG/enNDQUJ0jAvAc4e/5Lvo7skGDBjpHEFjniKLbBf/7O+LkyZPs3bvX9TdYZbZbW4+hP+V7+/btAHTo0KHEdovz+Ar2Sy+9VGJaYWEhKSkprFmzhn79+nm6Sd555x2eeuop+vfvz4wZM1xvVkJCQolPspz/j4+Pd73Rhw8fdn0K5fx/u3btPG5HUWU92N1sNpf70PfiV9OLslgsWMp4HpXJZCp3u8HBwWVuq7ztQtn7Utp2i6poX8ubFxQURFBQEHv3ws8/O6a99RYEBTnuue7Z04TFUvprl3cMndstTUXHsKrvDfj2GFZlu944hhaLhdDQULfXqal8V3a7YNx7U9Vj6C/niMpuF3yTb+e6pS1TE/n2dLv+eAx9fY7wdLs1me+iV310jnAItHOEv+fb+TsyEP4Wq8p2a/s5oih/PEeU9jdYZbZb246hv+W7srxSYIOja0j//v158MEHPdrekiVLeOKJJxgxYgQPPfSQ2y/Fbt268f7772Oz2Vxv6qZNm2jZsiUxMTFEREQQHh7Od9995yqws7Ky+P333xk+fLinuyYGWLwYCguhb18YMcLXrRERERERETGOxwW28/K4N+zatYunn36a/v37c/vtt7vdzxEWFsaQIUN47bXXePjhh7n11lv55ZdfePPNN3nssccAx6cPw4cPZ8aMGURHR9O0aVOmT59OQkIC/fv391o7pWqsVnjtNcf3o0f7ti0iIiIiIiJG8/ge7Jdeeolhw4YRHx9fYt7+/ftZvHgxU6dOrdS2FixYwKxZs0qdd9VVV/Hss8/yyy+/8NRTT/H7778TFxfHzTff7HZ12mazMXPmTFauXElubi7dunVj6tSprkElPOW8B7sy/et9xTmAS0hIiMej2tWkVavg8sshJgYOHIByemqInwmUjElgUr7ESMqXGE0ZEyMpX/7JkxrR4wL7tNNO44MPPqBjx44l5n3yySc88MADrgYEokAosAPFoEGwejVMngwzZvi6NSIiIiIiIp7zpEasVBfxa6+9lp//N1KV3W7nmmuuKXNZFabGs1qtZGRk0KhRo2rfhG+UfftgzRrH9+oeHngCIWMSuJQvMZLyJUZTxsRIylfgq9S79tRTT/Hpp59it9t5+eWXGTJkCAkJ7s82NpvNREZGMmDAAEMaKv+w2WxkZGQQERHhtz94zsHN+vSBYk88kwAQCBmTwKV8iZGULzGaMiZGUr4CX6XetdatW3PnnXcCjiHey7oHWwTAZtPgZiIiIiIiUvd4/LGIs9BOS0ujoKDA9ZDuwsJCTp48yebNm7nuuuu820oJKJ99Bvv3OwY3u+oqX7dGRERERESkZlTpMV2TJk1i165dpc43mUwqsOu4V15xfB05EsLCfNsWERERERGRmuJxgf3888+TlZXF/fffz7p16wgJCaFv376sX7+e9evX89ZbbxnRTinCbDbTsGFDvxy6/8ABx8jhALfd5tu2SNX5c8Yk8ClfYiTlS4ymjImRlK/A5/E79/PPPzNhwgRuuukmBg4cyIkTJ7j++utZsGABF154IW+//bYR7ZQigoODiYuLIzg42NdNKcE5uFmvXtCuna9bI1XlzxmTwKd8iZGULzGaMiZGUr4Cn8cFdn5+Pi1btgSgVatW/PHHH655gwcP5qeffvJa46R0hYWF5OXlUVhY6OumuNHgZrWHv2ZMagflS4ykfInRlDExkvIV+DwusJs0acK+ffsAaN68OcePH2f//v0AhISEkJmZ6d0WSgkFBQXs27ePgoICXzfFzf/9H+zdC1FRMGSIr1sj1eGvGZPaQfkSIylfYjRlTIykfAU+jwvsAQMGMGPGDD777DPi4uJo1aoVs2bN4o8//mDx4sUkJycb0U4JABrcTERERERE6jKPC+w777yTs846ixUrVgDw4IMP8sUXX3DllVfy7bffctddd3m9keL/Dh6EVasc32twMxERERERqYs8HkU8NDSUuXPnurot9OzZk9WrV/Prr7/Svn17mjVr5vVGiv97/XXHPdjnnw+nn+7r1oiIiIiIiNQ8jwtsJ4vFwvbt2zl8+DBnnnkm55xzDo0aNfJi06Q8/jR0f2EhvPqq43sNblZ7+FPGpPZRvsRIypcYTRkTIylfgc1kt9vtnq700Ucf8cILL3D48GHMZjPLli3jxRdfJDg4mBdeeIGQkBAj2lojtm7dCkCHDh183JLA8fnncPHF0KiRo6t4vXq+bpGIiIiIiIh3eFIjevzxyJo1a7j//vvp0aMHs2bNcg0hP2DAANavX8+8efM83aQEOOfgZjfeqOJaRERERETqLo8L7AULFnDttdfy/PPPM2DAANf0wYMHc+edd/LJJ594tYFSUn5+Pnv37iU/P9/XTSElBT76yPG9BjerPfwpY1L7KF9iJOVLjKaMiZGUr8DncYG9a9cu+vfvX+q8Tp06cejQoWo3Sspnt9vJz8+nCr37vc45uNm558IZZ/i6NeIt/pQxqX2ULzGS8iVGU8bESMpX4PN4kLOYmBj+/vtvzjvvvBLz/v77b2JiYrzSMPGeE0eOsOSccxjw2ms069OHtXfcwe/vvOO2jPXkSZpfeCFDP/+c19u3J2vPHrf5BTk59Hz6ac5+8EHXtKKDm91+u+G7ISIiIiIi4tc8LrAvvfRS5s6dS+PGjenduzcAJpOJX3/9lXnz5nHZZZd5vZFSdQc2buTTkSPJ+Ptv17T+CxbQf8EC1/93/9//sfq66+gzcyYAo377zW0b/5kyhZ2rV9Ol2DPOv/gCdu92DG42bJhhuyAiIiIiIhIQPC6wJ06cyI4dO5g4caJrCPkRI0Zw4sQJunbtyoQJE7zeSKmaX998k41Tp9L7+edZfe21pS5zIi2NT264gX5z5xLbvn2J+XvXrWPLrFnc+OOPhISHu81zDm42YoQGNxMREREREanSY7oANm7cyLfffktGRgYRERF0796d3r17YzKZvN3GGhUIj+my2Wzk5uYSFhaGxWIpc7mc1FTqxcZiDgpihsnE1evW0axPH7dlPrvlFo4fPMjQTz8tsX6hzcbrp5/OGTffzNn33+82LzUVkpPBaoVffgE/PlxSBZXNmEhVKF9iJOVLjKaMiZGUL//kSY3o8RVsp/POO6/U+7DFeBaLhQYNGlS4XIOEhHLnZ+zaxe9vv83w778vdf62JUsoOH6cM8ePLzHvjTccxfU556i4ro0qmzGRqlC+xEjKlxhNGRMjKV+Br1KjiJ9xxhn88ssvbtMOHTqEzWYzpFFSPqvVSnp6OlartVrb+XXxYpqedx6NO3cudf4vr7xCx9GjCS7W/7vo4GajR1erCeKnvJUxkdIoX2Ik5UuMpoyJkZSvwFepArv4G2yz2ejTpw/bt283pFFSPpvNxtGjR6v9AceOFSs4fcSIUuflHDrEgY0bS53/73/Dzp3QsCFcfXW1miB+ylsZEymN8iVGUr7EaMqYGEn5CnxV7iKuZ7MFtpNHj3Js2zaSevUqdf6BjRsJb9KERq1auabZbLBhAzzyiOP/118P9evXRGtFRERERET8X6WuYEvtk7lrFwDhTZuWPn/nTrd5K1dCixbQty98990/01auNLqlIiIiIiIigUEFdh1xj93uNoJ4Qteu3GO3l7i/2qnbPfcw/H+V9MqVMHQo7N/vvszhw47pKrJFRERERERUYAcks9lMeHi46znkRrLZYMIEKO2OAOe0iRMdy0ntUZMZk7pH+RIjKV9iNGVMjKR8Bb5K34O9fPly1q9fDzjuvzaZTHzwwQc0btzYbTmTycS4ceO820pxExwcTEIFj+Dylg0bSl65Lspuh337HMsVe8S2BLCazJjUPcqXGEn5EqMpY2Ik5SvwVbrAXrp0aaWmqcA2nt1ux2azYbFYMJlMhr5WSop3l5PAUJMZk7pH+RIjKV9iNGVMjKR8Bb5KFdh6HJd/yc/PZ9++fSQnJxMaGmroayUmenc5CQw1mTGpe5QvMZLyJUZTxsRIylfgU+d+KVfPnpCUBGV9gGYyQXKyYzkREREREZG6TAW2lMtigTlzSp/nLLpnz3YsJyIiIiIiUpepwJYKDR7sGEm8uKQkWL7cMV9ERERERKSuq/QgZ1K3FRQ4vg4Z4viXmOjoFq4r1yIiIiIiIg4mu720Jxy7++ijj+jVqxdRUVE10Saf2rp1KwAdOnTwcUvKVvQtq6nRBc88E378EZYuhWHDauQlxYd8kTGpO5QvMZLyJUZTxsRIypd/8qRGrFQX8WnTprFr1y4ALrjgAo0q7mMmk8n1ryYcPw4//+z4/pxzauQlxcdqOmNStyhfYiTlS4ymjImRlK/AV6ku4iEhIXz00UdYrVYOHDjATz/9RHZ2dpnLd+vWzWsNlJLy8/M5cuQIcXFxhISEGP56338PhYXQrJnjvmup/Wo6Y1K3KF9iJOVLjKaMiZGUr8BXqQJ72LBhvPbaayxduhSTycRjjz1W6nJ2ux2TycS2bdu82khxZ7fbOXnyJJXo3e8V33zj+HruuTXycuIHajpjUrcoX2Ik5UuMpoyJkZSvwFepAvuee+7hiiuuID09nRtvvJGpU6fSpk0bo9smfkIFtoiIiIiISMUqPYr4KaecAsCdd97JBRdcQHx8vGGNEv9RWAibNjm+V4EtIiIiIiJSNo8f03XnnXeSn5/P+++/z3fffUdWVhZRUVF07dqVq666itDQUCPaKT6yfTtkZED9+tCxo69bIyIiIiIi4r88LrCzsrK48cYb2b59O02aNCEuLo5du3axevVq3n33XZYsWUJERIQRbZX/CQoKonHjxgQFGf8Yc2f38O7dITjY8JcTP1GTGZO6R/kSIylfYjRlTIykfAU+j9+5F154gdTUVN555x26du3qmr5582bGjx/PnDlzeOSRR7zaSHFnsViIjIyskdfS/dd1U01mTOoe5UuMpHyJ0ZQxMZLyFfgq9Rzsor788ksmTpzoVlwDdO3alfHjx/N///d/XmuclM5ms5GVlYXNZjP8tVRg1001mTGpe5QvMZLyJUZTxsRIylfg87jAzsnJITk5udR5ycnJZGRkVLdNUgGr1crhw4exWq2Gvk5aGvzxh+P7Hj0MfSnxMzWVMamblC8xkvIlRlPGxEjKV+DzuMBu1aoV69atK3Xel19+SfPmzavdKPEP337r+NquHcTE+LYtIiIiIiIi/s7je7BvueUWJk2aRH5+PoMGDSI2Npa0tDRWrVrFsmXLmDZtmgHNFF9Q93AREREREZHK87jAvvTSS9m9ezcLFixg2bJlANjtdkJCQhg3bhzXXHON1xspvqECW0REREREpPKqNP772LFjGT58OD/99BOZmZk0bNiQTp060bBhQ2+3T0phMpmoV68eJpPJsNcoKIDvv3d8rwK77qmJjEndpXyJkZQvMZoyJkZSvgKfyW63233dCH+ydetWADp06ODjlvjW5s3QrRtERTkGOzN7fLe+iIiIiIhI4POkRtQTzANQ0c9EjPp0y9k9/JxzVFzXRTWRMam7lC8xkvIlRlPGxEjKV+BT6RSA8vPz+fvvv8nPzzfsNXT/dd1WExmTukv5EiMpX2I0ZUyMpHwFPhXYUioV2CIiIiIiIp7xuMBesGABf/75pxFtET+xb5/jn8XiuA9bREREREREKuZxgf3aa6+RkpJiRFvET2za5PjaqROEh/u2LSIiIiIiIoHC4wK7RYsWuoJdy6l7uIiIiIiIiOc8HkW8T58+zJo1i3Xr1nHKKacQExPjNt9kMjFu3DivNVBKCgkJoUWLFlgsFkO2rwJbjM6Y1G3KlxhJ+RKjKWNiJOUr8Hn8HOx27dqVv0GTiW3btlWrUb5U15+DfeIENGwIVivs3g3Nm/u6RSIiIiIiIr5j6HOwt2/f7nmLxKsKCgo4evQoMTExBAcHe3Xbmzc7iusmTaBZM69uWgKIkRkTUb7ESMqXGE0ZEyMpX4GvWo/pys7Odj2nzWazeatNUoHCwkKOHz9OYWGh17ddtHu4nm1fdxmZMRHlS4ykfInRlDExkvIV+KpUYH/33XcMGzaM7t27M2jQIP78808mT57Ms88+6+32SQ3T/dciIiIiIiJV43GBvWnTJm655RbCwsK45557cN7Cffrpp/PWW2/x+uuve72RUjPs9n8e0aUCW0RERERExDMeF9izZ8/mggsu4O2332bkyJGuAnv06NHceuutLFu2zOuNlJrx11+QlgahodCli69bIyIiIiIiElg8LrC3bdvGkCFDAMeI4UWdd955HDhwoMqNmTdvHiNGjHCb9uCDD9K2bVu3f7169XLNLywsZO7cufTs2ZNOnTpx8803s2fPniq3IRBYLBZiYmK8Pny/s3t4t24QEuLVTUuAMSpjIqB8ibGULzGaMiZGUr4Cn8ejiEdERHDkyJFS56WkpBAREVGlhrzxxhvMnTuXbt26uU3/448/uOOOOxg+fLhrWtHAzZs3j/fff59nnnmG+Ph4pk+fzm233cbq1asJqaVVYlBQEFFRUV7fru6/FiejMiYCypcYS/kSoyljYiTlK/B5fAX7ggsuYNasWa5ngYHjSnZqaioLFiygT58+Hm3v0KFD3HrrrcyZM4eWLVu6zbPZbPz111906NCBuLg417/o6GgA8vPzWbx4MXfddRe9e/emXbt2zJo1i0OHDrF27VpPdy1g2Gw2cnJyvD5yuwpscTIqYyKgfImxlC8xmjImRlK+Ap/HBfbkyZOJiYnh6quvdhXTkyZN4uKLL8ZkMjFp0iSPtvfbb7/RsGFDPv74Yzp16uQ2b/fu3eTl5dG6detS192+fTs5OTn06NHDNS0yMpLTTz+dH374wbMdCyBWq5WUlBSsVqvXtpmRAb/95vj+nHO8tlkJUEZkTMRJ+RIjKV9iNGVMjKR8BT6Pu4g3bNiQZcuW8eGHH/Ltt9+SkZFBREQEI0aMYPDgwdSrV8+j7fXr149+/fqVOm/Hjh2YTCbefPNN1q9fj9lspnfv3kycOJGIiAhSU1MBSExMdFuvcePGpKSkeLprddp33zlGEW/TBho39nVrREREREREAo/HBTZASEgIV199NVdffbW32+Pmzz//xGw207RpUxYsWMCePXt47rnn2LFjB2+++SYnT550taeo0NBQMjMzq/XaeXl5bv83m80EBwdTWFhIQUFBieVDQ0MBR7d158jqTkFBQVgsFmw2W4lPo0wmEyEhIdjtdvLz80tsNyQkBJPJREFBgeuB83l5edhsNlfXkfK2W9q+AAQHB2M2m13bXb/eAgRx9tk2rFY7QUFBFe5redu1Wq0lurZ44xiWt92KjmFV35uK9rXoe+NksVjKPIaV3a4vj6EzY872VfcYepJvJ+cxrMn3Bso/hka/N7XtGJa1XXAMUGnEeVbniMA8ht7Md15entuyOkcE3jnC3/Nd9HekzhHu2w2Ec0TR7YJ/niOK/g1Wme3W1mPoT/n2RJUK7L///pv58+ezadMmMjMziYmJoUePHowZM4YWLVpUZZOluuuuu7jpppuIjIwE4NRTTyUuLo5rrrmGrVu3EhYWBjgOkvN7cLwZnl5JL6qwsJB9+/a5TYuIiCA+Ph6bzVZiHkCbNm0AOHz4MLm5uW7z4uPjiYiI4Pjx4yUGiKtfvz5NmjTBbreXut2WLVtisVhIS0sjJyfH1b7c3FxycnKoX78+J0+edF3NdwoNDSU5ORmA/fv3lwhRs2bNCAkJIT09naysLL76qgkQRNu2R8nMdIxemJeXV2JU+KCgINd7XFr3laZNm1KvXj0yMzNJT093mxcZGUnjxo2xWq0l9tVkMrluBTh06FCJH6iEhATCw8M5fvw4aWlpbvMaNGhAYmJiqe8bQKtWrTCZTKSlpXHixAm3eXFxcTRs2JATJ05w6NAht3lhYWEkJSUBlLrd5s2bYzabOXbsGNnZ2W7zoqOjiY6OJjc3l4MHD7rNCw4Opnnz5gAcPHiwxA9xUlISYWFhZGRkkJGR4TavYcOGxMXFUVBQUKJNZrOZVq1aAZCamlriJJqYmEiDBg3Izs7m6NGjbvPCw8NJSEhw5duZsdTUVMxms+u9OXLkiOuDLafGjRsTGRlJTk4Ohw8fdptXr149mjZtWuYxbNGiBUFBQRw9epTjx4+7zYuJiSEqKorc3NwSPVJCQkJo1qwZAAcOHChx4k5OTiY0NJSMjIwSH7Y1atSI2NhY8vPz2b9/v9s8i8XiGgsiJSWlxMm5SZMm1K9fn6ysLI4dO+Y2z5/OEU6xsbE0atTIK+eIoqKioqp1jnD+YnTmy0nnCIdAOEcU50/niOIf3ugcEXjnCH//O6Lo78j4+HidIwiscwT4/98RRf8Gc9I5wsFX5whPmOzFj0YFNm3axG233UZUVBS9evUiJiaGI0eO8PXXX5OXl8e7775Lu3btPGqE0wMPPMCBAwd4++23y1zmxIkTdOnShblz55KYmMiwYcNYu3at64cE4LrrrqNdu3Y8+uijHrfBOXjbqaee6jbdnz55zs/PJy0tjYSEBOrVq1ftT5UKCgqJjw/h+HETmzfn06mTOeA+VdLVKQdvHUNnxmJjYwkJCdEnz8W2q6tT1fvk2Wq1cvDgQWJiYtx6IOkc4dl2a9N51pv5dp6/mjZtSkhIiM4RAXiO8Pd8F/0dWb9+fZ0jCKxzRNHtgv/9HeH8QMD5N1hltltbj6E/5Xv79u0AdOjQocR2i/O4wL766qtp0KABCxcudHvTc3JyuPXWWwkODuatt97yZJMuxQvsyZMnk5GRwaJFi1zLfPfdd9x4442sWbOG5ORkzjnnHB544AGGDRsGQFZWFj179uTpp59m4MCBHrfBWWBX5uDVFj//DJ07Q2QkHDsGeuyeiIiIiIiIgyc1osejiG/fvp2bbrqpxH3PDRo0YPTo0fz888+ebrJMl112GRs3bmT+/Pns3buXr7/+moceeojLLruM1q1bExISwvDhw5kxYwZffvkl27dv5+677yYhIYH+/ft7rR21nfPxXD16qLgWERERERGpKo/vwU5MTCxxL4hTTk4OsbGx1W6UU9++fZkzZw4LFixgwYIFREREMGjQICZOnOhaZvz48VitVh555BFyc3Pp1q0bixYtKvEBQG3ivF+hadOmrq4O1aHnX0tx3s6YSFHKlxhJ+RKjKWNiJOUr8HlcYN977708/PDDNGrUiAEDBmD53yXP7777jpkzZ3LvvfdWuTHPPvtsiWkXXXQRF110UZnrWCwW7r333mq9biAqfi9DdajAltJ4M2MixSlfYiTlS4ymjImRlK/AVqkCu127dphMJtf/7XY7kyZNwmKx0KhRI7Kzs8nPz8disfDUU09xySWXGNZg8a7UVNi5E0wmOPtsX7dGREREREQkcFWqwB43bpxbgS21x6ZNjq9nnOEY5ExERERERESqplIF9l133WV0O8RH1D1cRERERETEOzy+Bxsczw7buXMn2dnZpc7v1q1btRol5QsODiY5OZng4OBqb0sFtpTGmxkTKU75EiMpX2I0ZUyMpHwFPo8L7E2bNjF58mTS09MBXA/pNplM2O12TCYT27Zt824rxY3ZbPbKqIJ5ebB5s+N7FdhSlLcyJlIa5UuMpHyJ0ZQxMZLyFfg8LrCffvppoqKimDZtGo0aNTKgSVKRgoICMjIyaNSoUbU+3frvfyE/H+LioHVrLzZQAp63MiZSGuVLjKR8idGUMTGS8hX4PC6w9+7dy6xZs+jXr58R7ZFKKCwsJDMzk8hqjkpWtHu4xrCToryVMZHSKF9iJOVLjKaMiZGUr8Bn9nSFtm3burqHS2DT/dci8v/t3Xl4VOX9///XbEkICQmQQAJhR8AFECSyuLAoV/2VuvNRUVyoa6uCoixWVGhRv66s4gKoqKVqFal6Ka1KtW5VwLobLC5IQsKWkBBIZv/9ETJlMiFkwtyZOeH5uK5cSc59cuaec165k/dZAQAAEDtRF9h/+MMf9Oijj+qVV17RDz/8oK1bt0Z8IPb27dihZb1765d33w1N+/Kxx7TsqKO0IC1Ny446Sp8tXhxq81VX650bb9SSnBwtSE/Xn4cN0y9r14bag0EKbAAAAACIpSbfRfwPf/jDQdu5yVlsFX34od68/HLt/uGH0LTid97R53Pm6P/eeks5J5yg4nXr9MKppyrr2GPVdfRovX/77Sr+5BNd/vnnSu3QQZ8/9pheOess/a6kRElpafr5Z6mkRHK5pBNOiN97AwAAAICWIuoCe/bs2XI4HLr55puVnZ1tok84wNcrVujDO+/UyPvv1+sXXSRJcjgcOvr883XCBRcotW1bBXw+Ve3cKdlsSt5/47mR998vv9stV2qqPHv3qnrXLiVnZsq+/2YJtUevBw+WWrWKxztDInM4HMrMzJTD4Yh3V9ACkS+YRL5gGhmDSeTL+qIusH/88UctWLBAo0ePNtEf1NHjV7/SMZdcIrvTGSqwnU6nsrKyJEmlGzfqqWOPVdDv1wlTp6rjoEGSJLvDIXtqqr544gm9dd11crhc+vWf/yzn/tv+c3o4GnJgxoBYI18wiXzBNDIGk8iX9UV9DXa3bt1UVVVloi+oR+ucHNmd4ftBAoGAqqurFQgElNGzp26qqtLEdeu08fnn9cl994XNe+xll+lmt1v/34oVeuOSS1T04YeSKLDRsAMzBsQa+YJJ5AumkTGYRL6sL+oCe8qUKZo3b54+/PBD7d2710SfcAher1eFhYXyer1yuFxyuFzKGTJEg6dMUcHKlWHzOlNS5HC51O+ii9T1tNO08cUXtWeP9OWXNe0U2KjPgRkDYo18wSTyBdPIGEwiX9YX9SniDz30kHbu3Kmrrrqq3nabzaZvv/32sDuGhm166il9/f33Ouevfw1N87vdSmnXTpL02oUXKnfYMA25+eaI9k8/lQIBqVs3qVOnZu86AAAAALRIURfY48aNM9EPRCkrP1//evBBFbz4ovqOH6+tH3+szxYs0GlLlkiSOo0YoU/uvVfdTj9d7Y8+Wl8//bRK1q3TaY8+oWn/r2YZvXpJfr/EPRQAAAAA4PBFXWDfcMMNJvqBKGUed5x+/Ze/6JM5c/SPq65Sm27dNHrBAvW74AJJ0uDJk+WrqtIrZ54pd3m5sgcOVNasd3T8ab1UWFizjLVrpe7dpQULpPPOi997AQAAAICWIOoCe+vWrYecpxPnHRtxazAoSXK73XI4HOoybpz6HaQyttlsGjpzpobOnClJWrVKGj9e2r+IkKKimukvvUSRjXA8HgImkS+YRL5gGhmDSeTL2mzBYN2Sq2H9+vWTzWZrcJ7vvvvusDoVT1999ZUkqX///nHuSez4/TVHqmuPXNdls0l5edJPP3G6OAAAAAAcKJoaMeoj2Pfcc09Egb1v3z5t2LBB//73v3XPPfdEu0gY9v77By+upZqj2lu21Mw3alSzdQsAAAAAWpSoC+zzDnIe8SWXXKL77rtPr732mkZRpRnl8XhUXFys3NxcJSUlHXL+4uLGLbex86HlizZjQDTIF0wiXzCNjMEk8mV9UT8HuyGjRo3Su+++G8tFoh7BYFBer1eNPbs/N7dxy23sfGj5os0YEA3yBZPIF0wjYzCJfFlfTAvszz//XE5n1AfFYdgpp9RcY32wS+dtNqlLl5r5AAAAAABNE3U1fNttt0VMCwQCKi4u1vr16zV+/PiYdAyx43DUPIqrvk1TW3TPn88NzgAAAADgcERdYH/yyScR02w2m9LS0nT11Vfruuuui0nHEFvnnSe98IK0/zHZIXl5NcU1j+gCAAAAgMMTdYG9du1aE/1AFFwulzp16iSXyxXVzw0bVvPZ4ZBWrJA6d645LZwj16irqRkDGoN8wSTyBdPIGEwiX9bHBdMWZLfblZqaGvXP/fJLzeeuXaVLLolxp9CiNDVjQGOQL5hEvmAaGYNJ5Mv6GlVg13fd9cHYbDaehW2Yz+dTRUWF2rRpE9VN5Q4ssIGGNDVjQGOQL5hEvmAaGYNJ5Mv6GrXV6rvuuq6ysjJVVVVRYDcDv9+v0tJStW7dmgIbRjQ1Y0BjkC+YRL5gGhmDSeTL+hq11Rq67trr9erRRx/VE088oaysLM2ePTtWfUOMUWADAAAAgDmHtVvku+++08yZM/X9999r3LhxuuOOO5SRkRGrviHGKLABAAAAwJwmFdg+n0+PPPKIli5dqszMTC1evFinnXZarPuGGKPABgAAAABzoi6wv/32W912223auHGjzjrrLM2aNUtt2rQx0TcchN1uV3p6uux2e1Q/R4GNxmpqxoDGIF8wiXzBNDIGk8iX9TW6wPb5fFq8eLGWLVumdu3a6dFHH9Xo0aNN9g0H4XK51LFjx6h+pqJC2r275usuXWLfJ7QsTckY0FjkCyaRL5hGxmAS+bK+RhXY33zzjWbOnKlNmzbpnHPO0e233660tDTTfcNBBAIB+f1+ORyORu/d2rKl5nPbtlJ6usHOoUVoSsaAxiJfMIl8wTQyBpPIl/U1qsC+4IILFAgElJ6erqKiIv3+978/6Lw2m00rVqyIWQcRyev1asuWLerSpYuSk5Mb9TOcHo5oNCVjQGORL5hEvmAaGYNJ5Mv6GlVgDx48OPR1MBhscN5DtSM+KLABAAAAwKxGFdjPPvus6X7AMApsAAAAADCLE/uPEBTYAAAAAGAWBfYRggIbAAAAAMyK+jnYiL/k5GT17t07qp+hwEY0mpIxoLHIF0wiXzCNjMEk8mV9HME+Avj9UmFhzdcU2AAAAABgBgW2BXk8HhUWFsrj8TRq/pISyeeTHA4pN9dw59AiRJsxIBrkCyaRL5hGxmAS+bI+CmwLCgaDqq6ubvQj0WpPD8/LqymygUOJNmNANMgXTCJfMI2MwSTyZX0U2EcArr8GAAAAAPMosI8AFNgAAAAAYB4F9hGAAhsAAAAAzKPAtiCn06mOHTvK6WzcU9YosBGtaDMGRIN8wSTyBdPIGEwiX9bHlrMgh8Oh9PT0Rs9PgY1oRZsxIBrkCyaRL5hGxmAS+bI+jmBbkN/vV3l5ufx+f6Pmp8BGtKLNGBAN8gWTyBdMI2MwiXxZHwW2Bfl8Pu3YsUM+n++Q81ZWSqWlNV9TYKOxoskYEC3yBZPIF0wjYzCJfFkfBXYLt2VLzeeMDKlNm/j2BQAAAABaMgrsFo7TwwEAAACgeVBgt3AU2AAAAADQPCiwLchmsyk1NVU2m+2Q81JgoymiyRgQLfIFk8gXTCNjMIl8WR+P6bKgpKQkderUqVHzUmCjKaLJGBAt8gWTyBdMI2MwiXxZH0ewLSgYDCoQCCgYDB5yXgpsNEU0GQOiRb5gEvmCaWQMJpEv66PAtiCPx6Mff/xRHo/nkPNSYKMposkYEC3yBZPIF0wjYzCJfFkfBXYLFgj87zFdFNgAAAAAYBYFdgtWUiJ5vZLdLnEpBwAAAACYRYHdgtWeHt65s+TkdnYAAAAAYBQFdgtWW2B36xbffgAAAADAkYDjmhaUlJSkHj16yG5veP8INzhDUzU2Y0BTkC+YRL5gGhmDSeTL+iiwLchms8nhcBxyPgpsNFVjMwY0BfmCSeQLppExmES+rI9dIxbk9XpVXFwsr9fb4HwU2GiqxmYMaAryBZPIF0wjYzCJfFlfQhXYS5Ys0aWXXho27bvvvtPEiRN1/PHHa9SoUVq+fHlYeyAQ0MKFC3XKKado4MCB+u1vf6vNmzc3Z7ebXSAQ0N69exUIBBqcjwIbTdXYjAFNQb5gEvmCaWQMJpEv60uYAvvpp5/WwoULw6aVlZVp0qRJ6t69u15++WXdeOONWrBggV5++eXQPEuWLNHzzz+vuXPn6oUXXpDNZtPVV1/Nw9lFgQ0AAAAAzSnu12Bv27ZNt99+uzZs2KAePXqEtb344otKSkrS7Nmz5XQ61atXL23evFlLly7V+eefL4/HoyeffFLTpk3TyJEjJUnz5s3TKaecorfeekvjxo2Lx1tKCHv3Srt21XxNgQ0AAAAA5sX9CPY333yjjIwMvfrqqxo4cGBY2/r165Wfny/nAQ9xHjZsmH766Sft2rVLBQUF2rt3r4YNGxZqb9OmjY455hitW7eu2d5DItqypeZzmzZSRkZ8+wIAAAAAR4K4H8EeM2aMxowZU29bSUmJ+vTpEzatQ4cOkqStW7eqpKREkpSbmxsxT3Fx8WH1y+12h31vt9vlcrkUCATqvelAcnKyJMnj8SgYDIa1OZ1OORwO+f1++Xy+sDabzaakpCQFg8F6T2tPSkqSzWaT1+sNXYvh8/mUcUDVXN9yf/jBLsmlrl0j34skuVwu2e32sOXWcjgccjqdh3yvDS3X5/PJ7/eHtcViHTa03EOtw6Zum0O912jXYWOXG891WJux2nVyuOswmnzXql2HzbltpIbXoelt09LW4cGW63A41LZt24g+MUZEt9yWNM7GMt8+n0+ZmZmhu/AyRlhvjEj0fB/4N5IxIny5VhgjDlyulHhjhM1mC/sfrDHLbanrMJHyHY24F9gNqa6uDm24Wgeu8KqqKkmqd57y8vImv24gENCW2kPA+6Wnp6tjx47y+/0RbZLUu3dvSdL27dtVXV0d1taxY0elp6ersrJSO3bsCGtLTU1Vp06dFAwG611ujx495HA4tHPnTu3duzeszeVyKTk5WVVVVaGdDbW++aadpHbq2lUqLCyMCFHXrl2VlJSksrIyVVRUhLW1bdtW7du3l9vtVlFRUVib0+lU9+7dJUnFxcURv2ydO3dWq1atVF5errKysrC2Nm3aqEOHDvL5fBHv1WazqVevXpJqLhuo+wuVk5OjtLQ0VVZWaufOnWFtrVu3Vm5ubr3bTZJ69uwpm82mnTt3at++fWFt2dnZysjI0L59+7Rt27awtpSUFOXl5UlSvcvt1q2b7Ha7SktLtWfPnrC2du3aqV27dqqurtbWrVvD2lwul7p16yapZkdR3V/ivLw8paSkaPfu3dq9e3dYW0ZGhrKzs+X1eiP6ZLfb1bNnT0k1O6fqDqK5ublq3bq19uzZo1211w/sl5aWppycnIh81/4e1W6bHTt2hH7vanXo0EFt2rTR3r17tX379rC2Vq1aqXPnzpLqX4fdu3eX0+nUrl27VFlZGdbWvn17tW3bVtXV1RE7zJKSktR1/7UPRUVFEQN3ly5dlJycrN27d0eMBZmZmcrKypLH41FhYWFYm8PhCF2qUt8dPDt16qTU1FRVVFSotLQ0rC0Rx4isrCxlZmbWO0YkJyerS5cukuIzRtS2H4gxooaVxohaiThGZGVlSWKMsOoYYYX/I8rLyxkj9rPiGJGo/0fYbDaVl5dHvC5jRI14jRHRsAXrro04mjlzpoqKivTss89Kks4880ydeuqpmjZtWmieTZs2ady4cXrllVe0ZcsWTZ48WV988YVSUlJC80yZMkUej0ePPvpo1H346quvJCniyHki7Xn2+/2qrq5W69atlZycXO9y//hHp+65x6HrrpPmz2/5e5U4OlUjVuuwNmMpKSlyOBzsea6zXI5OHd4YEQwGVVlZGTqaXYsxIrrltqRxNpb59vv9crvdatOmjRwOB2OEBceIRM/3gX8jk5OTGSNkrTHiwOVKifd/hNfr1Z49e0L/gzVmuS11HSZSvgsKCiRJ/fv3j1huXQl9BDsnJydiT1bt9x07dgxt6O3bt4f2QtV+369fv8N67doNUJfdbj9omxR5NP1ADofjoA+Ot9lsDS7X5XKFvna73SotLQ3tTalvubU7g7p2Pfh7qbvcug71XhtqczqdYdfOR7PchtZhQ8s91Dps6raRjrx1WJux2j24jVlurPIdzXIlc9umqeswEcaIaJYrNX++3W63duzYEZGvxi6XMaJGSxpnY5lvt9utXbt2KTU1VQ6HgzFiPyuNEVJi5/vAv5G16411WMMKY0RdiTZGBAKBev8Ha8xyW9o6TLR8N1bcb3LWkPz8fG3YsCFsL8LHH3+sHj16qH379urXr5/S0tL0ySefhNorKir07bffasiQIfHocsLgEV0AAAAA0LwSusA+//zzVVlZqdtvv12bNm3SqlWrtGLFCl177bWSavY+TJw4UQ8++KDeeecdFRQU6Oabb1ZOTo7Gjh0b597HFwU2AAAAADSvhD5FvH379lq2bJnuvvtunXvuucrOztb06dN17rnnhuaZPHmyfD6fZs2aperqauXn52v58uUNHvpv6QKB/z2miwIbAAAAAJpHQt3kLBHU3uSsMRewx4vH49G2bdvUsWPHencklJRIubmS3S5VV0sNXPoA1OtQGQMOB/mCSeQLppExmES+ElM0NWJCH8FG/ZKSkkK3xa9P7enhnTpRXKNpDpUx4HCQL5hEvmAaGYNJ5Mv6EvoabDQN118DAAAAQPOjwLYgt9utH374od5nw0kU2Dh8h8oYcDjIF0wiXzCNjMEk8mV9FNgW1dCl8xTYiAVuzwCTyBdMIl8wjYzBJPJlbRTYLRAFNgAAAAA0PwrsFogCGwAAAACaHwV2C0SBDQAAAADNj8d0WZDL5VLXrl3ldEZuvqoqaceOmq8psNFUDWUMOFzkCyaRL5hGxmAS+bI+tpwF2e32gz54fsuWms9paVJmZvP1CS1LQxkDDhf5gknkC6aRMZhEvqyPU8QtyOv1avv27fJ6vRFtB54ebrM1c8fQYjSUMeBwkS+YRL5gGhmDSeTL+iiwLSgQCKiiokKBQCCijeuvEQsNZQw4XOQLJpEvmEbGYBL5sj4K7BaGAhsAAAAA4oMCu4WhwAYAAACA+KDAbmEosAEAAAAgPiiwLcjhcKht27ZyOBwRbRTYiIWGMgYcLvIFk8gXTCNjMIl8WR+P6bIgp9Op9u3bR0wPBimwERsHyxgQC+QLJpEvmEbGYBL5sj6OYFtQIBBQVVVVxN0Fd+yQ3O6ax3N17hynzqFFOFjGgFggXzCJfME0MgaTyJf1UWBbkNfrVVFRUcTz8WqPXufmSjyfHofjYBkDYoF8wSTyBdPIGEwiX9ZHgd2CcHo4AAAAAMQPBXYLQoENAAAAAPFDgd2CUGADAAAAQPxQYFuU0xl5A3gKbMRSfRkDYoV8wSTyBdPIGEwiX9bG1rOg5ORkde/ePWI6BTZi5WAZA2KBfMEk8gXTyBhMIl/WxxHsFoQCGwAAAADihwLbgtxut37++We53e7QtOpqadu2mq8psHG46ssYECvkCyaRL5hGxmAS+bI+CmyL8vl8Yd8XFtZ8Tk2V2rWLQ4fQ4tTNGBBL5AsmkS+YRsZgEvmyNgrsFuLA08Nttvj2BQAAAACORBTYLQTXXwMAAABAfFFgtxAU2AAAAAAQXxTYFuRyudS5c2e5XK7QNApsxFJ9GQNihXzBJPIF08gYTCJf1sdzsC3IbrerVatWYdMosBFL9WUMiBXyBZPIF0wjYzCJfFkfR7AtyOfzadeuXWF3GKTARizVlzEgVsgXTCJfMI2MwSTyZX0U2Bbk9/tVVlYmv98vSQoGKbARW3UzBsQS+YJJ5AumkTGYRL6sjwK7Bdi1S6qqqvk6Ly++fQEAAACAIxUFdgtQe/Q6J0dKTo5vXwAAAADgSEWB3QJwejgAAAAAxB8FtgXZ7Xa1adNGdnvN5qPARqzVzRgQS+QLJpEvmEbGYBL5sj4e02VBLpdLHTp0CH1PgY1Yq5sxIJbIF0wiXzCNjMEk8mV97BqxoEAgII/Ho0AgIIkCG7FXN2NALJEvmES+YBoZg0nky/oosC3I6/Xql19+kdfrlUSBjdirmzEglsgXTCJfMI2MwSTyZX0U2C0ABTYAAAAAxB8FtsW53VJxcc3XFNgAAAAAED8U2BZXVFTzOSVFysqKb18AAAAA4EhGgW1RNptNUvjp4fsnATFhI1AwiHzBJPIF08gYTCJf1sZjuiwoOTlZvXr1ksT11zDjwIwBsUa+YBL5gmlkDCaRL+vjCLbFbd5c85kCGwAAAADiiwLbgjwej7Zs2SKPx8MRbBhxYMaAWCNfMIl8wTQyBpPIl/VRYFtQMBiU2+1WMBikwIYRB2YMiDXyBZPIF0wjYzCJfFkfBbbFUWADAAAAQGKgwLawYJACGwAAAAASBQW2hZWWSvv21XydlxffvgAAAADAkY4C24KcTqdycnK0dWvNU9Y6dJBatYpzp9Ci1GbM6eRJfog98gWTyBdMI2MwiXxZH1vOghwOh9LS0lRUVPM9p4cj1mozBphAvmAS+YJpZAwmkS/r4wi2Bfl8Pu3evVs//eSXRIGN2KvNmM/ni3dX0AKRL5hEvmAaGYNJ5Mv6KLAtyO/3a+fOnfrll5rb91NgI9ZqM+b3++PdFbRA5AsmkS+YRsZgEvmyPgpsC9uyxSaJAhsAAAAAEgEFtoVRYAMAAABA4qDAtjAKbAAAAABIHBTYFmS32+VytVZxcc33FNiINbvdrtatW8tuZ4hA7JEvmES+YBoZg0nky/p4TJcFuVwuBQK5Cgal5GQpOzvePUJL43K5lJubG+9uoIUiXzCJfME0MgaTyJf1sWvEgoLBYOgRXV26SOzgQqwFg0H5/X4Fg8F4dwUtEPmCSeQLppExmES+rI/SLEEE/H49P2qU3rziitC0guef15NHH62FbdpoeZ8++vyxxyRJHo9Hn322U5LUrVs8eouWzuPx6KeffpLH44l3V9ACkS+YRL5gGhmDSeTL+jhFPEF8NGeOit5/Xxndu0uSdnz9tdZceaUueOcddRo2TEUffaQXR49W1rHHKvvEE7V1a82m4/prAAAAAEgMHMFOAL+sXav/vvyyjjr//NC0su+/V9DnUzAQUDAYlM1mk83hkCMlRZIosAEAAAAgwViiwC4qKlLfvn0jPv76179Kkr777jtNnDhRxx9/vEaNGqXly5fHuceNt3f7dq258kqNW7lSrtTU0PTuv/qVcocN019OOkkPu1xaOWKETvrTn5Sbny9JKi6mwAYAAACARGKJU8Q3btyo5ORkvf3227LZbKHp6enpKisr06RJk3T66adrzpw5+vzzzzVnzhxlZmbq/AOOCCeiYCCgNyZO1JCpU9Vh4MCwNr/brYwePTT8jjuUN3Kkfv7HP/T6hRcqu39/5Y4cyRFsAAAAAEgwliiwv//+e/Xo0UMdOnSIaFuxYoWSkpI0e/ZsOZ1O9erVS5s3b9bSpUsTvsD+5N575UxJ0eAbb4xo++iuu+RMSVG300+XJPUaN079JkzQF48/rq6nj1VJSc18FNgwISkpST179gzboQXECvmCSeQLppExmES+rM8SBfbGjRvVu3fvetvWr1+v/Px8OZ3/eyvDhg3T448/rl27dql9+/bN1c2offvss6rculWLMjMlSd59+yRJm1avVt7IkUpp1y5sfrvLJUdSksrLbaqsrJnWpUtz9hhHCpvNxsAOY8gXTCJfMI2MwSTyZX2WuAb7+++/165du3TxxRdrxIgRmjBhgt5//31JUklJiXJycsLmrz3SvXXr1mbvazR+W1CgyRUVunH3bt24e7eOvvhiHX3xxbpx9271PussbXzhBf30978rGAxqy3vv6bvnnlPfiy7Riy/6JEkZGUElJcX5TaBF8ng82rp1K4+IgBHkCyaRL5hGxmAS+bK+hD+C7fF49PPPP6tVq1aaPn26UlNT9eqrr+rqq6/WU089perqaiXVqTKTk5MlSW63u8mvW/dn7Xa7XC6XAoGAvF5vxPy1r+nxeCIeDO90OuVwOOT3++Xz+cLabDabkpKSQg+Vr33tPhMnqqqiQmsnT9be4mKld+mitMsXafT141RUVLNXq7zcpu7dpXnz/DrzzPqXe7D14HK5ZLfb5fV6FQgEwtocDoecTuch32tDy/X5fKH3UysW67Ch5QaDwXoHo6SkJNlstiZvm0O912jXYWOXG8916Ha7tWfPHqWlpSkYDB72OjzUtmloHTbntjnUOjS9bVraOjzYcoPBoCorK0P5qsUYEd1yW9I4G8t8u91uVVZWhs5gY4yw3hiR6Pk+8G+kw+FgjJC1xogDlysl3hjh9/vD/gdrzHJb6jpMpHxHI+EL7KSkJK1bt05OpzO0EY877jj98MMPWr58uVJSUiLCUrsxUg+4K3c0AoGAtmzZEjYtPT1dHTt2lN/vj2iTFDqFffv27aqurg5r69ixo9LT01VZWakdO3aEtaWmpqpTp04KBoPqd9ddkhRafrszz9QVkyfL4XBo+fIyXX11pupkQUVF0gUX2LVoUal+9au9oenJycnqsv/88cLCwogQde3aVUlJSSorK1NFRUVYW9u2bdW+fXu53W4VFRWFtTmdTnXf/6zu4uLiiF+2zp07q1WrViovL1dZWVlYW5s2bdShQwf5fL6IdWiz2dSrVy9J0rZt2yJ+oXJycpSWlqbKykrt3LkzrK1169bKzc2td7tJCl3HsnPnTu3bfxp+rezsbGVkZGjfvn3atm1bWFtKSory8vIkqd7lduvWTXa7XaWlpdqzZ09YW7t27dSuXTtVV1dHnEnhcrnUrVs3STVnWdT9Jc7Ly1NKSop2796t3bt3h7VlZGQoOztbXq83ok92u109e/aUVHNmR93fi9zcXLVu3Vp79uzRrl27wtrS0tKUk5MTyncgEFB1dbVKSkpkt9tD22bHjh2qqqoK+9kOHTqoTZs22rt3r7Zv3x7W1qpVK3Xu3Pmg67B79+5yOp3atWuXKmuve9ivffv2atu2raqrq1VcXBzWlpSUpK77b0BQVFQUMXB36dJFycnJ2r17t8rLy8PaMjMzlZWVJY/Ho8LCwrA2h8OhHj16SKrJd93BuVOnTkpNTVVFRYVKS0vD2ppjjKhvuT169JDD4dDOnTu1d+/esLasrCxlZmaqqqpKJbU3btgvnmNE7R/G2nzVYoyoYYUxoq5EGiMCgUBYPhgjrDdGJPr/EQf+jezYsSNjhKw1RkiJ/3/Egf+D1WKMqBGvMSIatmDdtWER9913nz744APl5OQoMzNTDzzwQKjto48+0qRJk/TRRx9FfQ32V199JUnq06dP2PR47nkOBGzq1i2omvxFXpNhswXVubO0caNHDkf4cqUjY68SR6dqxPIIdnFxsXJzc5WcnMye5zrL5ejU4Y0RXq9XmzdvVk5OTmheiTEi2uW2pHE21kewS0pK1K1bNyUnJzNGWHCMSPR8H/g3snXr1owRstYYceBypcT7P6Kqqkq//PJL6H+wxiy3pa7DRMp3QUGBJKl///4Ry60r4QvsgoICTZgwQUuXLtWQIUNC0y+99FJlZWXp6KOP1vPPP6+33npLjv3V5UMPPaS33npLa9asifr1agvsxqy85vLuu9Lo0Yee75//lEaNMt0bHAncbre2bNkS2oMLxBL5gknkC6aRMZhEvhJTNDViwt/krE+fPjrqqKM0Z84crV+/Xj/88IPuvfdeff7557ruuut0/vnnq7KyUrfffrs2bdqkVatWacWKFbr22mvj3fWYqXNWy2HPBxyK0+lUdnZ22N35gVghXzCJfME0MgaTyJf1JfyWs9vteuyxx/Tggw/qpptuUkVFhY455hg99dRT6tu3ryRp2bJluvvuu3XuuecqOztb06dP17nnnhvnnsdObm5s5wMOxeFwKCMjI97dQAtFvmAS+YJpZAwmkS/rS/hTxJtbIp4i7vdL3bvX3NCsvq1ls0l5edJPPyl0DTZwOPx+v/bt26fU1NTQpRdArJAvmES+YBoZg0nkKzG1qFPEUVM0L1hQ83Xd587Xfj9/PsU1Ysfn82nbtm0RN7QAYoF8wSTyBdPIGEwiX9ZHgW0R550nvfSStP9JBSF5eTXTzzsvPv0CAAAAANRI+Guw8T/nnSedfbb0zjseffNNqY49tp1OOy2JI9cAAAAAkAAosC3G4ZBGjgyqZ89KdenSluIaAAAAABIEp4hbkM1mU0pKimx1L8gGYoSMwSTyBZPIF0wjYzCJfFkfdxGvIxHvIg4AAAAAiA/uIg4AAAAAQDOjwLYgt9utTZs2ye12x7sraKHIGEwiXzCJfME0MgaTyJf1UWADAAAAABADFNgAAAAAAMQABTYAAAAAADFAgQ0AAAAAQAw4490BRM/lcqlbt25yOBzx7gpaKDIGk8gXTCJfMI2MwSTyZX0U2BZkt9tlt3PyAcwhYzCJfMEk8gXTyBhMIl/Wx9azIK/Xq23btsnr9ca7K2ihyBhMIl8wiXzBNDIGk8iX9VFgW1AgENCePXsUCATi3RW0UGQMJpEvmES+YBoZg0nky/oosAEAAAAAiAEKbAAAAAAAYsAWDAaD8e5EIvnss88UDAaVlJQU764cVDAYlM/nk9PplM1mi3d30AKRMZhEvmAS+YJpZAwmka/E5PF4ZLPZNHjw4EPOy13E67BCkG02m1wuV7y7gRaMjMEk8gWTyBdMI2MwiXwlJpvN1ug6kSPYAAAAAADEANdgAwAAAAAQAxTYAAAAAADEAAU2AAAAAAAxQIENAAAAAEAMUGADAAAAABADFNgAAAAAAMQABTYAAAAAADFAgQ0AAAAAQAxQYAMAAAAAEAMU2AAAAAAAxAAFNgAAAAAAMUCBnYACgYAWLlyoU045RQMHDtRvf/tbbd68+aDzl5WV6ZZbblF+fr7y8/N1xx13aN++fc3YY1hNtBk78OeuvPJKLVq0qBl6CauKNl///e9/dc0112jo0KEaPny4Jk+erK1btzZjj2El0ebr66+/1uWXX65BgwZp2LBhuvPOO1VRUdGMPYbVNPVvpCS99tpr6tu3rwoLCw33ElYVbb5eeeUV9e3bN+KjsZlE86PATkBLlizR888/r7lz5+qFF16QzWbT1VdfLY/HU+/8kydP1pYtW/T0009r4cKF+vDDDzVnzpxm7jWsJNqMSVJ1dbWmTZumDz74oBl7CiuKJl9lZWWaNGmSWrdureeee05Lly5VWVmZrrrqKrnd7jj0Hokumnxt375dkyZNUteuXfXKK69oyZIl+uyzzzRjxow49BxW0ZS/kZJUVFTE/184pGjztXHjRp144on64IMPwj7y8vKauedotCASitvtDg4aNCi4cuXK0LTy8vLggAEDgq+//nrE/J999lmwT58+wU2bNoWmvf/++8G+ffsGS0pKmqXPsJZoMxYMBoMbNmwInnHGGcHTTjstOGTIkODChQubq7uwmGjz9eKLLwYHDx4crK6uDk0rLi4O9unTJ/jRRx81S59hHU35G3nzzTcHvV5vaNrTTz8dHDhwYHN0FxbUlL+RwWAw6Pf7gxMmTAhedtllwT59+gS3bNnSHN2FxTQlX5MmTQrOnTu3ubqIGOAIdoIpKCjQ3r17NWzYsNC0Nm3a6JhjjtG6desi5l+/fr2ys7PVq1ev0LQTTzxRNptNGzZsaJY+w1qizZgkvf/++xo7dqxWr16t9PT05uoqLCjafA0fPlyPPPKIkpOTI9rKy8uN9hXWE22+Bg0apIcfflhOp1OStGnTJr3yyis66aSTmq3PsJam/I2UpMcee0xer1fXXnttc3QTFtWUfG3cuFG9e/duri4iBpzx7gDClZSUSJJyc3PDpnfo0EHFxcUR82/bti1i3qSkJGVmZtY7PxBtxiRpypQpxvuFliHafOXl5UWc5vb4448rOTlZ+fn55joKS2rK+FXrV7/6lX7++Wd17txZS5YsMdZHWFtTMvbll1/qySef1EsvvaRt27YZ7yOsK9p8lZaWaufOnVq3bp2effZZ7d69WwMHDtStt96qHj16NEufET2OYCeYqqoqSTVF8oGSk5PrvR6xqqoqYt6G5geizRgQjcPN1zPPPKOVK1dq6tSpat++vZE+wroOJ18PPvignnvuOWVnZ+uyyy7T3r17jfUT1hVtxvbt26dbb71Vt956q7p3794cXYSFRZuv77//XpLkcDh03333ad68edq3b58uvvhi7dy503yH0SQU2AkmJSVFkiJudOB2u9WqVat656/vpghut1upqalmOglLizZjQDSamq9gMKj58+fr7rvv1rXXXqsrrrjCZDdhUYczfvXv31/5+flatGiRioqK9NZbbxnrJ6wr2ozNnTtX3bt310UXXdQs/YO1RZuvYcOG6dNPP9V9992nY489Vvn5+XrkkUcUCAS0atWqZukzokeBnWBqTxnZvn172PTt27crJycnYv6cnJyIeT0ej3bv3q2OHTua6ygsK9qMAdFoSr68Xq+mTZumxx57TNOnT9fUqVON9xPWFG2+fvjhB7333nth0zp06KCMjAxO5UW9os3Yyy+/rI8//liDBg3SoEGDdPXVV0uSfvOb3+jOO+8032FYSlP+RmZkZIR9n5qaqry8PMawBEaBnWD69euntLQ0ffLJJ6FpFRUV+vbbbzVkyJCI+fPz81VSUhL2LLzanx08eLD5DsNyos0YEI2m5Gv69Olas2aNHnroIV155ZXN1VVYULT5ev/99zVlyhRVVlaGpv3yyy8qKysLuzkoUCvajP3jH//Q66+/rtWrV2v16tWaO3euJOmJJ57g/iWIEG2+Vq5cqaFDh6q6ujo0rbKyUj///DM3Pktg3OQswSQlJWnixIl68MEH1a5dO3Xu3FkPPPCAcnJyNHbsWPn9fpWWlio9PV0pKSkaOHCgBg8erJtvvlmzZ8/Wvn37dNddd+mcc87hCDbqFW3GgGhEm69Vq1bpjTfe0PTp03XiiSdqx44doWWRQdQVbb7OPvtsLV++XNOmTdPUqVNVXl6uuXPnasCAARo9enS83w4SULQZ69atW9jP197EqlOnTtxHAhGizdfo0aM1f/58TZ8+XTfeeKOqq6v18MMPq127djr33HPj/XZwEBzBTkCTJ0/W+PHjNWvWLE2YMEEOh0PLly9XUlKSiouLdfLJJ+uNN96QJNlsNi1evFh5eXm6/PLLddNNN+nUU0/V7Nmz4/smkNCiyRgQrWjy9frrr0uS7r//fp188slhH2QQ9YkmX23bttUzzzyjQCCgCRMm6Prrr9cxxxyj5cuXy+FwxPmdIFHxNxImRZOv3NxcrVixQnv37tWECRN0xRVXKD09Xc888ww7oBOYLRgMBuPdCQAAAAAArI4j2AAAAAAAxAAFNgAAAAAAMUCBDQAAAABADFBgAwAAAAAQAxTYAAAAAADEAAU2AAAAAAAxQIENAAAAAEAMUGADAAAAABADFNgAgCPapEmTdOKJJ8rj8Rx0nrPPPlv/93//1+TXKCwsVN++fbVq1aomL6M5XXrpperbt2+DH5deemlo3tqv42HVqlXq27evCgsLD3tZY8aM0cyZMxucZ+bMmRozZsxhvxYAoGVyxrsDAADE0/jx4/XRRx/pX//6l04//fSI9oKCAhUUFOiPf/xjk1+jQ4cOeuGFF9S1a9fD6Wqzueuuu1RZWRn6fs6cOaHptdLS0pq9XwAAJDoKbADAEW3s2LHKyMjQq6++Wm+BvXr1aqWmpmrcuHFNfo2kpCQdf/zxh9HL5tW7d++w72uLaSu9BwAA4oFTxAEAR7SkpCSdeeaZ+uc//6k9e/aEtfn9fr3++us644wzlJaWptLSUs2ZM0ejR4/WcccdpxNPPFHXX3992OnJl156qW699VZNnjxZgwcP1jXXXFPvKeLr1q3TlVdeqfz8fB133HEaM2aMFi1apEAgIOl/p5W/+eabmjx5sgYNGqT8/Hzdfvvt2rt3b2g5wWBQf/7znzVu3DgNGDBAY8eO1dKlSxUMBkPzrF+/XhMnTtTAgQN14oknasaMGSotLY3ZOgwGg1q6dKlGjRqlAQMG6MILL9RXX30Val+0aJHGjh2rxYsXa+jQoTr99NNVVlYmSfrrX/+qcePG6bjjjtOoUaO0aNEi+Xy+0M+Wlpbq1ltv1UknnaT+/fvr7LPP1urVqyP68MUXX+iiiy5S//79NWrUKC1fvjysfc+ePbr33nt1+umnq3///vrNb36jl156qcH3VV5erttuu01Dhw5Vfn6+HnjggdD2AQCgPhzBBgAc8caPH6/nnntOa9asCbvW+oMPPtCOHTs0fvx4BYNBXXvttSovL9ctt9yi7Oxsfffdd1qwYIHuvPNOPfnkk6Gfe/PNN3XGGWfokUcekd/vj3i9goICXXHFFTrjjDM0b948BYNB/e1vf9PixYvVvXt3nXnmmaF577rrLp1//vlasmSJvvzyS82bN0/t2rXTLbfcIkl6+OGHtXz5cl1xxRU66aST9M0332jevHnyeDy6/vrrtW7dOk2aNEnDhg3T/PnzVV5ergULFuiyyy7TSy+9pJSUlMNefxs2bJDH49Edd9whj8ej++67T9ddd53ee+89OZ01/2ps3bpVb731lh5++GGVlZWpbdu2evzxxzVv3jxNnDhRt912m7777jstWrRIxcXFuueeeyRJ06ZN065duzRnzhy1bt1ar776qmbMmKHc3FwNHTo01IfZs2drypQpmjx5sp5//nndf//96tmzp0aPHq3q6mpdfPHF2rlzp2688UZ16dJFb7/9tm6//Xbt3LlT1113XcR7CgQCuuqqq1RYWKhbb71V7du317Jly/Tll1+qQ4cOh73OAAAtEwU2AOCId/TRR+uYY47Ra6+9FlZgv/LKK+rVq5dOOOEEbdu2Ta1atdKMGTM0ZMgQSdLQoUNVWFio559/Pmx5drtdf/rTn5SamipJETfgKigo0IgRI/TAAw/Ibq85meykk07Su+++q3Xr1oUV2CNHjtSMGTMkScOHD9eHH36od999V7fccosqKir01FNP6dJLL9X06dNDyyktLdWGDRskSQ899JB69Oihxx9/XA6HQ5I0cOBAjRs3Ti+//LIuueSSw15/SUlJeuKJJ5SZmSlJqqys1KxZs7Rp0yb169dPkuTz+TRjxgyNGDFCUs0R5UcffVQXXnihZs2aJUk6+eSTlZmZqVmzZmnSpEk66qij9Omnn+r3v/996PT9oUOHKjMzM/Reak2dOlUTJkyQVHMq+9q1a/Xvf/9bo0eP1qpVq/T9999r5cqVOuGEEyRJp5xyinw+n5YsWaKLLroo1Pda//rXv/Tll1/q8ccf16hRoyRJw4YN4wZnAIAGcYo4AACqOYq9bt06lZSUSKopANeuXavx48dLkjp27KhnnnlGQ4YM0datW/Xxxx/rueee02effSav1xu2rLy8vFBxXZ9zzjlHS5culdfr1X//+1+9/fbbWrRokfx+f8Sy6l73nJOTo3379kmSPv/8c3m9Xo0dOzZsnpkzZ+rJJ59UVVWVvvjiC40cOVLBYFA+n08+n09dunRRr1699OGHHzZpXdXVu3fvsAI1Ly9PkiJOue/Tp0/o6//85z+qqqrSmDFjQv3y+XyhAra2b0OHDtWiRYs0ZcoUrVq1SqWlpWE7OWod+H1qaqqysrJUUVEhSfr000/VuXPnUHFd66yzzpLb7dYXX3wR8Z7Wr18vl8ulU089NWy5I0eObPR6AQAceTiCDQCApDPPPFP33XefXn/9dV111VV64403FAgEdPbZZ4fmefXVV/Xwww+ruLhYmZmZ6tevX72nWGdlZTX4WtXV1frTn/6kv/3tb/L5fMrLy9OgQYPkdDrDrp2WpFatWoV9b7fbQ/Ps3r1bktSuXbt6X6eiokKBQEBLly7V0qVLI9qTk5Mb7Gdj1d2ZUHtUvu71ygeul9q+X3PNNfUuc/v27ZKkefPm6bHHHtObb76pNWvWyG63a8SIEZo9e7a6dOkSmr+h9VReXl7vNqmdVluIH6i8vFyZmZmh91IrOzu73v4CACBRYAMAIElq06aNxo4dq9dee01XXXWVVq9erTFjxqh9+/aSao5ozpgxQxMnTtSVV16pnJwcSdL9998fOh27se6++279/e9/1/z58zVixIhQgTp8+PCo+yzV3AisZ8+eoenFxcXavHmzjjvuONlsNl1xxRX13gW9blHanGr7/uCDD6p79+4R7bXFb3p6uqZNm6Zp06bpxx9/1DvvvKMlS5Zozpw5WrZsWaNeKyMjQ5s3b46YvmPHDklS27ZtI9ratm2rsrIy+f3+sNPRa3cMAABQH04RBwBgv/Hjx6ugoECffvqp/vOf/4ROD5dqTmkOBAKaPHlyqLj2+/366KOPJEUerW3Ihg0bQnfTri2uv/76a5WWlka1nAEDBsjlcumdd94Jm75ixQpNmTJFKSkpOuaYY/Tjjz+qf//+oY+jjjpKixcv1ieffNLo14q1gQMHyuVyadu2bWF9c7lceuihh1RYWKiioiKNHDlSa9askST17NlTV199tUaMGBE6lb8x8vPzVVRUFLEj5NVXX5XL5dKAAQMifmb48OHy+Xx6++23Q9M8Hk/MTqsHALRMHMEGAGC/YcOGKS8vT3fccYdycnJ08sknh9pqi7A//vGPOv/881VRUaHnnntOBQUFkqR9+/aFnhd9KAMGDNCbb76pv/zlL+rVq5cKCgr06KOPymazqaqqqtH9bdeunS677DKtWLFCSUlJGjZsmL766is999xzmjp1qpxOp6ZOnaprrrlGt9xyi8466yz5/X49+eST+uKLL/S73/0uirUTW23bttVVV12lBQsWqLKyUkOHDtW2bdu0YMEC2Ww29evXT+np6crJydHcuXNVWVmprl276uuvv9Z7772na6+9ttGvdd5552nlypW64YYbNHnyZHXp0kVr167Vyy+/rBtuuCF0NP1Aw4cP18knn6xZs2Zp165d6ty5s5555hmVlpaGzmoAAKAuCmwAAPaz2Ww677zztHDhQl1//fVh198OHTpUd955p5566imtWbNGWVlZGjp0qBYvXqzrr79eGzZsaPQNsGbOnCmv16v58+fL4/EoLy9Pv/vd77Rp0yatXbu23kd7Hcy0adOUlZWlv/zlL3ryySeVl5enP/zhD7r44osl1dyZe/ny5Vq8eLEmT54sl8ulY489Vk899VTEDdSa20033aTs7GytXLlSy5YtU0ZGhoYPH66pU6cqPT1dkrR48WI9/PDDWrBggcrKypSbm6sbbrjhoNdu16dVq1Z69tln9dBDD2nhwoWqrKxUz549dffdd4edpVDX4sWL9eCDD2rhwoVyu9369a9/rQsuuCDijAEAAGrZgnXvpgIAAAAAAKLGNdgAAAAAAMQABTYAAAAAADFAgQ0AAAAAQAxQYAMAAAAAEAMU2AAAAAAAxAAFNgAAAAAAMUCBDQAAAABADFBgAwAAAAAQAxTYAAAAAADEAAU2AAAAAAAxQIENAAAAAEAMUGADAAAAABAD/z9FL6RT3FdL4QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define variance thresholds to test\n",
    "from sklearn.feature_selection import VarianceThreshold\n",
    "\n",
    "# Set Seaborn style\n",
    "sns.set(style=\"whitegrid\", palette=\"muted\")\n",
    "\n",
    "thresholds = np.arange(0, 0.55, 0.01)\n",
    "\n",
    "# Store the number of features retained for each threshold\n",
    "num_features = []\n",
    "\n",
    "for threshold in thresholds:\n",
    "    selector = VarianceThreshold(threshold=threshold)\n",
    "    selector.fit(df)\n",
    "    num_features.append(df.shape[1] - selector.get_support().sum())\n",
    "    \n",
    "# Plot the results\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "plt.text(0, num_features[0], f'{num_features[0]}', color='darkred', fontsize=10, ha='right', va='bottom')\n",
    "plt.text(0.01, num_features[1], f'{num_features[1]}', color='darkred', fontsize=10, ha='right', va='bottom')\n",
    "plt.text(0.02, num_features[2], f'{num_features[2]}', color='darkred', fontsize=10, ha='right', va='bottom')\n",
    "plt.text(0.03, num_features[3], f'{num_features[3]}', color='darkred', fontsize=10, ha='right', va='bottom')\n",
    "plt.text(0.04, num_features[4], f'{num_features[4]}', color='darkred', fontsize=10, ha='right', va='bottom')\n",
    "plt.text(0.05, num_features[5], f'{num_features[5]}', color='darkred', fontsize=10, ha='right', va='bottom')\n",
    "\n",
    "        \n",
    "plt.plot(thresholds, num_features, marker='o', linestyle='-', color='blue')\n",
    "plt.title('Number of Features vs Variance Threshold')\n",
    "plt.xlabel('Variance Threshold')\n",
    "plt.ylabel(f'Number of Features Removed')\n",
    "plt.grid(axis='both', linestyle='--', alpha=0.7)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Since our data is highly imbalanced we can simply remove the features with low variance so we are only removing features with zero variance i.e. Constant features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import VarianceThreshold\n",
    "\n",
    "# Define the variance threshold\n",
    "threshold = 0\n",
    "\n",
    "# Apply VarianceThreshold\n",
    "selector = VarianceThreshold(threshold=threshold)\n",
    "selector.fit_transform(df)\n",
    "\n",
    "# Get the selected feature names\n",
    "selected_features = df.columns[selector.get_support()]\n",
    "\n",
    "# Create a new DataFrame with selected features\n",
    "df = pd.DataFrame(df, columns=selected_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96801</th>\n",
       "      <td>96802</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96802</th>\n",
       "      <td>96803</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96803</th>\n",
       "      <td>96804</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96804</th>\n",
       "      <td>96805</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96805</th>\n",
       "      <td>96806</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96806 rows × 473 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       account_number  bad_flag  bureau_1  bureau_2  bureau_3  bureau_5  \\\n",
       "0                   1         0       0.0       0.0       0.0       0.0   \n",
       "1                   2         0       0.0       1.0       0.0       0.0   \n",
       "2                   3         0       0.0       2.0       0.0       0.0   \n",
       "3                   4         0       0.0       4.0       0.0       0.0   \n",
       "4                   5         0       1.0       0.0       4.0       0.0   \n",
       "...               ...       ...       ...       ...       ...       ...   \n",
       "96801           96802         0       0.0       1.0       0.0       4.0   \n",
       "96802           96803         0       0.0       0.0       0.0       0.0   \n",
       "96803           96804         0       0.0       0.0       1.0       0.0   \n",
       "96804           96805         0       2.0       2.0       0.0       2.0   \n",
       "96805           96806         0       0.0       0.0       0.0       0.0   \n",
       "\n",
       "       bureau_6  bureau_7  bureau_8  bureau_9  ...  bureau_enquiry_40  \\\n",
       "0           0.0      17.0       0.0      22.0  ...                1.0   \n",
       "1          22.0       6.0      22.0      10.0  ...                2.0   \n",
       "2           0.0       2.0       0.0       4.0  ...                8.0   \n",
       "3           7.0       8.0       7.0      17.0  ...               21.0   \n",
       "4           0.0      18.0       5.0      18.0  ...                1.0   \n",
       "...         ...       ...       ...       ...  ...                ...   \n",
       "96801       1.0       3.0       5.0      29.0  ...                0.0   \n",
       "96802       0.0       1.0       0.0       2.0  ...                3.0   \n",
       "96803       0.0      12.0       1.0      16.0  ...               16.0   \n",
       "96804       0.0       6.0       4.0      20.0  ...                3.0   \n",
       "96805       0.0       9.0       0.0      37.0  ...               21.0   \n",
       "\n",
       "       bureau_enquiry_41  bureau_enquiry_42  bureau_enquiry_43  \\\n",
       "0                    3.0                0.0                3.0   \n",
       "1                    6.0                2.0                4.0   \n",
       "2                   11.0                0.0               11.0   \n",
       "3                   38.0                0.0               38.0   \n",
       "4                    1.0                0.0                1.0   \n",
       "...                  ...                ...                ...   \n",
       "96801                5.0                0.0                5.0   \n",
       "96802                5.0                0.0                5.0   \n",
       "96803               37.0                0.0               37.0   \n",
       "96804               16.0                3.0               13.0   \n",
       "96805               31.0                0.0               31.0   \n",
       "\n",
       "       bureau_enquiry_44  bureau_enquiry_45  bureau_enquiry_46  \\\n",
       "0                    0.0                0.0                0.0   \n",
       "1                    0.0                1.0                0.0   \n",
       "2                    0.0                2.0                0.0   \n",
       "3                    0.0                6.0                0.0   \n",
       "4                    0.0                0.0                0.0   \n",
       "...                  ...                ...                ...   \n",
       "96801                0.0                1.0                0.0   \n",
       "96802                0.0                1.0                0.0   \n",
       "96803                0.0                9.0                0.0   \n",
       "96804                2.0                4.0                0.0   \n",
       "96805                0.0                0.0                0.0   \n",
       "\n",
       "       bureau_enquiry_48  bureau_enquiry_49  bureau_enquiry_50  \n",
       "0                    0.0                0.0                1.0  \n",
       "1                    0.0                2.0                3.0  \n",
       "2                    0.0                0.0                8.0  \n",
       "3                    0.0                0.0               30.0  \n",
       "4                    0.0                0.0                1.0  \n",
       "...                  ...                ...                ...  \n",
       "96801                0.0                0.0                1.0  \n",
       "96802                0.0                0.0                3.0  \n",
       "96803                0.0                0.0               20.0  \n",
       "96804                1.0                0.0                8.0  \n",
       "96805                0.0                0.0               25.0  \n",
       "\n",
       "[96806 rows x 473 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bad_flag             1.000000\n",
       "onus_attribute_37    0.072364\n",
       "onus_attribute_31    0.067412\n",
       "onus_attribute_34    0.065971\n",
       "onus_attribute_18    0.059707\n",
       "                       ...   \n",
       "bureau_244           0.000155\n",
       "bureau_171           0.000096\n",
       "bureau_215           0.000085\n",
       "bureau_228           0.000030\n",
       "bureau_204           0.000015\n",
       "Name: bad_flag, Length: 189, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate correlation matrix(between target and features with variance less than 0.05)\n",
    "correlation_matrix = df.loc[:,df.var() <=0.05].corr()\n",
    "abs(correlation_matrix['bad_flag']).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bad_flag                     1.000000\n",
       "onus_attribute_46            0.078038\n",
       "onus_attribute_44            0.075761\n",
       "transaction_attribute_619    0.063444\n",
       "transaction_attribute_189    0.058179\n",
       "                               ...   \n",
       "transaction_attribute_525         NaN\n",
       "transaction_attribute_582         NaN\n",
       "transaction_attribute_621         NaN\n",
       "bureau_436                        NaN\n",
       "bureau_447                        NaN\n",
       "Name: bad_flag, Length: 696, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# correlation between target and features with null value percentage greater than 0.25\n",
    "sample = train_df.loc[:, train_df.isnull().mean() > NULL_PERCENTAGE]\n",
    "\n",
    "sample = pd.concat([sample, df['bad_flag']], axis=1)\n",
    "\n",
    "correlation_matrix = sample.corr()\n",
    "abs(correlation_matrix['bad_flag']).sort_values(ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "onus_attribute_46            0.078038\n",
       "onus_attribute_44            0.075761\n",
       "transaction_attribute_619    0.063444\n",
       "transaction_attribute_189    0.058179\n",
       "onus_attribute_45            0.057981\n",
       "transaction_attribute_515    0.056867\n",
       "transaction_attribute_513    0.054752\n",
       "onus_attribute_43            0.053327\n",
       "transaction_attribute_98     0.052208\n",
       "transaction_attribute_332    0.051130\n",
       "transaction_attribute_333    0.047105\n",
       "bureau_433                   0.045936\n",
       "transaction_attribute_658    0.042225\n",
       "transaction_attribute_99     0.040517\n",
       "transaction_attribute_441    0.040342\n",
       "transaction_attribute_516    0.039848\n",
       "bureau_444                   0.039700\n",
       "transaction_attribute_257    0.039334\n",
       "transaction_attribute_388    0.039253\n",
       "Name: bad_flag, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs(correlation_matrix['bad_flag']).sort_values(ascending=False)[1:20]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bad_flag             1.000000\n",
       "onus_attribute_2     0.108491\n",
       "onus_attribute_17    0.103156\n",
       "onus_attribute_23    0.100748\n",
       "onus_attribute_20    0.100672\n",
       "                       ...   \n",
       "bureau_171           0.000096\n",
       "bureau_215           0.000085\n",
       "bureau_228           0.000030\n",
       "bureau_204           0.000015\n",
       "onus_attribute_5     0.000012\n",
       "Name: bad_flag, Length: 473, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate correlation matrix( between all the selected features after removing featues with null value > 0.25 and constant features)\n",
    "correlation_matrix = df.corr()\n",
    "abs(correlation_matrix['bad_flag']).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bad_flag\n",
       "False    416\n",
       "True      57\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(abs(correlation_matrix['bad_flag']) > 0.05).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Only 57 features have more than 0.05 correlation with the bad_flag feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The max correlation of features with variance less than 0.05 with the bad_flag column is 0.72 and taking the entire dataset the max correlation is 0.1084 so we can't remove all the features with low variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['account_number', 'bad_flag', 'bureau_1', 'bureau_2', 'bureau_3',\n",
       "       'bureau_5', 'bureau_6', 'bureau_7', 'bureau_8', 'bureau_9',\n",
       "       ...\n",
       "       'bureau_enquiry_40', 'bureau_enquiry_41', 'bureau_enquiry_42',\n",
       "       'bureau_enquiry_43', 'bureau_enquiry_44', 'bureau_enquiry_45',\n",
       "       'bureau_enquiry_46', 'bureau_enquiry_48', 'bureau_enquiry_49',\n",
       "       'bureau_enquiry_50'],\n",
       "      dtype='object', length=473)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imputing Null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account_number</th>\n",
       "      <th>bad_flag</th>\n",
       "      <th>bureau_1</th>\n",
       "      <th>bureau_2</th>\n",
       "      <th>bureau_3</th>\n",
       "      <th>bureau_5</th>\n",
       "      <th>bureau_6</th>\n",
       "      <th>bureau_7</th>\n",
       "      <th>bureau_8</th>\n",
       "      <th>bureau_9</th>\n",
       "      <th>...</th>\n",
       "      <th>bureau_enquiry_40</th>\n",
       "      <th>bureau_enquiry_41</th>\n",
       "      <th>bureau_enquiry_42</th>\n",
       "      <th>bureau_enquiry_43</th>\n",
       "      <th>bureau_enquiry_44</th>\n",
       "      <th>bureau_enquiry_45</th>\n",
       "      <th>bureau_enquiry_46</th>\n",
       "      <th>bureau_enquiry_48</th>\n",
       "      <th>bureau_enquiry_49</th>\n",
       "      <th>bureau_enquiry_50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96801</th>\n",
       "      <td>96802</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96802</th>\n",
       "      <td>96803</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96803</th>\n",
       "      <td>96804</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96804</th>\n",
       "      <td>96805</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96805</th>\n",
       "      <td>96806</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96806 rows × 473 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       account_number  bad_flag  bureau_1  bureau_2  bureau_3  bureau_5  \\\n",
       "0                   1         0       0.0       0.0       0.0       0.0   \n",
       "1                   2         0       0.0       1.0       0.0       0.0   \n",
       "2                   3         0       0.0       2.0       0.0       0.0   \n",
       "3                   4         0       0.0       4.0       0.0       0.0   \n",
       "4                   5         0       1.0       0.0       4.0       0.0   \n",
       "...               ...       ...       ...       ...       ...       ...   \n",
       "96801           96802         0       0.0       1.0       0.0       4.0   \n",
       "96802           96803         0       0.0       0.0       0.0       0.0   \n",
       "96803           96804         0       0.0       0.0       1.0       0.0   \n",
       "96804           96805         0       2.0       2.0       0.0       2.0   \n",
       "96805           96806         0       0.0       0.0       0.0       0.0   \n",
       "\n",
       "       bureau_6  bureau_7  bureau_8  bureau_9  ...  bureau_enquiry_40  \\\n",
       "0           0.0      17.0       0.0      22.0  ...                1.0   \n",
       "1          22.0       6.0      22.0      10.0  ...                2.0   \n",
       "2           0.0       2.0       0.0       4.0  ...                8.0   \n",
       "3           7.0       8.0       7.0      17.0  ...               21.0   \n",
       "4           0.0      18.0       5.0      18.0  ...                1.0   \n",
       "...         ...       ...       ...       ...  ...                ...   \n",
       "96801       1.0       3.0       5.0      29.0  ...                0.0   \n",
       "96802       0.0       1.0       0.0       2.0  ...                3.0   \n",
       "96803       0.0      12.0       1.0      16.0  ...               16.0   \n",
       "96804       0.0       6.0       4.0      20.0  ...                3.0   \n",
       "96805       0.0       9.0       0.0      37.0  ...               21.0   \n",
       "\n",
       "       bureau_enquiry_41  bureau_enquiry_42  bureau_enquiry_43  \\\n",
       "0                    3.0                0.0                3.0   \n",
       "1                    6.0                2.0                4.0   \n",
       "2                   11.0                0.0               11.0   \n",
       "3                   38.0                0.0               38.0   \n",
       "4                    1.0                0.0                1.0   \n",
       "...                  ...                ...                ...   \n",
       "96801                5.0                0.0                5.0   \n",
       "96802                5.0                0.0                5.0   \n",
       "96803               37.0                0.0               37.0   \n",
       "96804               16.0                3.0               13.0   \n",
       "96805               31.0                0.0               31.0   \n",
       "\n",
       "       bureau_enquiry_44  bureau_enquiry_45  bureau_enquiry_46  \\\n",
       "0                    0.0                0.0                0.0   \n",
       "1                    0.0                1.0                0.0   \n",
       "2                    0.0                2.0                0.0   \n",
       "3                    0.0                6.0                0.0   \n",
       "4                    0.0                0.0                0.0   \n",
       "...                  ...                ...                ...   \n",
       "96801                0.0                1.0                0.0   \n",
       "96802                0.0                1.0                0.0   \n",
       "96803                0.0                9.0                0.0   \n",
       "96804                2.0                4.0                0.0   \n",
       "96805                0.0                0.0                0.0   \n",
       "\n",
       "       bureau_enquiry_48  bureau_enquiry_49  bureau_enquiry_50  \n",
       "0                    0.0                0.0                1.0  \n",
       "1                    0.0                2.0                3.0  \n",
       "2                    0.0                0.0                8.0  \n",
       "3                    0.0                0.0               30.0  \n",
       "4                    0.0                0.0                1.0  \n",
       "...                  ...                ...                ...  \n",
       "96801                0.0                0.0                1.0  \n",
       "96802                0.0                0.0                3.0  \n",
       "96803                0.0                0.0               20.0  \n",
       "96804                1.0                0.0                8.0  \n",
       "96805                0.0                0.0               25.0  \n",
       "\n",
       "[96806 rows x 473 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.experimental import enable_iterative_imputer\n",
    "from sklearn.impute import SimpleImputer, KNNImputer,IterativeImputer\n",
    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['bad_flag'])\n",
    "y = df['bad_flag']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Simple Imputer (mode strategy)\n",
    "# simple_imputer = SimpleImputer(strategy='most_frequent')\n",
    "# X_train_simple_imputed = simple_imputer.fit_transform(X_train)\n",
    "# X_test_simple_imputed = simple_imputer.transform(X_test)\n",
    "\n",
    "# train_df = pd.concat([pd.DataFrame(X_train_simple_imputed, columns=X_train.columns), y_train.reset_index(drop=True)], axis=1)\n",
    "# test_df = pd.concat([pd.DataFrame(X_test_simple_imputed, columns=X_train.columns), y_test.reset_index(drop=True)], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # KNN Imputer\n",
    "# knn_imputer = KNNImputer(n_neighbors=5)\n",
    "# X_train_knn_imputed = knn_imputer.fit_transform(X_train)\n",
    "# X_test_knn_imputed = knn_imputer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd.concat([pd.DataFrame(X_train_knn_imputed, columns=X_train.columns), y_train.reset_index(drop=True)], axis=1).to_csv('data/interim/train.csv', index=False)\n",
    "# pd.concat([pd.DataFrame(X_test_knn_imputed, columns=X_train.columns), y_test.reset_index(drop=True)], axis=1).to_csv('data/interim/test.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('data/interim/train.csv')\n",
    "test_df = pd.read_csv('data/interim/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account_number</th>\n",
       "      <th>bureau_1</th>\n",
       "      <th>bureau_2</th>\n",
       "      <th>bureau_3</th>\n",
       "      <th>bureau_5</th>\n",
       "      <th>bureau_6</th>\n",
       "      <th>bureau_7</th>\n",
       "      <th>bureau_8</th>\n",
       "      <th>bureau_9</th>\n",
       "      <th>bureau_10</th>\n",
       "      <th>...</th>\n",
       "      <th>bureau_enquiry_41</th>\n",
       "      <th>bureau_enquiry_42</th>\n",
       "      <th>bureau_enquiry_43</th>\n",
       "      <th>bureau_enquiry_44</th>\n",
       "      <th>bureau_enquiry_45</th>\n",
       "      <th>bureau_enquiry_46</th>\n",
       "      <th>bureau_enquiry_48</th>\n",
       "      <th>bureau_enquiry_49</th>\n",
       "      <th>bureau_enquiry_50</th>\n",
       "      <th>bad_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47642.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>95032.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40510.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51692.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27459.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77439</th>\n",
       "      <td>6266.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77440</th>\n",
       "      <td>54887.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77441</th>\n",
       "      <td>76821.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77442</th>\n",
       "      <td>861.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77443</th>\n",
       "      <td>15796.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77444 rows × 473 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       account_number  bureau_1  bureau_2  bureau_3  bureau_5  bureau_6  \\\n",
       "0             47642.0       2.0       0.0       0.0       0.0       0.0   \n",
       "1             95032.0       0.0       6.0       1.0       1.0       4.0   \n",
       "2             40510.0       0.0       0.0       1.0       0.0       0.0   \n",
       "3             51692.0       0.0       0.0       2.0       0.0       0.0   \n",
       "4             27459.0       3.0       5.0       2.0       1.0       0.0   \n",
       "...               ...       ...       ...       ...       ...       ...   \n",
       "77439          6266.0       0.0       4.0       1.0       0.0       0.0   \n",
       "77440         54887.0       0.0       0.0       0.0       0.0       0.0   \n",
       "77441         76821.0       0.0       0.0       1.0       0.0       0.0   \n",
       "77442           861.0       0.0       1.0       0.0       0.0       0.0   \n",
       "77443         15796.0       1.0       4.0       0.0       0.0       2.0   \n",
       "\n",
       "       bureau_7  bureau_8  bureau_9  bureau_10  ...  bureau_enquiry_41  \\\n",
       "0           1.0       2.0       1.0        2.0  ...                5.0   \n",
       "1          13.0       7.0      31.0        6.0  ...               17.0   \n",
       "2           7.0       1.0      36.0        1.0  ...               15.0   \n",
       "3           3.0       6.0       3.0        6.0  ...                0.0   \n",
       "4           8.0       6.0      15.0        6.0  ...               22.0   \n",
       "...         ...       ...       ...        ...  ...                ...   \n",
       "77439       5.0       1.0      27.0        1.0  ...               14.0   \n",
       "77440       1.0       1.0       2.0        1.0  ...                4.0   \n",
       "77441      12.0       1.0      14.0        1.0  ...                6.0   \n",
       "77442      17.0       0.0      20.0        0.0  ...               12.0   \n",
       "77443       3.0       6.0       9.0        6.0  ...               36.0   \n",
       "\n",
       "       bureau_enquiry_42  bureau_enquiry_43  bureau_enquiry_44  \\\n",
       "0                    2.0                3.0                2.0   \n",
       "1                    3.0               14.0                0.0   \n",
       "2                    1.0               14.0                0.0   \n",
       "3                    0.0                0.0                0.0   \n",
       "4                    1.0               21.0                1.0   \n",
       "...                  ...                ...                ...   \n",
       "77439                0.0               14.0                0.0   \n",
       "77440                1.0                3.0                0.0   \n",
       "77441                0.0                6.0                0.0   \n",
       "77442                0.0               12.0                0.0   \n",
       "77443                2.0               34.0                0.0   \n",
       "\n",
       "       bureau_enquiry_45  bureau_enquiry_46  bureau_enquiry_48  \\\n",
       "0                    0.0                0.0                0.0   \n",
       "1                    4.0                2.0                0.0   \n",
       "2                    3.0                1.0                0.0   \n",
       "3                    0.0                0.0                0.0   \n",
       "4                    2.0                0.0                0.0   \n",
       "...                  ...                ...                ...   \n",
       "77439                4.0                0.0                0.0   \n",
       "77440                0.0                1.0                0.0   \n",
       "77441                0.0                0.0                0.0   \n",
       "77442                0.0                0.0                0.0   \n",
       "77443                4.0                0.0                0.0   \n",
       "\n",
       "       bureau_enquiry_49  bureau_enquiry_50  bad_flag  \n",
       "0                    0.0                3.0         0  \n",
       "1                    0.0               10.0         0  \n",
       "2                    0.0                7.0         0  \n",
       "3                    0.0                0.0         0  \n",
       "4                    0.0               18.0         0  \n",
       "...                  ...                ...       ...  \n",
       "77439                0.0                7.0         0  \n",
       "77440                0.0                3.0         0  \n",
       "77441                0.0                6.0         0  \n",
       "77442                0.0               11.0         0  \n",
       "77443                0.0               18.0         0  \n",
       "\n",
       "[77444 rows x 473 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of binary features: 8\n"
     ]
    }
   ],
   "source": [
    "cnt =0\n",
    "binary_features = []\n",
    "for col in train_df.columns:\n",
    "    if train_df[col].nunique() == 2:\n",
    "        cnt+=1\n",
    "        binary_features.append(col)\n",
    "\n",
    "print(f'Number of binary features: {cnt}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "account_number\n",
      "account_number\n",
      "1.0        1\n",
      "64559.0    1\n",
      "64557.0    1\n",
      "64556.0    1\n",
      "64555.0    1\n",
      "          ..\n",
      "32242.0    1\n",
      "32240.0    1\n",
      "32239.0    1\n",
      "32236.0    1\n",
      "96805.0    1\n",
      "Name: count, Length: 77444, dtype: int64\n",
      "---------------------\n",
      "bureau_1\n",
      "bureau_1\n",
      "0.0     58241\n",
      "1.0     12631\n",
      "2.0      3526\n",
      "3.0      1291\n",
      "4.0       527\n",
      "5.0       281\n",
      "0.2       265\n",
      "0.4       175\n",
      "6.0       120\n",
      "0.6       115\n",
      "0.8        62\n",
      "7.0        43\n",
      "8.0        34\n",
      "9.0        20\n",
      "1.2        20\n",
      "10.0       16\n",
      "1.4        11\n",
      "11.0       11\n",
      "1.6        10\n",
      "12.0        8\n",
      "1.8         7\n",
      "13.0        5\n",
      "16.0        5\n",
      "14.0        3\n",
      "15.0        2\n",
      "17.0        2\n",
      "25.0        2\n",
      "24.0        1\n",
      "64.0        1\n",
      "36.0        1\n",
      "26.0        1\n",
      "3.8         1\n",
      "23.0        1\n",
      "21.0        1\n",
      "19.0        1\n",
      "18.0        1\n",
      "2.8         1\n",
      "76.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_2\n",
      "bureau_2\n",
      "0.0     31718\n",
      "1.0     14527\n",
      "2.0      9346\n",
      "3.0      6312\n",
      "4.0      4303\n",
      "        ...  \n",
      "8.6         1\n",
      "6.4         1\n",
      "9.8         1\n",
      "10.2        1\n",
      "91.0        1\n",
      "Name: count, Length: 102, dtype: int64\n",
      "---------------------\n",
      "bureau_3\n",
      "bureau_3\n",
      "0.0     58829\n",
      "1.0     10834\n",
      "2.0      4037\n",
      "3.0      1667\n",
      "4.0       720\n",
      "5.0       332\n",
      "0.2       261\n",
      "6.0       177\n",
      "0.4       177\n",
      "0.6       120\n",
      "7.0        69\n",
      "0.8        66\n",
      "9.0        31\n",
      "8.0        30\n",
      "1.4        22\n",
      "1.2        17\n",
      "10.0       12\n",
      "1.8         7\n",
      "1.6         7\n",
      "13.0        4\n",
      "2.2         4\n",
      "11.0        4\n",
      "15.0        3\n",
      "14.0        3\n",
      "2.4         3\n",
      "12.0        3\n",
      "16.0        2\n",
      "2.6         1\n",
      "19.0        1\n",
      "25.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_5\n",
      "bureau_5\n",
      "0.0     51368\n",
      "1.0     17501\n",
      "2.0      5215\n",
      "3.0      1673\n",
      "4.0       576\n",
      "0.4       223\n",
      "0.2       212\n",
      "5.0       192\n",
      "0.6       160\n",
      "0.8        98\n",
      "6.0        78\n",
      "7.0        41\n",
      "1.2        35\n",
      "1.4        16\n",
      "8.0        13\n",
      "1.6        12\n",
      "9.0        10\n",
      "1.8         6\n",
      "10.0        5\n",
      "13.0        3\n",
      "2.2         2\n",
      "14.0        2\n",
      "11.0        1\n",
      "12.0        1\n",
      "15.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_6\n",
      "bureau_6\n",
      "0.0      65707\n",
      "1.0       3854\n",
      "2.0       2154\n",
      "3.0       1359\n",
      "4.0        891\n",
      "         ...  \n",
      "10.2         1\n",
      "10.4         1\n",
      "11.2         1\n",
      "5.6          1\n",
      "167.0        1\n",
      "Name: count, Length: 98, dtype: int64\n",
      "---------------------\n",
      "bureau_7\n",
      "bureau_7\n",
      "2.0     9985\n",
      "1.0     9952\n",
      "3.0     9468\n",
      "4.0     8212\n",
      "5.0     7022\n",
      "        ... \n",
      "41.0       1\n",
      "42.0       1\n",
      "50.0       1\n",
      "52.0       1\n",
      "55.0       1\n",
      "Name: count, Length: 89, dtype: int64\n",
      "---------------------\n",
      "bureau_8\n",
      "bureau_8\n",
      "0.0      25098\n",
      "1.0      18937\n",
      "2.0      11026\n",
      "3.0       6716\n",
      "4.0       4420\n",
      "         ...  \n",
      "55.0         1\n",
      "58.0         1\n",
      "60.0         1\n",
      "62.0         1\n",
      "172.0        1\n",
      "Name: count, Length: 121, dtype: int64\n",
      "---------------------\n",
      "bureau_9\n",
      "bureau_9\n",
      "5.0      4401\n",
      "4.0      4240\n",
      "6.0      4219\n",
      "1.0      4205\n",
      "2.0      4174\n",
      "         ... \n",
      "20.8        1\n",
      "20.6        1\n",
      "20.4        1\n",
      "18.2        1\n",
      "129.0       1\n",
      "Name: count, Length: 190, dtype: int64\n",
      "---------------------\n",
      "bureau_10\n",
      "bureau_10\n",
      "0.0      28244\n",
      "1.0      18145\n",
      "2.0      10290\n",
      "3.0       6210\n",
      "4.0       4061\n",
      "         ...  \n",
      "54.0         1\n",
      "55.0         1\n",
      "61.0         1\n",
      "64.0         1\n",
      "171.0        1\n",
      "Name: count, Length: 119, dtype: int64\n",
      "---------------------\n",
      "bureau_11\n",
      "bureau_11\n",
      "4.0      5046\n",
      "2.0      5037\n",
      "3.0      4906\n",
      "1.0      4904\n",
      "5.0      4833\n",
      "         ... \n",
      "20.6        1\n",
      "21.6        1\n",
      "22.2        1\n",
      "23.2        1\n",
      "121.0       1\n",
      "Name: count, Length: 171, dtype: int64\n",
      "---------------------\n",
      "bureau_12\n",
      "bureau_12\n",
      "6.0      3754\n",
      "5.0      3683\n",
      "7.0      3607\n",
      "4.0      3588\n",
      "8.0      3565\n",
      "         ... \n",
      "94.0        1\n",
      "97.0        1\n",
      "98.0        1\n",
      "99.0        1\n",
      "179.0       1\n",
      "Name: count, Length: 224, dtype: int64\n",
      "---------------------\n",
      "bureau_13\n",
      "bureau_13\n",
      "0.0     67745\n",
      "1.0      8351\n",
      "2.0       769\n",
      "0.2       324\n",
      "3.0       104\n",
      "0.4        95\n",
      "4.0        19\n",
      "0.6        17\n",
      "0.8         8\n",
      "5.0         8\n",
      "6.0         1\n",
      "7.0         1\n",
      "8.0         1\n",
      "20.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_14\n",
      "bureau_14\n",
      "0.0     43939\n",
      "1.0     17267\n",
      "2.0      9081\n",
      "3.0      3766\n",
      "4.0      1481\n",
      "5.0       598\n",
      "6.0       267\n",
      "0.6       153\n",
      "0.4       148\n",
      "0.2       145\n",
      "7.0       135\n",
      "0.8       119\n",
      "1.2        93\n",
      "8.0        50\n",
      "1.4        44\n",
      "9.0        34\n",
      "1.8        24\n",
      "1.6        22\n",
      "10.0       21\n",
      "11.0       11\n",
      "2.2         9\n",
      "12.0        8\n",
      "13.0        6\n",
      "2.6         5\n",
      "2.4         4\n",
      "14.0        3\n",
      "19.0        3\n",
      "18.0        2\n",
      "24.0        1\n",
      "26.0        1\n",
      "2.8         1\n",
      "22.0        1\n",
      "21.0        1\n",
      "34.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_15\n",
      "bureau_15\n",
      "0.0    64197\n",
      "1.0    10432\n",
      "2.0     1828\n",
      "3.0      325\n",
      "0.2      319\n",
      "0.4      174\n",
      "4.0       66\n",
      "0.6       64\n",
      "0.8       27\n",
      "5.0        6\n",
      "6.0        2\n",
      "7.0        2\n",
      "1.2        1\n",
      "1.6        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_17\n",
      "bureau_17\n",
      "0.0    70333\n",
      "1.0     6191\n",
      "2.0      484\n",
      "0.2      277\n",
      "0.4       70\n",
      "3.0       53\n",
      "0.6       17\n",
      "4.0       12\n",
      "0.8        4\n",
      "5.0        2\n",
      "6.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_18\n",
      "bureau_18\n",
      "0.0     72687\n",
      "1.0      3241\n",
      "2.0       839\n",
      "3.0       272\n",
      "0.2       155\n",
      "4.0        89\n",
      "0.4        46\n",
      "5.0        41\n",
      "0.6        25\n",
      "6.0        16\n",
      "0.8        12\n",
      "7.0        10\n",
      "8.0         3\n",
      "9.0         3\n",
      "11.0        3\n",
      "1.4         1\n",
      "12.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_19\n",
      "bureau_19\n",
      "1.0     17099\n",
      "2.0     15203\n",
      "3.0     11370\n",
      "0.0     10482\n",
      "4.0      7770\n",
      "5.0      5130\n",
      "6.0      3420\n",
      "7.0      2323\n",
      "8.0      1527\n",
      "9.0       972\n",
      "10.0      586\n",
      "11.0      332\n",
      "12.0      208\n",
      "13.0      118\n",
      "1.6        85\n",
      "2.2        81\n",
      "2.4        81\n",
      "2.8        65\n",
      "14.0       64\n",
      "1.8        64\n",
      "2.6        56\n",
      "1.4        55\n",
      "3.2        39\n",
      "1.2        38\n",
      "3.4        33\n",
      "0.8        32\n",
      "15.0       31\n",
      "3.8        25\n",
      "3.6        22\n",
      "16.0       20\n",
      "0.6        14\n",
      "4.2        14\n",
      "4.6        13\n",
      "17.0       12\n",
      "4.4         9\n",
      "5.4         8\n",
      "18.0        6\n",
      "0.4         6\n",
      "5.2         6\n",
      "5.8         4\n",
      "4.8         4\n",
      "5.6         3\n",
      "6.6         2\n",
      "0.2         2\n",
      "19.0        2\n",
      "20.0        2\n",
      "21.0        2\n",
      "6.8         1\n",
      "6.4         1\n",
      "24.0        1\n",
      "26.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_20\n",
      "bureau_20\n",
      "0.0     44677\n",
      "1.0     19872\n",
      "2.0      7411\n",
      "3.0      2815\n",
      "4.0      1050\n",
      "5.0       479\n",
      "0.4       179\n",
      "0.6       167\n",
      "6.0       164\n",
      "0.2       154\n",
      "0.8       115\n",
      "7.0        87\n",
      "1.2        85\n",
      "8.0        46\n",
      "1.4        31\n",
      "9.0        26\n",
      "1.6        26\n",
      "10.0       12\n",
      "1.8        10\n",
      "2.2         9\n",
      "11.0        8\n",
      "13.0        6\n",
      "12.0        4\n",
      "2.8         3\n",
      "14.0        2\n",
      "17.0        1\n",
      "20.0        1\n",
      "3.2         1\n",
      "15.0        1\n",
      "2.6         1\n",
      "22.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_21\n",
      "bureau_21\n",
      "2.0     11099\n",
      "1.0     11053\n",
      "3.0     10119\n",
      "4.0      8822\n",
      "5.0      7186\n",
      "        ...  \n",
      "0.4         1\n",
      "0.6         1\n",
      "9.4         1\n",
      "8.8         1\n",
      "41.0        1\n",
      "Name: count, Length: 74, dtype: int64\n",
      "---------------------\n",
      "bureau_22\n",
      "bureau_22\n",
      "2.0     9682\n",
      "3.0     9566\n",
      "1.0     9004\n",
      "4.0     8895\n",
      "5.0     7690\n",
      "        ... \n",
      "9.8        1\n",
      "10.4       1\n",
      "38.0       1\n",
      "41.0       1\n",
      "48.0       1\n",
      "Name: count, Length: 79, dtype: int64\n",
      "---------------------\n",
      "bureau_23\n",
      "bureau_23\n",
      "0.0     67542\n",
      "1.0      8502\n",
      "2.0       808\n",
      "0.2       326\n",
      "3.0       113\n",
      "0.4        96\n",
      "0.6        19\n",
      "4.0        16\n",
      "5.0        10\n",
      "0.8         8\n",
      "6.0         1\n",
      "7.0         1\n",
      "8.0         1\n",
      "20.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_24\n",
      "bureau_24\n",
      "0.0     40560\n",
      "1.0     18418\n",
      "2.0      9872\n",
      "3.0      4414\n",
      "4.0      1895\n",
      "5.0       759\n",
      "6.0       364\n",
      "7.0       160\n",
      "0.4       148\n",
      "0.6       143\n",
      "0.8       130\n",
      "0.2       126\n",
      "1.2        88\n",
      "8.0        88\n",
      "1.4        63\n",
      "9.0        53\n",
      "1.6        36\n",
      "1.8        25\n",
      "10.0       25\n",
      "11.0       17\n",
      "2.2        15\n",
      "12.0        8\n",
      "13.0        5\n",
      "14.0        4\n",
      "2.6         4\n",
      "2.8         4\n",
      "2.4         4\n",
      "19.0        3\n",
      "3.2         2\n",
      "15.0        2\n",
      "21.0        2\n",
      "3.6         1\n",
      "16.0        1\n",
      "17.0        1\n",
      "23.0        1\n",
      "24.0        1\n",
      "26.0        1\n",
      "34.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_25\n",
      "bureau_25\n",
      "0.0    63977\n",
      "1.0    10544\n",
      "2.0     1900\n",
      "3.0      352\n",
      "0.2      321\n",
      "0.4      168\n",
      "0.6       71\n",
      "4.0       67\n",
      "0.8       27\n",
      "5.0        9\n",
      "1.2        3\n",
      "7.0        3\n",
      "1.6        1\n",
      "6.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_27\n",
      "bureau_27\n",
      "0.0    69294\n",
      "1.0     7009\n",
      "2.0      629\n",
      "0.2      297\n",
      "0.4       91\n",
      "3.0       77\n",
      "0.6       21\n",
      "4.0       12\n",
      "0.8        6\n",
      "5.0        4\n",
      "6.0        4\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_28\n",
      "bureau_28\n",
      "0.0     72621\n",
      "1.0      3280\n",
      "2.0       849\n",
      "3.0       272\n",
      "0.2       156\n",
      "4.0        90\n",
      "0.4        49\n",
      "5.0        44\n",
      "0.6        24\n",
      "6.0        24\n",
      "0.8        12\n",
      "7.0        10\n",
      "9.0         4\n",
      "8.0         3\n",
      "1.2         1\n",
      "1.4         1\n",
      "12.0        1\n",
      "13.0        1\n",
      "14.0        1\n",
      "16.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_29\n",
      "bureau_29\n",
      "1.0     12587\n",
      "2.0     12451\n",
      "3.0     11351\n",
      "4.0      9013\n",
      "5.0      7014\n",
      "        ...  \n",
      "29.0        2\n",
      "8.6         1\n",
      "8.4         1\n",
      "26.0        1\n",
      "32.0        1\n",
      "Name: count, Length: 62, dtype: int64\n",
      "---------------------\n",
      "bureau_30\n",
      "bureau_30\n",
      "0.0     43297\n",
      "1.0     20471\n",
      "2.0      7788\n",
      "3.0      3040\n",
      "4.0      1142\n",
      "5.0       498\n",
      "6.0       199\n",
      "0.4       171\n",
      "0.6       167\n",
      "0.2       140\n",
      "0.8       126\n",
      "7.0       103\n",
      "1.2        79\n",
      "1.4        48\n",
      "8.0        46\n",
      "9.0        33\n",
      "1.6        22\n",
      "1.8        16\n",
      "10.0       14\n",
      "2.2         9\n",
      "11.0        8\n",
      "13.0        5\n",
      "2.4         5\n",
      "2.8         3\n",
      "15.0        3\n",
      "17.0        2\n",
      "12.0        2\n",
      "14.0        2\n",
      "22.0        1\n",
      "3.2         1\n",
      "16.0        1\n",
      "2.6         1\n",
      "23.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_31\n",
      "bureau_31\n",
      "3.0     8621\n",
      "4.0     8514\n",
      "2.0     7834\n",
      "5.0     7684\n",
      "1.0     7331\n",
      "        ... \n",
      "35.0       1\n",
      "0.8        1\n",
      "37.0       1\n",
      "39.0       1\n",
      "41.0       1\n",
      "Name: count, Length: 84, dtype: int64\n",
      "---------------------\n",
      "bureau_32\n",
      "bureau_32\n",
      "0.0     45861\n",
      "1.0     19143\n",
      "2.0      7186\n",
      "3.0      2677\n",
      "4.0      1029\n",
      "5.0       415\n",
      "0.4       186\n",
      "6.0       176\n",
      "0.2       171\n",
      "0.6       148\n",
      "0.8       126\n",
      "7.0        89\n",
      "1.2        56\n",
      "8.0        44\n",
      "1.4        39\n",
      "9.0        30\n",
      "1.6        14\n",
      "1.8        12\n",
      "10.0       10\n",
      "2.2         6\n",
      "11.0        4\n",
      "12.0        4\n",
      "13.0        3\n",
      "14.0        3\n",
      "2.4         2\n",
      "2.6         2\n",
      "15.0        2\n",
      "17.0        2\n",
      "2.8         1\n",
      "16.0        1\n",
      "22.0        1\n",
      "23.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_33\n",
      "bureau_33\n",
      "3.0     9500\n",
      "2.0     9389\n",
      "4.0     8610\n",
      "1.0     8308\n",
      "5.0     7465\n",
      "        ... \n",
      "11.2       1\n",
      "33.0       1\n",
      "10.6       1\n",
      "0.8        1\n",
      "44.0       1\n",
      "Name: count, Length: 79, dtype: int64\n",
      "---------------------\n",
      "bureau_34\n",
      "bureau_34\n",
      "4.0     7918\n",
      "5.0     7687\n",
      "3.0     7538\n",
      "6.0     7020\n",
      "2.0     6761\n",
      "        ... \n",
      "1.6        1\n",
      "11.2       1\n",
      "1.4        1\n",
      "1.2        1\n",
      "51.0       1\n",
      "Name: count, Length: 92, dtype: int64\n",
      "---------------------\n",
      "bureau_35\n",
      "bureau_35\n",
      "0.0           58268\n",
      "500000.0        518\n",
      "300000.0        483\n",
      "400000.0        404\n",
      "600000.0        345\n",
      "              ...  \n",
      "535540.0          1\n",
      "535538.0          1\n",
      "535528.0          1\n",
      "535509.0          1\n",
      "65950921.0        1\n",
      "Name: count, Length: 8978, dtype: int64\n",
      "---------------------\n",
      "bureau_36\n",
      "bureau_36\n",
      "0.0           31739\n",
      "100000.0        783\n",
      "200000.0        698\n",
      "300000.0        506\n",
      "50000.0         506\n",
      "              ...  \n",
      "300157.0          1\n",
      "300150.0          1\n",
      "300143.0          1\n",
      "300138.0          1\n",
      "44000000.0        1\n",
      "Name: count, Length: 25884, dtype: int64\n",
      "---------------------\n",
      "bureau_37\n",
      "bureau_37\n",
      "0.0            58874\n",
      "2000000.0        362\n",
      "3000000.0        348\n",
      "1500000.0        328\n",
      "1000000.0        254\n",
      "               ...  \n",
      "2132715.0          1\n",
      "2134681.0          1\n",
      "2135242.0          1\n",
      "2135367.2          1\n",
      "604434627.0        1\n",
      "Name: count, Length: 10350, dtype: int64\n",
      "---------------------\n",
      "bureau_39\n",
      "bureau_39\n",
      "0.0          51389\n",
      "40000.0        379\n",
      "50000.0        337\n",
      "45000.0        312\n",
      "30000.0        304\n",
      "             ...  \n",
      "66172.0          1\n",
      "66192.0          1\n",
      "66216.0          1\n",
      "66224.0          1\n",
      "2657000.0        1\n",
      "Name: count, Length: 11018, dtype: int64\n",
      "---------------------\n",
      "bureau_40\n",
      "bureau_40\n",
      "0.0           65747\n",
      "50000.0         131\n",
      "100000.0        122\n",
      "200000.0         82\n",
      "300000.0         67\n",
      "              ...  \n",
      "201360.0          1\n",
      "201322.0          1\n",
      "201239.0          1\n",
      "201180.0          1\n",
      "34439159.0        1\n",
      "Name: count, Length: 6498, dtype: int64\n",
      "---------------------\n",
      "bureau_41\n",
      "bureau_41\n",
      "0.000000e+00    25744\n",
      "5.000000e+05      253\n",
      "5.000000e+04      220\n",
      "3.000000e+05      219\n",
      "4.000000e+04      212\n",
      "                ...  \n",
      "7.415400e+05        1\n",
      "7.414540e+05        1\n",
      "7.414500e+05        1\n",
      "7.414000e+05        1\n",
      "1.719964e+09        1\n",
      "Name: count, Length: 33344, dtype: int64\n",
      "---------------------\n",
      "bureau_42\n",
      "bureau_42\n",
      "0.000000e+00    12142\n",
      "2.000000e+03      953\n",
      "1.000000e+05      285\n",
      "4.000000e+03      233\n",
      "3.000000e+05      227\n",
      "                ...  \n",
      "2.960700e+05        1\n",
      "2.960900e+05        1\n",
      "2.961010e+05        1\n",
      "2.961140e+05        1\n",
      "1.393238e+09        1\n",
      "Name: count, Length: 51505, dtype: int64\n",
      "---------------------\n",
      "bureau_43\n",
      "bureau_43\n",
      "0.000000e+00    6092\n",
      "2.000000e+03     586\n",
      "3.000000e+05     147\n",
      "4.000000e+05     141\n",
      "1.000000e+05     132\n",
      "                ... \n",
      "6.154380e+05       1\n",
      "6.154800e+05       1\n",
      "6.154960e+05       1\n",
      "6.155250e+05       1\n",
      "1.721964e+09       1\n",
      "Name: count, Length: 60811, dtype: int64\n",
      "---------------------\n",
      "bureau_44\n",
      "bureau_44\n",
      "0.0           67546\n",
      "500000.0        310\n",
      "600000.0        240\n",
      "400000.0        217\n",
      "700000.0        186\n",
      "              ...  \n",
      "216314.0          1\n",
      "596775.0          1\n",
      "596876.0          1\n",
      "597000.0          1\n",
      "39642200.0        1\n",
      "Name: count, Length: 4858, dtype: int64\n",
      "---------------------\n",
      "bureau_45\n",
      "bureau_45\n",
      "0.0           40599\n",
      "200000.0        657\n",
      "5000.0          611\n",
      "100000.0        541\n",
      "300000.0        495\n",
      "              ...  \n",
      "237244.0          1\n",
      "237270.0          1\n",
      "237327.0          1\n",
      "237350.0          1\n",
      "44000000.0        1\n",
      "Name: count, Length: 18666, dtype: int64\n",
      "---------------------\n",
      "bureau_46\n",
      "bureau_46\n",
      "0.0            63994\n",
      "3000000.0        351\n",
      "2000000.0        298\n",
      "1500000.0        257\n",
      "2500000.0        220\n",
      "               ...  \n",
      "1746963.0          1\n",
      "1745680.0          1\n",
      "1745576.0          1\n",
      "1745350.0          1\n",
      "191530000.0        1\n",
      "Name: count, Length: 6903, dtype: int64\n",
      "---------------------\n",
      "bureau_48\n",
      "bureau_48\n",
      "0.0          69295\n",
      "60000.0         97\n",
      "50000.0         90\n",
      "100000.0        77\n",
      "55000.0         70\n",
      "             ...  \n",
      "65291.0          1\n",
      "65308.0          1\n",
      "65315.0          1\n",
      "65326.0          1\n",
      "1657000.0        1\n",
      "Name: count, Length: 4400, dtype: int64\n",
      "---------------------\n",
      "bureau_49\n",
      "bureau_49\n",
      "0.0          72638\n",
      "50000.0         84\n",
      "100000.0        84\n",
      "200000.0        56\n",
      "40000.0         42\n",
      "             ...  \n",
      "97250.0          1\n",
      "97002.0          1\n",
      "96900.0          1\n",
      "96850.0          1\n",
      "6705000.0        1\n",
      "Name: count, Length: 2296, dtype: int64\n",
      "---------------------\n",
      "bureau_50\n",
      "bureau_50\n",
      "0.0            43979\n",
      "500000.0         284\n",
      "3000000.0        255\n",
      "2000000.0        242\n",
      "1000000.0        240\n",
      "               ...  \n",
      "601538.0           1\n",
      "601448.0           1\n",
      "601404.0           1\n",
      "601352.2           1\n",
      "699752644.0        1\n",
      "Name: count, Length: 19995, dtype: int64\n",
      "---------------------\n",
      "bureau_51\n",
      "bureau_51\n",
      "0.000000e+00    24502\n",
      "2.000000e+03     1983\n",
      "4.000000e+03      431\n",
      "2.000000e+05      375\n",
      "3.000000e+03      365\n",
      "                ...  \n",
      "2.068010e+05        1\n",
      "2.067960e+05        1\n",
      "2.067870e+05        1\n",
      "2.067780e+05        1\n",
      "1.250000e+09        1\n",
      "Name: count, Length: 32030, dtype: int64\n",
      "---------------------\n",
      "bureau_52\n",
      "bureau_52\n",
      "0.000000e+00    15579\n",
      "2.000000e+03     1353\n",
      "5.000000e+05      307\n",
      "4.000000e+03      286\n",
      "3.000000e+05      248\n",
      "                ...  \n",
      "4.081560e+05        1\n",
      "4.081900e+05        1\n",
      "4.082800e+05        1\n",
      "4.082870e+05        1\n",
      "1.250000e+09        1\n",
      "Name: count, Length: 42977, dtype: int64\n",
      "---------------------\n",
      "bureau_53\n",
      "bureau_53\n",
      " 0.0           67559\n",
      " 1.0              16\n",
      " 1699.0           13\n",
      " 600000.0          8\n",
      "-1.0               7\n",
      "               ...  \n",
      " 267538.0          1\n",
      " 267636.0          1\n",
      " 267719.0          1\n",
      " 267748.0          1\n",
      " 23021617.0        1\n",
      "Name: count, Length: 9669, dtype: int64\n",
      "---------------------\n",
      "bureau_54\n",
      "bureau_54\n",
      "0.0           43850\n",
      "50000.0          43\n",
      "100.0            38\n",
      "200000.0         37\n",
      "100000.0         31\n",
      "              ...  \n",
      "122820.0          1\n",
      "122782.0          1\n",
      "122756.0          1\n",
      "122700.0          1\n",
      "13338836.0        1\n",
      "Name: count, Length: 31726, dtype: int64\n",
      "---------------------\n",
      "bureau_55\n",
      "bureau_55\n",
      "0.0           64085\n",
      "2000000.0        12\n",
      "2500000.0        10\n",
      "1500000.0         9\n",
      "3000000.0         9\n",
      "              ...  \n",
      "1160740.0         1\n",
      "1161611.0         1\n",
      "1161830.0         1\n",
      "1162462.0         1\n",
      "96461931.0        1\n",
      "Name: count, Length: 13063, dtype: int64\n",
      "---------------------\n",
      "bureau_57\n",
      "bureau_57\n",
      "0.0          70164\n",
      "400.0           14\n",
      "800.0           10\n",
      "76000.0          7\n",
      "3868.2           5\n",
      "             ...  \n",
      "23114.0          1\n",
      "23108.0          1\n",
      "23107.0          1\n",
      "23106.0          1\n",
      "1452722.0        1\n",
      "Name: count, Length: 6967, dtype: int64\n",
      "---------------------\n",
      "bureau_58\n",
      "bureau_58\n",
      "0.0          72676\n",
      "50000.0         35\n",
      "30000.0         21\n",
      "100000.0        20\n",
      "200000.0        19\n",
      "             ...  \n",
      "83333.0          1\n",
      "83402.0          1\n",
      "83424.2          1\n",
      "83429.0          1\n",
      "6729219.0        1\n",
      "Name: count, Length: 4010, dtype: int64\n",
      "---------------------\n",
      "bureau_59\n",
      "bureau_59\n",
      " 0.0           8180\n",
      "-1.0            324\n",
      "-2.0             81\n",
      "-5.0             63\n",
      "-3.0             56\n",
      "               ... \n",
      " 50720.0          1\n",
      " 50723.0          1\n",
      " 50724.0          1\n",
      " 50733.0          1\n",
      " 32086856.0       1\n",
      "Name: count, Length: 57050, dtype: int64\n",
      "---------------------\n",
      "bureau_60\n",
      "bureau_60\n",
      " 0.0            44202\n",
      " 1.0               32\n",
      "-1.0               31\n",
      " 50000.0           27\n",
      " 100000.0          22\n",
      "                ...  \n",
      " 283102.0           1\n",
      " 283093.0           1\n",
      " 283000.0           1\n",
      " 282929.0           1\n",
      " 425576515.0        1\n",
      "Name: count, Length: 32016, dtype: int64\n",
      "---------------------\n",
      "bureau_61\n",
      "bureau_61\n",
      " 0.000000e+00    4719\n",
      "-1.000000e+00     208\n",
      "-2.000000e+00      52\n",
      "-5.000000e+00      42\n",
      "-3.000000e+00      38\n",
      "                 ... \n",
      " 1.003090e+05       1\n",
      " 1.003130e+05       1\n",
      " 1.003190e+05       1\n",
      " 1.003260e+05       1\n",
      " 1.018020e+09       1\n",
      "Name: count, Length: 65777, dtype: int64\n",
      "---------------------\n",
      "bureau_62\n",
      "bureau_62\n",
      " 0.000000e+00    3080\n",
      "-1.000000e+00     159\n",
      "-5.000000e+00      37\n",
      "-2.000000e+00      26\n",
      "-4.000000e+00      24\n",
      "                 ... \n",
      " 1.945580e+05       1\n",
      " 1.945600e+05       1\n",
      " 1.945820e+05       1\n",
      " 1.945870e+05       1\n",
      " 1.018020e+09       1\n",
      "Name: count, Length: 70163, dtype: int64\n",
      "---------------------\n",
      "bureau_63\n",
      "bureau_63\n",
      "0.0           12324\n",
      "50000.0        2493\n",
      "100000.0       1676\n",
      "35000.0        1401\n",
      "60000.0        1132\n",
      "              ...  \n",
      "200148.0          1\n",
      "200400.0          1\n",
      "200690.0          1\n",
      "201500.0          1\n",
      "10000000.0        1\n",
      "Name: count, Length: 3983, dtype: int64\n",
      "---------------------\n",
      "bureau_64\n",
      "bureau_64\n",
      "0.000000e+00    12324\n",
      "5.000000e+03      463\n",
      "1.000000e+04      457\n",
      "2.000000e+04      345\n",
      "1.500000e+04      331\n",
      "                ...  \n",
      "2.567654e+04        1\n",
      "2.567400e+04        1\n",
      "2.567368e+04        1\n",
      "2.566625e+04        1\n",
      "2.820000e+06        1\n",
      "Name: count, Length: 25424, dtype: int64\n",
      "---------------------\n",
      "bureau_65\n",
      "bureau_65\n",
      "0.0           12324\n",
      "50000.0        1067\n",
      "25000.0         703\n",
      "35000.0         689\n",
      "100000.0        625\n",
      "              ...  \n",
      "589110.0          1\n",
      "589600.0          1\n",
      "589762.0          1\n",
      "590159.0          1\n",
      "36102000.0        1\n",
      "Name: count, Length: 11869, dtype: int64\n",
      "---------------------\n",
      "bureau_66\n",
      "bureau_66\n",
      " 0.0           18065\n",
      "-1.0             510\n",
      "-2.0             152\n",
      "-3.0              95\n",
      "-5.0              91\n",
      "               ...  \n",
      " 39290.0           1\n",
      " 39301.0           1\n",
      " 39304.0           1\n",
      " 39309.0           1\n",
      " 27438114.0        1\n",
      "Name: count, Length: 47116, dtype: int64\n",
      "---------------------\n",
      "bureau_67\n",
      "bureau_67\n",
      "0.0    77167\n",
      "1.0      263\n",
      "0.2       14\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_68\n",
      "bureau_68\n",
      "0.0     75120\n",
      "1.0      1889\n",
      "2.0       196\n",
      "0.2        91\n",
      "3.0        36\n",
      "4.0        25\n",
      "6.0        15\n",
      "5.0        15\n",
      "0.4        15\n",
      "7.0         7\n",
      "9.0         5\n",
      "8.0         4\n",
      "10.0        4\n",
      "11.0        4\n",
      "0.6         3\n",
      "1.8         2\n",
      "1.2         2\n",
      "0.8         2\n",
      "15.0        2\n",
      "16.0        2\n",
      "19.0        2\n",
      "2.2         1\n",
      "22.0        1\n",
      "33.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_69\n",
      "bureau_69\n",
      "0.0    77089\n",
      "1.0      324\n",
      "0.2       18\n",
      "2.0       12\n",
      "0.4        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_71\n",
      "bureau_71\n",
      "0.0    77251\n",
      "1.0      185\n",
      "0.2        7\n",
      "2.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_72\n",
      "bureau_72\n",
      "0.0     76617\n",
      "1.0       712\n",
      "2.0        53\n",
      "0.2        38\n",
      "3.0        14\n",
      "4.0         5\n",
      "0.4         2\n",
      "5.0         2\n",
      "10.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_73\n",
      "bureau_73\n",
      "0.0    69475\n",
      "1.0     6750\n",
      "2.0      756\n",
      "0.2      257\n",
      "3.0      100\n",
      "0.4       64\n",
      "0.6       24\n",
      "4.0       11\n",
      "0.8        4\n",
      "1.2        1\n",
      "5.0        1\n",
      "6.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_74\n",
      "bureau_74\n",
      "0.0     75507\n",
      "1.0      1698\n",
      "2.0       115\n",
      "0.2        86\n",
      "3.0        18\n",
      "0.4         9\n",
      "4.0         5\n",
      "5.0         3\n",
      "0.6         2\n",
      "10.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_75\n",
      "bureau_75\n",
      "0.0     64613\n",
      "1.0      9547\n",
      "2.0      2005\n",
      "3.0       475\n",
      "0.2       283\n",
      "4.0       150\n",
      "0.4       120\n",
      "5.0        68\n",
      "0.6        61\n",
      "6.0        31\n",
      "0.8        20\n",
      "8.0        16\n",
      "7.0        13\n",
      "9.0         6\n",
      "10.0        6\n",
      "1.4         4\n",
      "12.0        4\n",
      "19.0        3\n",
      "16.0        3\n",
      "2.6         2\n",
      "13.0        2\n",
      "15.0        2\n",
      "11.0        2\n",
      "1.2         2\n",
      "14.0        1\n",
      "1.6         1\n",
      "18.0        1\n",
      "3.2         1\n",
      "22.0        1\n",
      "33.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_76\n",
      "bureau_76\n",
      "0.0     63211\n",
      "1.0     10465\n",
      "2.0      2304\n",
      "3.0       577\n",
      "0.2       297\n",
      "4.0       165\n",
      "0.4       136\n",
      "5.0        78\n",
      "0.6        65\n",
      "6.0        36\n",
      "0.8        29\n",
      "7.0        20\n",
      "8.0        14\n",
      "9.0         7\n",
      "1.4         6\n",
      "10.0        6\n",
      "11.0        4\n",
      "12.0        4\n",
      "16.0        3\n",
      "13.0        2\n",
      "19.0        2\n",
      "15.0        2\n",
      "2.6         2\n",
      "1.2         2\n",
      "14.0        1\n",
      "1.8         1\n",
      "18.0        1\n",
      "3.4         1\n",
      "20.0        1\n",
      "22.0        1\n",
      "33.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_77\n",
      "bureau_77\n",
      "0.0    76230\n",
      "1.0     1132\n",
      "0.2       58\n",
      "2.0       20\n",
      "0.4        2\n",
      "3.0        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_78\n",
      "bureau_78\n",
      "0.0     66012\n",
      "1.0      8642\n",
      "2.0      1519\n",
      "3.0       394\n",
      "0.2       278\n",
      "4.0       153\n",
      "0.4       116\n",
      "5.0        72\n",
      "6.0        54\n",
      "0.6        47\n",
      "7.0        37\n",
      "0.8        28\n",
      "9.0        19\n",
      "8.0        16\n",
      "10.0       11\n",
      "1.2         8\n",
      "12.0        7\n",
      "11.0        6\n",
      "13.0        5\n",
      "16.0        4\n",
      "19.0        3\n",
      "15.0        2\n",
      "17.0        2\n",
      "20.0        1\n",
      "26.0        1\n",
      "22.0        1\n",
      "21.0        1\n",
      "3.8         1\n",
      "18.0        1\n",
      "1.6         1\n",
      "1.4         1\n",
      "33.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_79\n",
      "bureau_79\n",
      "0.0    75869\n",
      "1.0     1376\n",
      "0.2       97\n",
      "2.0       92\n",
      "0.4        7\n",
      "3.0        2\n",
      "0.6        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_81\n",
      "bureau_81\n",
      "0.0    76564\n",
      "1.0      822\n",
      "0.2       44\n",
      "2.0       13\n",
      "0.4        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_82\n",
      "bureau_82\n",
      "0.0     74998\n",
      "1.0      1838\n",
      "2.0       303\n",
      "3.0       106\n",
      "0.2       104\n",
      "4.0        36\n",
      "0.4        21\n",
      "0.6        12\n",
      "5.0         9\n",
      "6.0         4\n",
      "7.0         3\n",
      "8.0         3\n",
      "0.8         2\n",
      "9.0         2\n",
      "10.0        2\n",
      "12.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_83\n",
      "bureau_83\n",
      "0.0    40695\n",
      "1.0    26261\n",
      "2.0     7178\n",
      "3.0     1934\n",
      "4.0      505\n",
      "0.2      214\n",
      "0.4      194\n",
      "0.6      144\n",
      "5.0      119\n",
      "0.8      101\n",
      "1.2       34\n",
      "6.0       26\n",
      "1.4       14\n",
      "1.6       11\n",
      "1.8        6\n",
      "7.0        4\n",
      "2.2        2\n",
      "8.0        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_84\n",
      "bureau_84\n",
      "0.0     70120\n",
      "1.0      5818\n",
      "2.0       834\n",
      "0.2       249\n",
      "3.0       211\n",
      "0.4        81\n",
      "4.0        57\n",
      "0.6        26\n",
      "5.0        16\n",
      "0.8        15\n",
      "6.0         4\n",
      "7.0         4\n",
      "8.0         3\n",
      "9.0         2\n",
      "1.2         1\n",
      "10.0        1\n",
      "11.0        1\n",
      "12.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_85\n",
      "bureau_85\n",
      "0.0     30689\n",
      "1.0     24853\n",
      "2.0     11337\n",
      "3.0      5197\n",
      "4.0      2408\n",
      "5.0      1085\n",
      "6.0       512\n",
      "7.0       226\n",
      "8.0       150\n",
      "0.4       134\n",
      "0.6       131\n",
      "0.2       120\n",
      "0.8       118\n",
      "1.2        79\n",
      "1.4        64\n",
      "9.0        64\n",
      "10.0       40\n",
      "1.6        30\n",
      "1.8        30\n",
      "11.0       28\n",
      "2.2        19\n",
      "2.4        17\n",
      "13.0       14\n",
      "12.0       14\n",
      "2.6        11\n",
      "14.0       11\n",
      "16.0       10\n",
      "15.0        7\n",
      "20.0        6\n",
      "23.0        4\n",
      "3.4         4\n",
      "2.8         4\n",
      "18.0        3\n",
      "22.0        3\n",
      "21.0        3\n",
      "17.0        3\n",
      "19.0        2\n",
      "27.0        2\n",
      "29.0        1\n",
      "26.0        1\n",
      "33.0        1\n",
      "35.0        1\n",
      "38.0        1\n",
      "4.2         1\n",
      "25.0        1\n",
      "5.4         1\n",
      "6.4         1\n",
      "3.8         1\n",
      "5.6         1\n",
      "58.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_86\n",
      "bureau_86\n",
      "0.0     28329\n",
      "1.0     24723\n",
      "2.0     12183\n",
      "3.0      5920\n",
      "4.0      2820\n",
      "5.0      1341\n",
      "6.0       610\n",
      "7.0       302\n",
      "8.0       179\n",
      "0.6       122\n",
      "0.4       120\n",
      "0.8       108\n",
      "0.2        87\n",
      "9.0        84\n",
      "1.2        82\n",
      "1.4        77\n",
      "1.6        54\n",
      "10.0       53\n",
      "1.8        37\n",
      "11.0       35\n",
      "2.2        25\n",
      "12.0       19\n",
      "2.6        18\n",
      "2.4        17\n",
      "13.0       16\n",
      "14.0       14\n",
      "16.0        7\n",
      "15.0        7\n",
      "20.0        6\n",
      "17.0        6\n",
      "2.8         4\n",
      "21.0        4\n",
      "3.4         3\n",
      "18.0        3\n",
      "19.0        3\n",
      "3.6         3\n",
      "22.0        3\n",
      "3.2         2\n",
      "6.4         2\n",
      "27.0        2\n",
      "23.0        2\n",
      "24.0        2\n",
      "38.0        1\n",
      "35.0        1\n",
      "33.0        1\n",
      "29.0        1\n",
      "4.2         1\n",
      "26.0        1\n",
      "25.0        1\n",
      "4.4         1\n",
      "5.6         1\n",
      "58.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_87\n",
      "bureau_87\n",
      "0.0    74701\n",
      "1.0     2525\n",
      "0.2      127\n",
      "2.0       70\n",
      "0.4        9\n",
      "3.0        9\n",
      "0.6        1\n",
      "4.0        1\n",
      "5.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_88\n",
      "bureau_88\n",
      "0.0     56665\n",
      "1.0     13849\n",
      "2.0      3629\n",
      "3.0      1223\n",
      "4.0       477\n",
      "0.2       274\n",
      "5.0       246\n",
      "0.4       197\n",
      "6.0       158\n",
      "7.0       104\n",
      "0.6        94\n",
      "8.0        79\n",
      "9.0        59\n",
      "0.8        56\n",
      "10.0       37\n",
      "11.0       36\n",
      "12.0       31\n",
      "1.2        24\n",
      "13.0       20\n",
      "16.0       19\n",
      "17.0       19\n",
      "14.0       14\n",
      "19.0       14\n",
      "15.0       13\n",
      "18.0       13\n",
      "1.4        12\n",
      "1.6        10\n",
      "20.0        8\n",
      "21.0        8\n",
      "22.0        7\n",
      "23.0        7\n",
      "1.8         6\n",
      "26.0        5\n",
      "28.0        3\n",
      "24.0        2\n",
      "25.0        2\n",
      "29.0        2\n",
      "33.0        2\n",
      "2.4         2\n",
      "3.2         2\n",
      "2.2         2\n",
      "6.4         1\n",
      "5.8         1\n",
      "5.2         1\n",
      "4.8         1\n",
      "4.2         1\n",
      "27.0        1\n",
      "3.6         1\n",
      "30.0        1\n",
      "31.0        1\n",
      "32.0        1\n",
      "3.4         1\n",
      "36.0        1\n",
      "37.0        1\n",
      "43.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_89\n",
      "bureau_89\n",
      "0.0    74206\n",
      "1.0     2791\n",
      "2.0      242\n",
      "0.2      155\n",
      "0.4       36\n",
      "3.0       11\n",
      "0.6        2\n",
      "4.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_91\n",
      "bureau_91\n",
      "0.0    74973\n",
      "1.0     2263\n",
      "0.2      129\n",
      "2.0       60\n",
      "0.4        9\n",
      "0.6        4\n",
      "3.0        4\n",
      "0.8        1\n",
      "5.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_92\n",
      "bureau_92\n",
      "0.0     72408\n",
      "1.0      3022\n",
      "2.0       984\n",
      "3.0       372\n",
      "4.0       180\n",
      "0.2       129\n",
      "5.0       111\n",
      "0.4        56\n",
      "6.0        38\n",
      "0.6        32\n",
      "7.0        25\n",
      "8.0        20\n",
      "9.0        17\n",
      "10.0        9\n",
      "0.8         6\n",
      "11.0        5\n",
      "12.0        3\n",
      "15.0        3\n",
      "14.0        3\n",
      "1.8         3\n",
      "16.0        2\n",
      "2.2         2\n",
      "1.2         2\n",
      "1.4         2\n",
      "1.6         2\n",
      "21.0        1\n",
      "27.0        1\n",
      "26.0        1\n",
      "13.0        1\n",
      "19.0        1\n",
      "18.0        1\n",
      "17.0        1\n",
      "31.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_93\n",
      "bureau_93\n",
      "0.0     29082\n",
      "1.0     26471\n",
      "2.0     11900\n",
      "3.0      5452\n",
      "4.0      2287\n",
      "5.0       910\n",
      "6.0       352\n",
      "0.8       176\n",
      "7.0       142\n",
      "0.6       137\n",
      "0.4       121\n",
      "1.2       100\n",
      "0.2        92\n",
      "1.4        53\n",
      "8.0        45\n",
      "1.6        39\n",
      "1.8        29\n",
      "9.0        16\n",
      "2.2        15\n",
      "2.4         7\n",
      "2.6         6\n",
      "10.0        4\n",
      "12.0        2\n",
      "3.2         2\n",
      "11.0        1\n",
      "2.8         1\n",
      "4.6         1\n",
      "13.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_94\n",
      "bureau_94\n",
      "0.0     62057\n",
      "1.0     10835\n",
      "2.0      2509\n",
      "3.0       786\n",
      "4.0       301\n",
      "0.2       292\n",
      "5.0       163\n",
      "0.4       161\n",
      "6.0        79\n",
      "0.6        76\n",
      "0.8        44\n",
      "7.0        32\n",
      "8.0        22\n",
      "9.0        18\n",
      "10.0       15\n",
      "1.2        14\n",
      "1.4         5\n",
      "12.0        5\n",
      "1.6         4\n",
      "11.0        4\n",
      "14.0        4\n",
      "1.8         3\n",
      "16.0        3\n",
      "15.0        2\n",
      "26.0        1\n",
      "2.6         1\n",
      "27.0        1\n",
      "19.0        1\n",
      "21.0        1\n",
      "13.0        1\n",
      "18.0        1\n",
      "17.0        1\n",
      "2.4         1\n",
      "31.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_95\n",
      "bureau_95\n",
      "1.0     19480\n",
      "0.0     17386\n",
      "2.0     13441\n",
      "3.0      9275\n",
      "4.0      6055\n",
      "        ...  \n",
      "7.6         1\n",
      "7.2         1\n",
      "6.8         1\n",
      "6.6         1\n",
      "82.0        1\n",
      "Name: count, Length: 76, dtype: int64\n",
      "---------------------\n",
      "bureau_96\n",
      "bureau_96\n",
      "1.0     18301\n",
      "0.0     14806\n",
      "2.0     13580\n",
      "3.0      9826\n",
      "4.0      6722\n",
      "        ...  \n",
      "5.2         1\n",
      "9.8         1\n",
      "6.6         1\n",
      "6.4         1\n",
      "83.0        1\n",
      "Name: count, Length: 77, dtype: int64\n",
      "---------------------\n",
      "bureau_97\n",
      "bureau_97\n",
      "0.0    72239\n",
      "1.0     4640\n",
      "2.0      271\n",
      "0.2      222\n",
      "3.0       33\n",
      "0.4       28\n",
      "4.0        4\n",
      "0.6        3\n",
      "5.0        1\n",
      "6.0        1\n",
      "7.0        1\n",
      "8.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_98\n",
      "bureau_98\n",
      "0.0     43585\n",
      "1.0     16572\n",
      "2.0      7837\n",
      "3.0      3550\n",
      "4.0      1692\n",
      "        ...  \n",
      "4.2         1\n",
      "8.8         1\n",
      "39.0        1\n",
      "7.2         1\n",
      "82.0        1\n",
      "Name: count, Length: 79, dtype: int64\n",
      "---------------------\n",
      "bureau_99\n",
      "bureau_99\n",
      "0.0    71827\n",
      "1.0     4679\n",
      "2.0      565\n",
      "0.2      225\n",
      "3.0       61\n",
      "0.4       59\n",
      "0.6       13\n",
      "4.0        7\n",
      "0.8        4\n",
      "5.0        2\n",
      "1.4        1\n",
      "7.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_101\n",
      "bureau_101\n",
      "0.0    71404\n",
      "1.0     5373\n",
      "2.0      300\n",
      "0.2      249\n",
      "0.4       55\n",
      "3.0       37\n",
      "0.6       13\n",
      "4.0        9\n",
      "0.8        2\n",
      "5.0        1\n",
      "6.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_102\n",
      "bureau_102\n",
      "0.0     70712\n",
      "1.0      2995\n",
      "2.0      1520\n",
      "3.0       744\n",
      "4.0       413\n",
      "5.0       220\n",
      "6.0       154\n",
      "7.0       118\n",
      "0.2       114\n",
      "0.4        79\n",
      "8.0        64\n",
      "9.0        47\n",
      "10.0       43\n",
      "0.6        43\n",
      "0.8        25\n",
      "11.0       23\n",
      "12.0       19\n",
      "14.0       13\n",
      "13.0       10\n",
      "15.0        9\n",
      "1.2         9\n",
      "1.6         7\n",
      "1.4         7\n",
      "19.0        6\n",
      "16.0        6\n",
      "17.0        5\n",
      "18.0        5\n",
      "20.0        4\n",
      "2.6         3\n",
      "1.8         3\n",
      "25.0        2\n",
      "33.0        2\n",
      "2.2         2\n",
      "27.0        2\n",
      "3.4         2\n",
      "2.4         1\n",
      "21.0        1\n",
      "22.0        1\n",
      "26.0        1\n",
      "3.6         1\n",
      "29.0        1\n",
      "31.0        1\n",
      "2.8         1\n",
      "34.0        1\n",
      "36.0        1\n",
      "41.0        1\n",
      "44.0        1\n",
      "46.0        1\n",
      "58.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_103\n",
      "bureau_103\n",
      "1.0     21442\n",
      "0.0     17134\n",
      "2.0     14461\n",
      "3.0      9321\n",
      "4.0      5881\n",
      "5.0      3628\n",
      "6.0      2194\n",
      "7.0      1177\n",
      "8.0       669\n",
      "9.0       373\n",
      "10.0      196\n",
      "1.2       103\n",
      "1.4       103\n",
      "1.6        94\n",
      "11.0       89\n",
      "1.8        84\n",
      "0.8        70\n",
      "2.2        54\n",
      "12.0       53\n",
      "0.6        49\n",
      "2.4        49\n",
      "2.6        38\n",
      "0.4        33\n",
      "2.8        27\n",
      "3.2        21\n",
      "13.0       20\n",
      "0.2        17\n",
      "3.4        16\n",
      "14.0       11\n",
      "3.6        10\n",
      "3.8         8\n",
      "15.0        4\n",
      "16.0        4\n",
      "4.8         3\n",
      "4.2         3\n",
      "4.4         2\n",
      "6.2         1\n",
      "4.6         1\n",
      "18.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_104\n",
      "bureau_104\n",
      "0.0     53188\n",
      "1.0     15004\n",
      "2.0      4662\n",
      "3.0      1776\n",
      "4.0       854\n",
      "5.0       415\n",
      "6.0       261\n",
      "0.2       219\n",
      "0.4       185\n",
      "7.0       182\n",
      "0.6       120\n",
      "8.0        95\n",
      "0.8        80\n",
      "9.0        72\n",
      "10.0       55\n",
      "1.2        38\n",
      "11.0       33\n",
      "12.0       33\n",
      "1.4        28\n",
      "13.0       20\n",
      "1.6        15\n",
      "14.0       12\n",
      "1.8        11\n",
      "15.0       10\n",
      "19.0        9\n",
      "16.0        9\n",
      "2.6         7\n",
      "2.4         6\n",
      "20.0        5\n",
      "18.0        5\n",
      "2.8         4\n",
      "17.0        4\n",
      "2.2         3\n",
      "27.0        2\n",
      "33.0        2\n",
      "26.0        2\n",
      "4.2         1\n",
      "3.8         1\n",
      "47.0        1\n",
      "44.0        1\n",
      "41.0        1\n",
      "37.0        1\n",
      "36.0        1\n",
      "3.2         1\n",
      "31.0        1\n",
      "29.0        1\n",
      "4.4         1\n",
      "5.4         1\n",
      "25.0        1\n",
      "24.0        1\n",
      "23.0        1\n",
      "22.0        1\n",
      "21.0        1\n",
      "59.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_105\n",
      "bureau_105\n",
      "1.0     12191\n",
      "2.0     10071\n",
      "0.0      8762\n",
      "3.0      8634\n",
      "4.0      7336\n",
      "        ...  \n",
      "13.6        1\n",
      "12.6        1\n",
      "10.4        1\n",
      "9.4         1\n",
      "95.0        1\n",
      "Name: count, Length: 117, dtype: int64\n",
      "---------------------\n",
      "bureau_106\n",
      "bureau_106\n",
      "1.0     10869\n",
      "2.0      9509\n",
      "3.0      8454\n",
      "4.0      7361\n",
      "0.0      6923\n",
      "        ...  \n",
      "10.6        1\n",
      "10.4        1\n",
      "10.2        1\n",
      "9.6         1\n",
      "96.0        1\n",
      "Name: count, Length: 127, dtype: int64\n",
      "---------------------\n",
      "bureau_107\n",
      "bureau_107\n",
      "0.0     69570\n",
      "1.0      6809\n",
      "2.0       577\n",
      "0.2       266\n",
      "3.0       102\n",
      "0.4        75\n",
      "4.0        14\n",
      "0.6        13\n",
      "5.0         7\n",
      "0.8         5\n",
      "7.0         2\n",
      "8.0         2\n",
      "6.0         1\n",
      "14.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_108\n",
      "bureau_108\n",
      "0.0     38451\n",
      "1.0     15479\n",
      "2.0      9105\n",
      "3.0      5145\n",
      "4.0      2823\n",
      "        ...  \n",
      "9.4         1\n",
      "5.6         1\n",
      "8.6         1\n",
      "8.8         1\n",
      "90.0        1\n",
      "Name: count, Length: 92, dtype: int64\n",
      "---------------------\n",
      "bureau_109\n",
      "bureau_109\n",
      "0.0     69639\n",
      "1.0      6210\n",
      "2.0      1004\n",
      "0.2       272\n",
      "3.0       155\n",
      "0.4        87\n",
      "4.0        33\n",
      "0.6        28\n",
      "0.8         6\n",
      "5.0         4\n",
      "6.0         2\n",
      "1.2         1\n",
      "1.4         1\n",
      "2.2         1\n",
      "11.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_111\n",
      "bureau_111\n",
      "0.0    67023\n",
      "1.0     8813\n",
      "2.0      968\n",
      "0.2      301\n",
      "0.4      134\n",
      "3.0      123\n",
      "0.6       39\n",
      "4.0       22\n",
      "5.0       10\n",
      "0.8        9\n",
      "7.0        1\n",
      "8.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_112\n",
      "bureau_112\n",
      "0.0     69574\n",
      "1.0      3151\n",
      "2.0      1650\n",
      "3.0       985\n",
      "4.0       568\n",
      "5.0       336\n",
      "6.0       232\n",
      "7.0       151\n",
      "8.0       118\n",
      "0.2       105\n",
      "0.4        86\n",
      "9.0        71\n",
      "0.6        64\n",
      "10.0       54\n",
      "11.0       40\n",
      "12.0       30\n",
      "0.8        30\n",
      "14.0       27\n",
      "15.0       23\n",
      "13.0       21\n",
      "1.4        14\n",
      "1.2        13\n",
      "16.0       12\n",
      "18.0       10\n",
      "17.0       10\n",
      "1.6         7\n",
      "20.0        6\n",
      "19.0        5\n",
      "23.0        4\n",
      "2.6         4\n",
      "36.0        3\n",
      "25.0        3\n",
      "24.0        3\n",
      "22.0        3\n",
      "46.0        3\n",
      "38.0        3\n",
      "3.2         2\n",
      "21.0        2\n",
      "4.8         2\n",
      "32.0        2\n",
      "2.4         2\n",
      "1.8         2\n",
      "33.0        2\n",
      "37.0        1\n",
      "34.0        1\n",
      "2.8         1\n",
      "31.0        1\n",
      "30.0        1\n",
      "29.0        1\n",
      "7.6         1\n",
      "4.2         1\n",
      "3.8         1\n",
      "3.6         1\n",
      "66.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_113\n",
      "bureau_113\n",
      "1.0     17246\n",
      "2.0     13716\n",
      "0.0     11365\n",
      "3.0     10300\n",
      "4.0      7533\n",
      "5.0      5322\n",
      "6.0      3779\n",
      "7.0      2622\n",
      "8.0      1720\n",
      "9.0      1136\n",
      "10.0      791\n",
      "11.0      480\n",
      "12.0      241\n",
      "13.0      182\n",
      "14.0      121\n",
      "1.8        89\n",
      "2.2        84\n",
      "1.4        67\n",
      "2.4        57\n",
      "2.8        55\n",
      "1.6        55\n",
      "3.2        54\n",
      "2.6        53\n",
      "15.0       50\n",
      "1.2        38\n",
      "3.4        31\n",
      "3.6        29\n",
      "3.8        27\n",
      "16.0       22\n",
      "4.4        21\n",
      "0.6        21\n",
      "0.8        20\n",
      "4.6        16\n",
      "4.2        14\n",
      "19.0       11\n",
      "18.0       11\n",
      "17.0       11\n",
      "4.8        10\n",
      "0.4         8\n",
      "0.2         7\n",
      "20.0        6\n",
      "5.4         4\n",
      "5.6         3\n",
      "6.8         3\n",
      "5.2         3\n",
      "21.0        3\n",
      "6.4         2\n",
      "6.2         2\n",
      "5.8         2\n",
      "6.6         1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_114\n",
      "bureau_114\n",
      "0.0     45435\n",
      "1.0     18002\n",
      "2.0      6649\n",
      "3.0      2820\n",
      "4.0      1457\n",
      "        ...  \n",
      "4.2         1\n",
      "4.4         1\n",
      "4.8         1\n",
      "5.2         1\n",
      "67.0        1\n",
      "Name: count, Length: 66, dtype: int64\n",
      "---------------------\n",
      "bureau_115\n",
      "bureau_115\n",
      "1.0      8841\n",
      "2.0      7715\n",
      "3.0      6950\n",
      "4.0      6367\n",
      "5.0      5549\n",
      "         ... \n",
      "16.6        1\n",
      "15.4        1\n",
      "14.6        1\n",
      "12.6        1\n",
      "118.0       1\n",
      "Name: count, Length: 150, dtype: int64\n",
      "---------------------\n",
      "bureau_116\n",
      "bureau_116\n",
      "1.0      7614\n",
      "2.0      7089\n",
      "3.0      6615\n",
      "4.0      6097\n",
      "5.0      5571\n",
      "         ... \n",
      "18.2        1\n",
      "18.6        1\n",
      "18.8        1\n",
      "20.2        1\n",
      "122.0       1\n",
      "Name: count, Length: 160, dtype: int64\n",
      "---------------------\n",
      "bureau_117\n",
      "bureau_117\n",
      "0.0      58241\n",
      "8.0        251\n",
      "6.0        246\n",
      "20.0       235\n",
      "54.0       215\n",
      "         ...  \n",
      "51.2         1\n",
      "4.8          1\n",
      "52.4         1\n",
      "53.2         1\n",
      "336.0        1\n",
      "Name: count, Length: 526, dtype: int64\n",
      "---------------------\n",
      "bureau_118\n",
      "bureau_118\n",
      "0.0      31719\n",
      "18.0      1128\n",
      "19.0      1124\n",
      "23.0       914\n",
      "21.0       895\n",
      "         ...  \n",
      "69.6         1\n",
      "70.2         1\n",
      "72.6         1\n",
      "73.4         1\n",
      "407.0        1\n",
      "Name: count, Length: 566, dtype: int64\n",
      "---------------------\n",
      "bureau_119\n",
      "bureau_119\n",
      "0.0     58830\n",
      "6.0       254\n",
      "5.0       225\n",
      "4.0       220\n",
      "8.0       201\n",
      "        ...  \n",
      "53.4        1\n",
      "53.2        1\n",
      "51.8        1\n",
      "51.4        1\n",
      "84.8        1\n",
      "Name: count, Length: 639, dtype: int64\n",
      "---------------------\n",
      "bureau_121\n",
      "bureau_121\n",
      "0.0     51368\n",
      "44.0      425\n",
      "31.0      385\n",
      "20.0      368\n",
      "32.0      351\n",
      "        ...  \n",
      "54.8        1\n",
      "52.4        1\n",
      "52.2        1\n",
      "51.8        1\n",
      "66.6        1\n",
      "Name: count, Length: 552, dtype: int64\n",
      "---------------------\n",
      "bureau_122\n",
      "bureau_122\n",
      "0.0      65707\n",
      "26.0       236\n",
      "24.0       216\n",
      "25.0       207\n",
      "10.0       190\n",
      "         ...  \n",
      "27.8         1\n",
      "28.2         1\n",
      "29.2         1\n",
      "29.8         1\n",
      "231.0        1\n",
      "Name: count, Length: 323, dtype: int64\n",
      "---------------------\n",
      "bureau_123\n",
      "bureau_123\n",
      "0.0      4297\n",
      "5.0      1289\n",
      "27.0     1141\n",
      "30.0     1027\n",
      "39.0     1022\n",
      "         ... \n",
      "83.4        1\n",
      "82.8        1\n",
      "82.4        1\n",
      "82.2        1\n",
      "622.0       1\n",
      "Name: count, Length: 812, dtype: int64\n",
      "---------------------\n",
      "bureau_124\n",
      "bureau_124\n",
      "0.0     25098\n",
      "44.0      568\n",
      "56.0      522\n",
      "20.0      509\n",
      "39.0      487\n",
      "        ...  \n",
      "76.2        1\n",
      "75.6        1\n",
      "74.6        1\n",
      "73.6        1\n",
      "90.8        1\n",
      "Name: count, Length: 763, dtype: int64\n",
      "---------------------\n",
      "bureau_125\n",
      "bureau_125\n",
      "44.0      1080\n",
      "0.0        999\n",
      "56.0       955\n",
      "50.0       906\n",
      "49.0       897\n",
      "          ... \n",
      "95.2         1\n",
      "201.8        1\n",
      "93.8         1\n",
      "91.2         1\n",
      "1212.0       1\n",
      "Name: count, Length: 814, dtype: int64\n",
      "---------------------\n",
      "bureau_126\n",
      "bureau_126\n",
      "44.0      907\n",
      "56.0      851\n",
      "45.0      771\n",
      "50.0      754\n",
      "47.0      746\n",
      "         ... \n",
      "229.8       1\n",
      "97.4        1\n",
      "97.2        1\n",
      "96.4        1\n",
      "1212.0      1\n",
      "Name: count, Length: 821, dtype: int64\n",
      "---------------------\n",
      "bureau_127\n",
      "bureau_127\n",
      "0.0       985\n",
      "44.0      930\n",
      "45.0      825\n",
      "56.0      792\n",
      "47.0      785\n",
      "         ... \n",
      "97.2        1\n",
      "96.8        1\n",
      "96.4        1\n",
      "191.2       1\n",
      "1212.0      1\n",
      "Name: count, Length: 822, dtype: int64\n",
      "---------------------\n",
      "bureau_128\n",
      "bureau_128\n",
      "0.0      77339\n",
      "8.0          5\n",
      "32.0         4\n",
      "16.0         4\n",
      "71.0         3\n",
      "         ...  \n",
      "46.0         1\n",
      "48.0         1\n",
      "53.0         1\n",
      "56.0         1\n",
      "250.0        1\n",
      "Name: count, Length: 72, dtype: int64\n",
      "---------------------\n",
      "bureau_129\n",
      "bureau_129\n",
      "0.0      77231\n",
      "6.0         14\n",
      "3.0         12\n",
      "18.0        12\n",
      "8.0         10\n",
      "         ...  \n",
      "3.8          1\n",
      "3.6          1\n",
      "33.0         1\n",
      "3.4          1\n",
      "173.0        1\n",
      "Name: count, Length: 73, dtype: int64\n",
      "---------------------\n",
      "bureau_130\n",
      "bureau_130\n",
      "0.0      77130\n",
      "7.0         12\n",
      "6.0         11\n",
      "9.0         10\n",
      "17.0         7\n",
      "         ...  \n",
      "60.0         1\n",
      "57.0         1\n",
      "55.0         1\n",
      "51.0         1\n",
      "306.0        1\n",
      "Name: count, Length: 153, dtype: int64\n",
      "---------------------\n",
      "bureau_132\n",
      "bureau_132\n",
      "0.0      77296\n",
      "19.0         8\n",
      "24.0         6\n",
      "7.0          5\n",
      "32.0         4\n",
      "         ...  \n",
      "57.0         1\n",
      "58.0         1\n",
      "59.0         1\n",
      "60.0         1\n",
      "198.0        1\n",
      "Name: count, Length: 80, dtype: int64\n",
      "---------------------\n",
      "bureau_133\n",
      "bureau_133\n",
      "0.0      77374\n",
      "4.0          5\n",
      "9.0          4\n",
      "7.0          3\n",
      "16.0         2\n",
      "48.0         2\n",
      "47.0         2\n",
      "43.0         2\n",
      "40.0         2\n",
      "38.0         2\n",
      "31.0         2\n",
      "19.0         2\n",
      "37.0         2\n",
      "11.0         2\n",
      "8.0          2\n",
      "6.0          2\n",
      "56.0         1\n",
      "58.0         1\n",
      "59.0         1\n",
      "61.0         1\n",
      "62.0         1\n",
      "64.0         1\n",
      "72.0         1\n",
      "52.0         1\n",
      "75.0         1\n",
      "83.0         1\n",
      "84.0         1\n",
      "85.0         1\n",
      "90.0         1\n",
      "91.0         1\n",
      "108.0        1\n",
      "53.0         1\n",
      "3.2          1\n",
      "3.0          1\n",
      "27.0         1\n",
      "18.0         1\n",
      "10.0         1\n",
      "21.0         1\n",
      "24.0         1\n",
      "25.0         1\n",
      "26.0         1\n",
      "30.0         1\n",
      "17.0         1\n",
      "35.0         1\n",
      "1.0          1\n",
      "5.0          1\n",
      "3.6          1\n",
      "45.0         1\n",
      "46.0         1\n",
      "111.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_134\n",
      "bureau_134\n",
      "0.0      71181\n",
      "5.0       1250\n",
      "4.0        808\n",
      "3.0        272\n",
      "7.0        251\n",
      "         ...  \n",
      "41.2         1\n",
      "34.8         1\n",
      "34.2         1\n",
      "32.4         1\n",
      "287.0        1\n",
      "Name: count, Length: 294, dtype: int64\n",
      "---------------------\n",
      "bureau_135\n",
      "bureau_135\n",
      "0.0      76370\n",
      "8.0         41\n",
      "9.0         38\n",
      "7.0         33\n",
      "6.0         32\n",
      "         ...  \n",
      "88.0         1\n",
      "1.8          1\n",
      "77.0         1\n",
      "0.6          1\n",
      "344.0        1\n",
      "Name: count, Length: 217, dtype: int64\n",
      "---------------------\n",
      "bureau_136\n",
      "bureau_136\n",
      "0.0      51552\n",
      "5.0       4855\n",
      "4.0       4782\n",
      "3.0       2689\n",
      "7.0       1442\n",
      "         ...  \n",
      "28.6         1\n",
      "12.2         1\n",
      "30.2         1\n",
      "31.2         1\n",
      "287.0        1\n",
      "Name: count, Length: 337, dtype: int64\n",
      "---------------------\n",
      "bureau_137\n",
      "bureau_137\n",
      "4.0      15666\n",
      "5.0      14245\n",
      "3.0       9379\n",
      "6.0       4656\n",
      "7.0       4341\n",
      "         ...  \n",
      "38.6         1\n",
      "38.4         1\n",
      "38.2         1\n",
      "37.6         1\n",
      "344.0        1\n",
      "Name: count, Length: 383, dtype: int64\n",
      "---------------------\n",
      "bureau_138\n",
      "bureau_138\n",
      "0.0      54786\n",
      "5.0       1495\n",
      "3.0       1471\n",
      "4.0       1452\n",
      "6.0       1377\n",
      "         ...  \n",
      "26.8         1\n",
      "27.2         1\n",
      "27.8         1\n",
      "28.8         1\n",
      "344.0        1\n",
      "Name: count, Length: 370, dtype: int64\n",
      "---------------------\n",
      "bureau_139\n",
      "bureau_139\n",
      "0.0         77061\n",
      "1.0             4\n",
      "5.0             3\n",
      "47.0            3\n",
      "590.0           3\n",
      "            ...  \n",
      "650.0           1\n",
      "649.0           1\n",
      "640.0           1\n",
      "634.0           1\n",
      "480890.0        1\n",
      "Name: count, Length: 359, dtype: int64\n",
      "---------------------\n",
      "bureau_140\n",
      "bureau_140\n",
      "0.0         76547\n",
      "472.0          10\n",
      "5.0             8\n",
      "19.0            7\n",
      "6.0             7\n",
      "            ...  \n",
      "1245.0          1\n",
      "1251.0          1\n",
      "1267.0          1\n",
      "1273.0          1\n",
      "557096.0        1\n",
      "Name: count, Length: 720, dtype: int64\n",
      "---------------------\n",
      "bureau_141\n",
      "bureau_141\n",
      "0.0         77129\n",
      "11800.0         4\n",
      "8.0             3\n",
      "600.0           3\n",
      "182.0           3\n",
      "            ...  \n",
      "3828.0          1\n",
      "3669.0          1\n",
      "3540.0          1\n",
      "3528.0          1\n",
      "446517.0        1\n",
      "Name: count, Length: 303, dtype: int64\n",
      "---------------------\n",
      "bureau_143\n",
      "bureau_143\n",
      "0.0        77136\n",
      "634.4          6\n",
      "86.6           3\n",
      "56.2           3\n",
      "13.0           3\n",
      "           ...  \n",
      "1119.0         1\n",
      "1158.0         1\n",
      "1164.0         1\n",
      "1172.0         1\n",
      "79303.0        1\n",
      "Name: count, Length: 284, dtype: int64\n",
      "---------------------\n",
      "bureau_144\n",
      "bureau_144\n",
      "0.0          77211\n",
      "354.0            3\n",
      "270.0            2\n",
      "236.0            2\n",
      "1240.0           2\n",
      "             ...  \n",
      "1481.0           1\n",
      "1497.0           1\n",
      "1506.0           1\n",
      "1552.0           1\n",
      "2659209.0        1\n",
      "Name: count, Length: 224, dtype: int64\n",
      "---------------------\n",
      "bureau_145\n",
      "bureau_145\n",
      "0.0          73748\n",
      "200.0           96\n",
      "100.0           84\n",
      "290.0           69\n",
      "40.0            69\n",
      "             ...  \n",
      "1459.0           1\n",
      "1444.0           1\n",
      "1441.0           1\n",
      "1437.6           1\n",
      "4971217.0        1\n",
      "Name: count, Length: 1817, dtype: int64\n",
      "---------------------\n",
      "bureau_146\n",
      "bureau_146\n",
      "0.0          75841\n",
      "1.0             12\n",
      "634.4            6\n",
      "20.0             6\n",
      "8.0              5\n",
      "             ...  \n",
      "2291.0           1\n",
      "2289.0           1\n",
      "2283.0           1\n",
      "2277.0           1\n",
      "3464755.0        1\n",
      "Name: count, Length: 1401, dtype: int64\n",
      "---------------------\n",
      "bureau_149\n",
      "bureau_149\n",
      "0.0    77189\n",
      "1.0      245\n",
      "0.2       10\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_150\n",
      "bureau_150\n",
      "0.0    76660\n",
      "1.0      743\n",
      "0.2       41\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_151\n",
      "bureau_151\n",
      "0.0    77105\n",
      "1.0      313\n",
      "0.2       25\n",
      "0.4        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_153\n",
      "bureau_153\n",
      "0.0    77241\n",
      "1.0      190\n",
      "0.2       13\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_154\n",
      "bureau_154\n",
      "0.0    77231\n",
      "1.0      205\n",
      "0.2        8\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_155\n",
      "bureau_155\n",
      "0.0    74096\n",
      "1.0     3149\n",
      "0.2      184\n",
      "0.4       15\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_156\n",
      "bureau_156\n",
      "0.0    76179\n",
      "1.0     1196\n",
      "0.2       66\n",
      "0.4        2\n",
      "0.6        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_157\n",
      "bureau_157\n",
      "0.0    72537\n",
      "1.0     4640\n",
      "0.2      235\n",
      "0.4       32\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_158\n",
      "bureau_158\n",
      "0.0    71464\n",
      "1.0     5667\n",
      "0.2      263\n",
      "0.4       47\n",
      "0.6        3\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_159\n",
      "bureau_159\n",
      "0.0    77276\n",
      "1.0      161\n",
      "0.2        7\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_160\n",
      "bureau_160\n",
      "0.0    77035\n",
      "1.0      389\n",
      "0.2       20\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_161\n",
      "bureau_161\n",
      "0.0    77252\n",
      "1.0      177\n",
      "0.2       15\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_163\n",
      "bureau_163\n",
      "0.0    77304\n",
      "1.0      137\n",
      "0.2        3\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_164\n",
      "bureau_164\n",
      "0.0    77315\n",
      "1.0      123\n",
      "0.2        6\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_165\n",
      "bureau_165\n",
      "0.0    76295\n",
      "1.0     1086\n",
      "0.2       61\n",
      "0.4        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_166\n",
      "bureau_166\n",
      "0.0    76680\n",
      "1.0      729\n",
      "0.2       33\n",
      "0.4        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_167\n",
      "bureau_167\n",
      "0.0    75296\n",
      "1.0     2034\n",
      "0.2      110\n",
      "0.4        4\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_168\n",
      "bureau_168\n",
      "0.0    74610\n",
      "1.0     2692\n",
      "0.2      132\n",
      "0.4       10\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_169\n",
      "bureau_169\n",
      "0.0    77324\n",
      "1.0      117\n",
      "0.2        3\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_170\n",
      "bureau_170\n",
      "0.0    77153\n",
      "1.0      279\n",
      "0.2       12\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_171\n",
      "bureau_171\n",
      "0.0    77324\n",
      "1.0      111\n",
      "0.2        9\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_173\n",
      "bureau_173\n",
      "0.0    77347\n",
      "1.0       95\n",
      "0.2        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_174\n",
      "bureau_174\n",
      "0.0    77339\n",
      "1.0      100\n",
      "0.2        5\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_175\n",
      "bureau_175\n",
      "0.0    76673\n",
      "1.0      729\n",
      "0.2       40\n",
      "0.4        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_176\n",
      "bureau_176\n",
      "0.0    76935\n",
      "1.0      489\n",
      "0.2       18\n",
      "0.4        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_177\n",
      "bureau_177\n",
      "0.0    75860\n",
      "1.0     1500\n",
      "0.2       81\n",
      "0.4        3\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_178\n",
      "bureau_178\n",
      "0.0    75393\n",
      "1.0     1951\n",
      "0.2       94\n",
      "0.4        6\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_179\n",
      "bureau_179\n",
      "0.0    77388\n",
      "1.0       55\n",
      "0.2        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_180\n",
      "bureau_180\n",
      "0.0    77275\n",
      "1.0      166\n",
      "0.2        3\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_181\n",
      "bureau_181\n",
      "0.0    77415\n",
      "1.0       27\n",
      "0.2        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_183\n",
      "bureau_183\n",
      "0.0    77363\n",
      "1.0       79\n",
      "0.2        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_185\n",
      "bureau_185\n",
      "0.0    76852\n",
      "1.0      562\n",
      "0.2       29\n",
      "0.4        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_186\n",
      "bureau_186\n",
      "0.0    77220\n",
      "1.0      216\n",
      "0.2        8\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_187\n",
      "bureau_187\n",
      "0.0    76343\n",
      "1.0     1046\n",
      "0.2       54\n",
      "0.4        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_188\n",
      "bureau_188\n",
      "0.0    76138\n",
      "1.0     1244\n",
      "0.2       61\n",
      "0.4        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_189\n",
      "bureau_189\n",
      "0.0    76542\n",
      "1.0      849\n",
      "0.2       52\n",
      "0.4        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_190\n",
      "bureau_190\n",
      "0.0    75340\n",
      "1.0     2007\n",
      "0.2       92\n",
      "0.4        5\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_191\n",
      "bureau_191\n",
      "0.0    76685\n",
      "1.0      704\n",
      "0.2       53\n",
      "0.4        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_193\n",
      "bureau_193\n",
      "0.0    76619\n",
      "1.0      789\n",
      "0.2       36\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_194\n",
      "bureau_194\n",
      "0.0    76045\n",
      "1.0     1330\n",
      "0.2       68\n",
      "0.4        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_195\n",
      "bureau_195\n",
      "0.0    69687\n",
      "1.0     7396\n",
      "0.2      286\n",
      "0.4       65\n",
      "0.6        9\n",
      "0.8        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_196\n",
      "bureau_196\n",
      "0.0    73191\n",
      "1.0     4040\n",
      "0.2      189\n",
      "0.4       23\n",
      "0.6        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_197\n",
      "bureau_197\n",
      "0.0    66377\n",
      "1.0    10583\n",
      "0.2      352\n",
      "0.4      112\n",
      "0.6       18\n",
      "0.8        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_198\n",
      "bureau_198\n",
      "0.0    63326\n",
      "1.0    13544\n",
      "0.2      385\n",
      "0.4      154\n",
      "0.6       29\n",
      "0.8        6\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_199\n",
      "bureau_199\n",
      "0.0    76928\n",
      "1.0      485\n",
      "0.2       30\n",
      "0.4        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_200\n",
      "bureau_200\n",
      "0.0    76433\n",
      "1.0      965\n",
      "0.2       45\n",
      "0.4        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_201\n",
      "bureau_201\n",
      "0.0    77020\n",
      "1.0      390\n",
      "0.2       34\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_203\n",
      "bureau_203\n",
      "0.0    76957\n",
      "1.0      475\n",
      "0.2       12\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_204\n",
      "bureau_204\n",
      "0.0    76801\n",
      "1.0      615\n",
      "0.2       27\n",
      "0.4        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_205\n",
      "bureau_205\n",
      "0.0    74581\n",
      "1.0     2710\n",
      "0.2      144\n",
      "0.4        8\n",
      "0.6        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_206\n",
      "bureau_206\n",
      "0.0    75159\n",
      "1.0     2176\n",
      "0.2      102\n",
      "0.4        7\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_207\n",
      "bureau_207\n",
      "0.0    72792\n",
      "1.0     4418\n",
      "0.2      214\n",
      "0.4       19\n",
      "0.6        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_208\n",
      "bureau_208\n",
      "0.0    70999\n",
      "1.0     6148\n",
      "0.2      254\n",
      "0.4       39\n",
      "0.6        4\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_209\n",
      "bureau_209\n",
      "0.0    77072\n",
      "1.0      350\n",
      "0.2       22\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_210\n",
      "bureau_210\n",
      "0.0    76660\n",
      "1.0      747\n",
      "0.2       37\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_211\n",
      "bureau_211\n",
      "0.0    77187\n",
      "1.0      240\n",
      "0.2       17\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_213\n",
      "bureau_213\n",
      "0.0    77075\n",
      "1.0      361\n",
      "0.2        8\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_214\n",
      "bureau_214\n",
      "0.0    77019\n",
      "1.0      405\n",
      "0.2       19\n",
      "0.4        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_215\n",
      "bureau_215\n",
      "0.0    75418\n",
      "1.0     1917\n",
      "0.2      102\n",
      "0.4        6\n",
      "0.6        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_216\n",
      "bureau_216\n",
      "0.0    75876\n",
      "1.0     1498\n",
      "0.2       67\n",
      "0.4        3\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_217\n",
      "bureau_217\n",
      "0.0    73959\n",
      "1.0     3306\n",
      "0.2      165\n",
      "0.4       13\n",
      "0.6        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_218\n",
      "bureau_218\n",
      "0.0    72709\n",
      "1.0     4517\n",
      "0.2      190\n",
      "0.4       26\n",
      "0.6        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_219\n",
      "bureau_219\n",
      "0.0    77218\n",
      "1.0      208\n",
      "0.2       18\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_220\n",
      "bureau_220\n",
      "0.0    76855\n",
      "1.0      566\n",
      "0.2       23\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_221\n",
      "bureau_221\n",
      "0.0    77370\n",
      "1.0       69\n",
      "0.2        5\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_223\n",
      "bureau_223\n",
      "0.0    77122\n",
      "1.0      317\n",
      "0.2        5\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_224\n",
      "bureau_224\n",
      "0.0    77250\n",
      "1.0      188\n",
      "0.2        6\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_225\n",
      "bureau_225\n",
      "0.0    75824\n",
      "1.0     1527\n",
      "0.2       89\n",
      "0.4        4\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_226\n",
      "bureau_226\n",
      "0.0    76542\n",
      "1.0      864\n",
      "0.2       38\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_227\n",
      "bureau_227\n",
      "0.0    74831\n",
      "1.0     2473\n",
      "0.2      131\n",
      "0.4        9\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_228\n",
      "bureau_228\n",
      "0.0    74127\n",
      "1.0     3157\n",
      "0.2      146\n",
      "0.4       14\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_229\n",
      "bureau_229\n",
      "0.0    76276\n",
      "1.0     1096\n",
      "0.2       71\n",
      "0.4        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_230\n",
      "bureau_230\n",
      "0.0    74527\n",
      "1.0     2778\n",
      "0.2      127\n",
      "0.4       12\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_231\n",
      "bureau_231\n",
      "0.0    76430\n",
      "1.0      944\n",
      "0.2       68\n",
      "0.4        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_233\n",
      "bureau_233\n",
      "0.0    76269\n",
      "1.0     1122\n",
      "0.2       53\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_234\n",
      "bureau_234\n",
      "0.0    75765\n",
      "1.0     1596\n",
      "0.2       81\n",
      "0.4        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_235\n",
      "bureau_235\n",
      "0.0    66374\n",
      "1.0    10589\n",
      "0.2      353\n",
      "0.4      105\n",
      "0.6       21\n",
      "0.8        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_236\n",
      "bureau_236\n",
      "0.0    72039\n",
      "1.0     5139\n",
      "0.2      228\n",
      "0.4       36\n",
      "0.6        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_237\n",
      "bureau_237\n",
      "0.0    62229\n",
      "1.0    14604\n",
      "0.2      401\n",
      "0.4      157\n",
      "0.6       44\n",
      "0.8        9\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_238\n",
      "bureau_238\n",
      "0.0    58722\n",
      "1.0    18035\n",
      "0.2      394\n",
      "0.4      208\n",
      "0.6       71\n",
      "0.8       14\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_239\n",
      "bureau_239\n",
      "0.0    76817\n",
      "1.0      591\n",
      "0.2       35\n",
      "0.4        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_240\n",
      "bureau_240\n",
      "0.0    76293\n",
      "1.0     1096\n",
      "0.2       54\n",
      "0.4        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_241\n",
      "bureau_241\n",
      "0.0    76893\n",
      "1.0      507\n",
      "0.2       44\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_243\n",
      "bureau_243\n",
      "0.0    76814\n",
      "1.0      610\n",
      "0.2       20\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_244\n",
      "bureau_244\n",
      "0.0    76695\n",
      "1.0      718\n",
      "0.2       30\n",
      "0.4        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_245\n",
      "bureau_245\n",
      "0.0    74081\n",
      "1.0     3184\n",
      "0.2      163\n",
      "0.4       15\n",
      "0.6        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_246\n",
      "bureau_246\n",
      "0.0    74694\n",
      "1.0     2621\n",
      "0.2      118\n",
      "0.4       11\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_247\n",
      "bureau_247\n",
      "0.0    72066\n",
      "1.0     5108\n",
      "0.2      242\n",
      "0.4       24\n",
      "0.6        4\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_248\n",
      "bureau_248\n",
      "0.0    69970\n",
      "1.0     7138\n",
      "0.2      276\n",
      "0.4       54\n",
      "0.6        5\n",
      "0.8        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_249\n",
      "bureau_249\n",
      "0.0    77001\n",
      "1.0      421\n",
      "0.2       22\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_250\n",
      "bureau_250\n",
      "0.0    76572\n",
      "1.0      830\n",
      "0.2       42\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_251\n",
      "bureau_251\n",
      "0.0    77145\n",
      "1.0      281\n",
      "0.2       18\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_253\n",
      "bureau_253\n",
      "0.0    76983\n",
      "1.0      452\n",
      "0.2        9\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_254\n",
      "bureau_254\n",
      "0.0    76952\n",
      "1.0      470\n",
      "0.2       21\n",
      "0.4        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_255\n",
      "bureau_255\n",
      "0.0    75093\n",
      "1.0     2225\n",
      "0.2      116\n",
      "0.4        9\n",
      "0.6        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_256\n",
      "bureau_256\n",
      "0.0    75608\n",
      "1.0     1763\n",
      "0.2       68\n",
      "0.4        5\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_257\n",
      "bureau_257\n",
      "0.0    73476\n",
      "1.0     3766\n",
      "0.2      182\n",
      "0.4       18\n",
      "0.6        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_258\n",
      "bureau_258\n",
      "0.0    72027\n",
      "1.0     5175\n",
      "0.2      207\n",
      "0.4       31\n",
      "0.6        4\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_259\n",
      "bureau_259\n",
      "0.0    77192\n",
      "1.0      234\n",
      "0.2       18\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_260\n",
      "bureau_260\n",
      "0.0    76779\n",
      "1.0      638\n",
      "0.2       27\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_261\n",
      "bureau_261\n",
      "0.0    77350\n",
      "1.0       89\n",
      "0.2        5\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_263\n",
      "bureau_263\n",
      "0.0    77042\n",
      "1.0      396\n",
      "0.2        6\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_264\n",
      "bureau_264\n",
      "0.0    77212\n",
      "1.0      225\n",
      "0.2        7\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_265\n",
      "bureau_265\n",
      "0.0    75557\n",
      "1.0     1776\n",
      "0.2      105\n",
      "0.4        6\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_266\n",
      "bureau_266\n",
      "0.0    76382\n",
      "1.0     1022\n",
      "0.2       39\n",
      "0.4        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_267\n",
      "bureau_267\n",
      "0.0    74455\n",
      "1.0     2827\n",
      "0.2      149\n",
      "0.4       12\n",
      "0.6        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_268\n",
      "bureau_268\n",
      "0.0    73623\n",
      "1.0     3637\n",
      "0.2      166\n",
      "0.4       16\n",
      "0.6        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_269\n",
      "bureau_269\n",
      "0.0    75767\n",
      "1.0     1578\n",
      "0.2       94\n",
      "0.4        5\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_270\n",
      "bureau_270\n",
      "0.0    73159\n",
      "1.0     4094\n",
      "0.2      166\n",
      "0.4       22\n",
      "0.6        3\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_271\n",
      "bureau_271\n",
      "0.0    75887\n",
      "1.0     1454\n",
      "0.2       97\n",
      "0.4        6\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_273\n",
      "bureau_273\n",
      "0.0    75664\n",
      "1.0     1697\n",
      "0.2       82\n",
      "0.4        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_274\n",
      "bureau_274\n",
      "0.0    75618\n",
      "1.0     1736\n",
      "0.2       87\n",
      "0.4        3\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_275\n",
      "bureau_275\n",
      "0.0    62166\n",
      "1.0    14675\n",
      "0.2      379\n",
      "0.4      175\n",
      "0.6       43\n",
      "0.8        6\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_276\n",
      "bureau_276\n",
      "0.0    70136\n",
      "1.0     6958\n",
      "0.2      292\n",
      "0.4       55\n",
      "0.6        3\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_277\n",
      "bureau_277\n",
      "0.0    56827\n",
      "1.0    19888\n",
      "0.2      384\n",
      "0.4      249\n",
      "0.6       77\n",
      "0.8       19\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_278\n",
      "bureau_278\n",
      "0.0    52657\n",
      "1.0    23995\n",
      "0.2      342\n",
      "0.4      306\n",
      "0.6      109\n",
      "0.8       35\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_279\n",
      "bureau_279\n",
      "0.0    76608\n",
      "1.0      791\n",
      "0.2       41\n",
      "0.4        4\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_280\n",
      "bureau_280\n",
      "0.0    76090\n",
      "1.0     1286\n",
      "0.2       65\n",
      "0.4        3\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_281\n",
      "bureau_281\n",
      "0.0    76623\n",
      "1.0      761\n",
      "0.2       58\n",
      "0.4        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_283\n",
      "bureau_283\n",
      "0.0    76620\n",
      "1.0      792\n",
      "0.2       32\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_284\n",
      "bureau_284\n",
      "0.0    76607\n",
      "1.0      803\n",
      "0.2       32\n",
      "0.4        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_285\n",
      "bureau_285\n",
      "0.0    73144\n",
      "1.0     4078\n",
      "0.2      203\n",
      "0.4       18\n",
      "0.6        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_286\n",
      "bureau_286\n",
      "0.0    73848\n",
      "1.0     3426\n",
      "0.2      151\n",
      "0.4       18\n",
      "0.6        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_287\n",
      "bureau_287\n",
      "0.0    70756\n",
      "1.0     6367\n",
      "0.2      273\n",
      "0.4       44\n",
      "0.6        4\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_288\n",
      "bureau_288\n",
      "0.0    68058\n",
      "1.0     8979\n",
      "0.2      314\n",
      "0.4       81\n",
      "0.6       11\n",
      "0.8        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_289\n",
      "bureau_289\n",
      "0.0    76935\n",
      "1.0      485\n",
      "0.2       23\n",
      "0.4        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_290\n",
      "bureau_290\n",
      "0.0    76523\n",
      "1.0      877\n",
      "0.2       44\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_291\n",
      "bureau_291\n",
      "0.0    77093\n",
      "1.0      332\n",
      "0.2       19\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_293\n",
      "bureau_293\n",
      "0.0    76914\n",
      "1.0      520\n",
      "0.2       10\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_294\n",
      "bureau_294\n",
      "0.0    76900\n",
      "1.0      520\n",
      "0.2       22\n",
      "0.4        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_295\n",
      "bureau_295\n",
      "0.0    74818\n",
      "1.0     2490\n",
      "0.2      125\n",
      "0.4       10\n",
      "0.6        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_296\n",
      "bureau_296\n",
      "0.0    75364\n",
      "1.0     2002\n",
      "0.2       71\n",
      "0.4        7\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_297\n",
      "bureau_297\n",
      "0.0    73089\n",
      "1.0     4144\n",
      "0.2      184\n",
      "0.4       25\n",
      "0.6        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_298\n",
      "bureau_298\n",
      "0.0    71485\n",
      "1.0     5705\n",
      "0.2      210\n",
      "0.4       39\n",
      "0.6        5\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_299\n",
      "bureau_299\n",
      "0.0    77154\n",
      "1.0      272\n",
      "0.2       17\n",
      "0.4        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_300\n",
      "bureau_300\n",
      "0.0    76747\n",
      "1.0      669\n",
      "0.2       28\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_301\n",
      "bureau_301\n",
      "0.0    77325\n",
      "1.0      113\n",
      "0.2        6\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_303\n",
      "bureau_303\n",
      "0.0    76998\n",
      "1.0      439\n",
      "0.2        7\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_304\n",
      "bureau_304\n",
      "0.0    77176\n",
      "1.0      260\n",
      "0.2        8\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_305\n",
      "bureau_305\n",
      "0.0    75329\n",
      "1.0     1997\n",
      "0.2      111\n",
      "0.4        7\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_306\n",
      "bureau_306\n",
      "0.0    76231\n",
      "1.0     1169\n",
      "0.2       42\n",
      "0.4        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_307\n",
      "bureau_307\n",
      "0.0    74158\n",
      "1.0     3116\n",
      "0.2      153\n",
      "0.4       16\n",
      "0.6        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_308\n",
      "bureau_308\n",
      "0.0    73227\n",
      "1.0     4023\n",
      "0.2      171\n",
      "0.4       21\n",
      "0.6        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_309\n",
      "bureau_309\n",
      "0.0    75027\n",
      "1.0     2276\n",
      "0.2      134\n",
      "0.4        7\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_310\n",
      "bureau_310\n",
      "0.0    72404\n",
      "1.0     4818\n",
      "0.2      191\n",
      "0.4       26\n",
      "0.6        5\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_311\n",
      "bureau_311\n",
      "0.0    74951\n",
      "1.0     2348\n",
      "0.2      132\n",
      "0.4       12\n",
      "0.6        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_313\n",
      "bureau_313\n",
      "0.0    74987\n",
      "1.0     2338\n",
      "0.2      111\n",
      "0.4        8\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_314\n",
      "bureau_314\n",
      "0.0    75507\n",
      "1.0     1836\n",
      "0.2       98\n",
      "0.4        3\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_315\n",
      "bureau_315\n",
      "0.0    55678\n",
      "1.0    21007\n",
      "0.2      367\n",
      "0.4      278\n",
      "0.6       98\n",
      "0.8       16\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_316\n",
      "bureau_316\n",
      "0.0    67804\n",
      "1.0     9198\n",
      "0.2      332\n",
      "0.4      102\n",
      "0.6        8\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_317\n",
      "bureau_317\n",
      "0.0    49858\n",
      "1.0    26749\n",
      "0.2      313\n",
      "0.4      305\n",
      "0.6      175\n",
      "0.8       44\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_318\n",
      "bureau_318\n",
      "0.0    45237\n",
      "1.0    31337\n",
      "0.4      324\n",
      "0.2      251\n",
      "0.6      222\n",
      "0.8       73\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_319\n",
      "bureau_319\n",
      "0.0    76205\n",
      "1.0     1167\n",
      "0.2       68\n",
      "0.4        4\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_320\n",
      "bureau_320\n",
      "0.0    75836\n",
      "1.0     1526\n",
      "0.2       78\n",
      "0.4        4\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_321\n",
      "bureau_321\n",
      "0.0    76092\n",
      "1.0     1269\n",
      "0.2       80\n",
      "0.4        3\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_323\n",
      "bureau_323\n",
      "0.0    76260\n",
      "1.0     1136\n",
      "0.2       47\n",
      "0.4        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_324\n",
      "bureau_324\n",
      "0.0    76543\n",
      "1.0      858\n",
      "0.2       41\n",
      "0.4        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_325\n",
      "bureau_325\n",
      "0.0    70453\n",
      "1.0     6638\n",
      "0.2      299\n",
      "0.4       48\n",
      "0.6        6\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_326\n",
      "bureau_326\n",
      "0.0    72438\n",
      "1.0     4765\n",
      "0.2      207\n",
      "0.4       32\n",
      "0.6        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_327\n",
      "bureau_327\n",
      "0.0    67294\n",
      "1.0     9674\n",
      "0.2      368\n",
      "0.4       92\n",
      "0.6       14\n",
      "0.8        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_328\n",
      "bureau_328\n",
      "0.0    63779\n",
      "1.0    13101\n",
      "0.2      370\n",
      "0.4      162\n",
      "0.6       27\n",
      "0.8        5\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_329\n",
      "bureau_329\n",
      "0.0    76696\n",
      "1.0      709\n",
      "0.2       38\n",
      "0.4        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_330\n",
      "bureau_330\n",
      "0.0    76376\n",
      "1.0     1016\n",
      "0.2       52\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_331\n",
      "bureau_331\n",
      "0.0    76877\n",
      "1.0      537\n",
      "0.2       30\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_333\n",
      "bureau_333\n",
      "0.0    76729\n",
      "1.0      696\n",
      "0.2       19\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_334\n",
      "bureau_334\n",
      "0.0    76860\n",
      "1.0      555\n",
      "0.2       27\n",
      "0.4        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_335\n",
      "bureau_335\n",
      "0.0    73304\n",
      "1.0     3920\n",
      "0.2      196\n",
      "0.4       22\n",
      "0.6        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_336\n",
      "bureau_336\n",
      "0.0    74625\n",
      "1.0     2695\n",
      "0.2      112\n",
      "0.4       12\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_337\n",
      "bureau_337\n",
      "0.0    70988\n",
      "1.0     6136\n",
      "0.2      266\n",
      "0.4       50\n",
      "0.6        3\n",
      "0.8        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_338\n",
      "bureau_338\n",
      "0.0    68900\n",
      "1.0     8171\n",
      "0.2      277\n",
      "0.4       85\n",
      "0.6       10\n",
      "0.8        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_339\n",
      "bureau_339\n",
      "0.0    77030\n",
      "1.0      392\n",
      "0.2       21\n",
      "0.4        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_340\n",
      "bureau_340\n",
      "0.0    76663\n",
      "1.0      749\n",
      "0.2       32\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_341\n",
      "bureau_341\n",
      "0.0    77254\n",
      "1.0      181\n",
      "0.2        9\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_343\n",
      "bureau_343\n",
      "0.0    76900\n",
      "1.0      534\n",
      "0.2       10\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_344\n",
      "bureau_344\n",
      "0.0    77162\n",
      "1.0      272\n",
      "0.2       10\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_345\n",
      "bureau_345\n",
      "0.0    74198\n",
      "1.0     3064\n",
      "0.2      163\n",
      "0.4       19\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_346\n",
      "bureau_346\n",
      "0.0    75893\n",
      "1.0     1493\n",
      "0.2       56\n",
      "0.4        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_347\n",
      "bureau_347\n",
      "0.0    72662\n",
      "1.0     4535\n",
      "0.2      209\n",
      "0.4       37\n",
      "0.6        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_348\n",
      "bureau_348\n",
      "0.0    71499\n",
      "1.0     5667\n",
      "0.2      231\n",
      "0.4       43\n",
      "0.6        4\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_349\n",
      "bureau_349\n",
      "0.0    74520\n",
      "1.0     2755\n",
      "0.2      157\n",
      "0.4       12\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_350\n",
      "bureau_350\n",
      "0.0    71994\n",
      "1.0     5215\n",
      "0.2      201\n",
      "0.4       29\n",
      "0.6        5\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_351\n",
      "bureau_351\n",
      "0.0    74311\n",
      "1.0     2955\n",
      "0.2      156\n",
      "0.4       20\n",
      "0.6        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_353\n",
      "bureau_353\n",
      "0.0    74639\n",
      "1.0     2670\n",
      "0.2      126\n",
      "0.4        9\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_354\n",
      "bureau_354\n",
      "0.0    75474\n",
      "1.0     1869\n",
      "0.2       98\n",
      "0.4        3\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_355\n",
      "bureau_355\n",
      "0.0    52188\n",
      "1.0    24442\n",
      "0.2      343\n",
      "0.4      304\n",
      "0.6      134\n",
      "0.8       33\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_356\n",
      "bureau_356\n",
      "0.0    66405\n",
      "1.0    10555\n",
      "0.2      348\n",
      "0.4      124\n",
      "0.6       10\n",
      "0.8        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_357\n",
      "bureau_357\n",
      "0.0    46285\n",
      "1.0    30285\n",
      "0.4      308\n",
      "0.2      279\n",
      "0.6      220\n",
      "0.8       67\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_358\n",
      "bureau_358\n",
      "0.0    41508\n",
      "1.0    35047\n",
      "0.4      309\n",
      "0.6      253\n",
      "0.2      215\n",
      "0.8      112\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_359\n",
      "bureau_359\n",
      "0.0    75914\n",
      "1.0     1443\n",
      "0.2       81\n",
      "0.4        6\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_360\n",
      "bureau_360\n",
      "0.0    75707\n",
      "1.0     1653\n",
      "0.2       80\n",
      "0.4        4\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_361\n",
      "bureau_361\n",
      "0.0    75707\n",
      "1.0     1633\n",
      "0.2       99\n",
      "0.4        5\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_363\n",
      "bureau_363\n",
      "0.0    76056\n",
      "1.0     1334\n",
      "0.2       51\n",
      "0.4        3\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_364\n",
      "bureau_364\n",
      "0.0    76516\n",
      "1.0      885\n",
      "0.2       41\n",
      "0.4        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_365\n",
      "bureau_365\n",
      "0.0    68558\n",
      "1.0     8460\n",
      "0.2      338\n",
      "0.4       78\n",
      "0.6       10\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_366\n",
      "bureau_366\n",
      "0.0    71540\n",
      "1.0     5636\n",
      "0.2      222\n",
      "0.4       42\n",
      "0.6        4\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_367\n",
      "bureau_367\n",
      "0.0    65053\n",
      "1.0    11836\n",
      "0.2      397\n",
      "0.4      134\n",
      "0.6       22\n",
      "0.8        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_368\n",
      "bureau_368\n",
      "0.0    61083\n",
      "1.0    15728\n",
      "0.2      369\n",
      "0.4      212\n",
      "0.6       46\n",
      "0.8        6\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_369\n",
      "bureau_369\n",
      "0.0    76506\n",
      "1.0      891\n",
      "0.2       45\n",
      "0.4        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_370\n",
      "bureau_370\n",
      "0.0    76311\n",
      "1.0     1081\n",
      "0.2       52\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_371\n",
      "bureau_371\n",
      "0.0    76680\n",
      "1.0      718\n",
      "0.2       46\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_373\n",
      "bureau_373\n",
      "0.0    76620\n",
      "1.0      799\n",
      "0.2       25\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_374\n",
      "bureau_374\n",
      "0.0    76846\n",
      "1.0      569\n",
      "0.2       27\n",
      "0.4        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_375\n",
      "bureau_375\n",
      "0.0    72161\n",
      "1.0     5017\n",
      "0.2      227\n",
      "0.4       37\n",
      "0.6        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_376\n",
      "bureau_376\n",
      "0.0    74114\n",
      "1.0     3184\n",
      "0.2      131\n",
      "0.4       14\n",
      "0.6        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_377\n",
      "bureau_377\n",
      "0.0    69536\n",
      "1.0     7531\n",
      "0.2      302\n",
      "0.4       71\n",
      "0.6        3\n",
      "0.8        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_378\n",
      "bureau_378\n",
      "0.0    67136\n",
      "1.0     9869\n",
      "0.2      316\n",
      "0.4      108\n",
      "0.6       14\n",
      "0.8        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_379\n",
      "bureau_379\n",
      "0.0    76921\n",
      "1.0      498\n",
      "0.2       24\n",
      "0.4        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_380\n",
      "bureau_380\n",
      "0.0    76626\n",
      "1.0      786\n",
      "0.2       32\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_381\n",
      "bureau_381\n",
      "0.0    77196\n",
      "1.0      236\n",
      "0.2       12\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_383\n",
      "bureau_383\n",
      "0.0    76835\n",
      "1.0      596\n",
      "0.2       13\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_384\n",
      "bureau_384\n",
      "0.0    77158\n",
      "1.0      276\n",
      "0.2       10\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_385\n",
      "bureau_385\n",
      "0.0    73370\n",
      "1.0     3858\n",
      "0.2      189\n",
      "0.4       27\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_386\n",
      "bureau_386\n",
      "0.0    75639\n",
      "1.0     1739\n",
      "0.2       62\n",
      "0.4        4\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_387\n",
      "bureau_387\n",
      "0.0    71640\n",
      "1.0     5517\n",
      "0.2      240\n",
      "0.4       46\n",
      "0.6        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_388\n",
      "bureau_388\n",
      "0.0    70314\n",
      "1.0     6810\n",
      "0.2      259\n",
      "0.4       57\n",
      "0.6        4\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_389\n",
      "bureau_389\n",
      "0.0       58241\n",
      "8.0         245\n",
      "6.0         242\n",
      "20.0        229\n",
      "18.0        195\n",
      "          ...  \n",
      "207.8         1\n",
      "19.2          1\n",
      "206.6         1\n",
      "19.4          1\n",
      "7704.0        1\n",
      "Name: count, Length: 1113, dtype: int64\n",
      "---------------------\n",
      "bureau_390\n",
      "bureau_390\n",
      "0.0       31719\n",
      "18.0        693\n",
      "19.0        674\n",
      "17.0        558\n",
      "4.0         544\n",
      "          ...  \n",
      "189.4         1\n",
      "189.2         1\n",
      "855.0         1\n",
      "856.0         1\n",
      "5348.0        1\n",
      "Name: count, Length: 1728, dtype: int64\n",
      "---------------------\n",
      "bureau_391\n",
      "bureau_391\n",
      "0.0       58830\n",
      "6.0         237\n",
      "5.0         213\n",
      "4.0         211\n",
      "8.0         190\n",
      "          ...  \n",
      "132.6         1\n",
      "655.0         1\n",
      "654.0         1\n",
      "55.2          1\n",
      "2981.0        1\n",
      "Name: count, Length: 1217, dtype: int64\n",
      "---------------------\n",
      "bureau_393\n",
      "bureau_393\n",
      "0.0       51368\n",
      "44.0        356\n",
      "31.0        352\n",
      "20.0        345\n",
      "32.0        323\n",
      "          ...  \n",
      "432.0         1\n",
      "63.8          1\n",
      "64.2          1\n",
      "64.8          1\n",
      "1485.0        1\n",
      "Name: count, Length: 941, dtype: int64\n",
      "---------------------\n",
      "bureau_394\n",
      "bureau_394\n",
      "0.0        65707\n",
      "6.0          149\n",
      "9.0          140\n",
      "3.0          138\n",
      "7.0          137\n",
      "           ...  \n",
      "671.0          1\n",
      "680.0          1\n",
      "186.2          1\n",
      "685.0          1\n",
      "12986.0        1\n",
      "Name: count, Length: 1370, dtype: int64\n",
      "---------------------\n",
      "bureau_395\n",
      "bureau_395\n",
      "0.0       4297\n",
      "5.0       1218\n",
      "4.0        832\n",
      "7.0        480\n",
      "27.0       399\n",
      "          ... \n",
      "100.4        1\n",
      "102.4        1\n",
      "1603.0       1\n",
      "1597.0       1\n",
      "7303.0       1\n",
      "Name: count, Length: 3496, dtype: int64\n",
      "---------------------\n",
      "bureau_396\n",
      "bureau_396\n",
      "0.0        25098\n",
      "8.0          399\n",
      "20.0         395\n",
      "44.0         375\n",
      "32.0         374\n",
      "           ...  \n",
      "40.4           1\n",
      "40.2           1\n",
      "39.8           1\n",
      "39.2           1\n",
      "13011.0        1\n",
      "Name: count, Length: 2341, dtype: int64\n",
      "---------------------\n",
      "bureau_397\n",
      "bureau_397\n",
      "0.0        999\n",
      "5.0        507\n",
      "4.0        350\n",
      "7.0        183\n",
      "26.0       157\n",
      "          ... \n",
      "201.8        1\n",
      "202.8        1\n",
      "204.2        1\n",
      "206.2        1\n",
      "11223.0      1\n",
      "Name: count, Length: 4281, dtype: int64\n",
      "---------------------\n",
      "bureau_398\n",
      "bureau_398\n",
      "5.0        288\n",
      "4.0        197\n",
      "7.0        151\n",
      "33.0       137\n",
      "79.0       132\n",
      "          ... \n",
      "372.4        1\n",
      "2886.0       1\n",
      "375.8        1\n",
      "2884.0       1\n",
      "18163.0      1\n",
      "Name: count, Length: 4959, dtype: int64\n",
      "---------------------\n",
      "bureau_399\n",
      "bureau_399\n",
      "0.0      67542\n",
      "8.0        332\n",
      "6.0        311\n",
      "20.0       294\n",
      "7.0        256\n",
      "         ...  \n",
      "149.0        1\n",
      "148.0        1\n",
      "147.0        1\n",
      "145.0        1\n",
      "688.0        1\n",
      "Name: count, Length: 296, dtype: int64\n",
      "---------------------\n",
      "bureau_400\n",
      "bureau_400\n",
      "0.0      40563\n",
      "18.0      1119\n",
      "4.0       1066\n",
      "19.0      1066\n",
      "6.0       1001\n",
      "         ...  \n",
      "52.4         1\n",
      "51.8         1\n",
      "48.2         1\n",
      "47.2         1\n",
      "809.0        1\n",
      "Name: count, Length: 464, dtype: int64\n",
      "---------------------\n",
      "bureau_401\n",
      "bureau_401\n",
      "0.0      63978\n",
      "6.0        337\n",
      "5.0        282\n",
      "4.0        277\n",
      "9.0        269\n",
      "         ...  \n",
      "65.6         1\n",
      "68.8         1\n",
      "69.2         1\n",
      "71.6         1\n",
      "910.0        1\n",
      "Name: count, Length: 520, dtype: int64\n",
      "---------------------\n",
      "bureau_403\n",
      "bureau_403\n",
      "0.0      69294\n",
      "20.0       394\n",
      "19.0       307\n",
      "7.0        283\n",
      "8.0        273\n",
      "         ...  \n",
      "13.8         1\n",
      "158.0        1\n",
      "155.0        1\n",
      "151.0        1\n",
      "668.0        1\n",
      "Name: count, Length: 372, dtype: int64\n",
      "---------------------\n",
      "bureau_404\n",
      "bureau_404\n",
      "0.0      72626\n",
      "6.0        353\n",
      "3.0        324\n",
      "9.0        317\n",
      "4.0        305\n",
      "         ...  \n",
      "111.0        1\n",
      "113.0        1\n",
      "116.0        1\n",
      "121.0        1\n",
      "461.0        1\n",
      "Name: count, Length: 179, dtype: int64\n",
      "---------------------\n",
      "bureau_405\n",
      "bureau_405\n",
      "0.0       5798\n",
      "5.0       1557\n",
      "4.0       1106\n",
      "7.0        542\n",
      "27.0       479\n",
      "          ... \n",
      "313.2        1\n",
      "313.8        1\n",
      "65.2         1\n",
      "317.4        1\n",
      "3512.0       1\n",
      "Name: count, Length: 1916, dtype: int64\n",
      "---------------------\n",
      "bureau_406\n",
      "bureau_406\n",
      "0.0       43299\n",
      "8.0         870\n",
      "6.0         846\n",
      "7.0         769\n",
      "9.0         737\n",
      "          ...  \n",
      "341.0         1\n",
      "44.8          1\n",
      "44.6          1\n",
      "44.2          1\n",
      "1071.0        1\n",
      "Name: count, Length: 724, dtype: int64\n",
      "---------------------\n",
      "bureau_407\n",
      "bureau_407\n",
      "0.0       2811\n",
      "5.0       1017\n",
      "4.0        700\n",
      "7.0        330\n",
      "63.0       330\n",
      "          ... \n",
      "97.6         1\n",
      "95.8         1\n",
      "95.6         1\n",
      "94.8         1\n",
      "3756.0       1\n",
      "Name: count, Length: 1992, dtype: int64\n",
      "---------------------\n",
      "bureau_408\n",
      "bureau_408\n",
      "0.0       1590\n",
      "5.0        747\n",
      "4.0        492\n",
      "75.0       314\n",
      "63.0       312\n",
      "          ... \n",
      "279.4        1\n",
      "279.2        1\n",
      "151.4        1\n",
      "151.6        1\n",
      "3756.0       1\n",
      "Name: count, Length: 2095, dtype: int64\n",
      "---------------------\n",
      "bureau_409\n",
      "bureau_409\n",
      "0.000000      67542\n",
      "1.000000        169\n",
      "2.000000        157\n",
      "3.000000        104\n",
      "0.500000         87\n",
      "              ...  \n",
      "0.372727          1\n",
      "1.950000          1\n",
      "0.381818          1\n",
      "0.383333          1\n",
      "216.000000        1\n",
      "Name: count, Length: 2339, dtype: int64\n",
      "---------------------\n",
      "bureau_410\n",
      "bureau_410\n",
      "0.000000      40563\n",
      "1.000000        827\n",
      "2.000000        638\n",
      "3.000000        412\n",
      "1.500000        383\n",
      "              ...  \n",
      "2.450980          1\n",
      "2.459016          1\n",
      "2.463415          1\n",
      "0.696429          1\n",
      "124.000000        1\n",
      "Name: count, Length: 4534, dtype: int64\n",
      "---------------------\n",
      "bureau_411\n",
      "bureau_411\n",
      "0.000000      63978\n",
      "1.000000        168\n",
      "2.000000        137\n",
      "3.000000        120\n",
      "0.500000        107\n",
      "              ...  \n",
      "2.628571          1\n",
      "2.631250          1\n",
      "2.655172          1\n",
      "2.655556          1\n",
      "159.000000        1\n",
      "Name: count, Length: 3628, dtype: int64\n",
      "---------------------\n",
      "bureau_413\n",
      "bureau_413\n",
      "0.000000      69294\n",
      "1.000000        159\n",
      "2.000000        121\n",
      "0.500000         78\n",
      "3.000000         75\n",
      "              ...  \n",
      "2.590909          1\n",
      "0.445946          1\n",
      "2.581395          1\n",
      "2.578947          1\n",
      "124.500000        1\n",
      "Name: count, Length: 2164, dtype: int64\n",
      "---------------------\n",
      "bureau_414\n",
      "bureau_414\n",
      "0.000000     72626\n",
      "0.500000       104\n",
      "1.000000        99\n",
      "0.333333        81\n",
      "0.250000        77\n",
      "             ...  \n",
      "0.452830         1\n",
      "0.457143         1\n",
      "0.457627         1\n",
      "0.459459         1\n",
      "31.000000        1\n",
      "Name: count, Length: 1232, dtype: int64\n",
      "---------------------\n",
      "bureau_415\n",
      "bureau_415\n",
      "0.000000      5798\n",
      "5.000000       650\n",
      "4.000000       533\n",
      "2.500000       511\n",
      "2.000000       506\n",
      "              ... \n",
      "5.931507         1\n",
      "5.938776         1\n",
      "5.939394         1\n",
      "18.730769        1\n",
      "244.000000       1\n",
      "Name: count, Length: 12640, dtype: int64\n",
      "---------------------\n",
      "bureau_416\n",
      "bureau_416\n",
      "0.000000      43299\n",
      "1.000000        532\n",
      "2.000000        431\n",
      "3.000000        344\n",
      "0.500000        280\n",
      "              ...  \n",
      "0.980392          1\n",
      "0.979592          1\n",
      "0.978261          1\n",
      "0.977273          1\n",
      "344.000000        1\n",
      "Name: count, Length: 5703, dtype: int64\n",
      "---------------------\n",
      "bureau_417\n",
      "bureau_417\n",
      "0.000000      2811\n",
      "5.000000       640\n",
      "4.000000       530\n",
      "7.000000       515\n",
      "8.000000       510\n",
      "              ... \n",
      "2.729730         1\n",
      "15.073506        1\n",
      "15.070556        1\n",
      "2.730769         1\n",
      "244.000000       1\n",
      "Name: count, Length: 13288, dtype: int64\n",
      "---------------------\n",
      "bureau_418\n",
      "bureau_418\n",
      "0.000000      1590\n",
      "5.000000       612\n",
      "7.000000       512\n",
      "8.000000       500\n",
      "9.000000       491\n",
      "              ... \n",
      "14.678571        1\n",
      "14.676471        1\n",
      "14.656250        1\n",
      "14.633333        1\n",
      "344.000000       1\n",
      "Name: count, Length: 13933, dtype: int64\n",
      "---------------------\n",
      "bureau_419\n",
      "bureau_419\n",
      "0.000000      3597\n",
      "8.000000       504\n",
      "9.000000       445\n",
      "7.000000       433\n",
      "6.000000       406\n",
      "              ... \n",
      "13.939394        1\n",
      "13.938533        1\n",
      "13.935484        1\n",
      "13.931034        1\n",
      "344.000000       1\n",
      "Name: count, Length: 13892, dtype: int64\n",
      "---------------------\n",
      "bureau_420\n",
      "bureau_420\n",
      "0.000000      58241\n",
      "2.000000        152\n",
      "1.000000        143\n",
      "3.000000        131\n",
      "4.000000        122\n",
      "              ...  \n",
      "0.829787          1\n",
      "6.650000          1\n",
      "0.828571          1\n",
      "6.678571          1\n",
      "250.000000        1\n",
      "Name: count, Length: 6322, dtype: int64\n",
      "---------------------\n",
      "bureau_421\n",
      "bureau_421\n",
      "0.000000      31719\n",
      "1.000000        477\n",
      "2.000000        445\n",
      "3.000000        389\n",
      "4.000000        377\n",
      "              ...  \n",
      "7.403846          1\n",
      "7.396226          1\n",
      "7.394737          1\n",
      "7.393939          1\n",
      "173.000000        1\n",
      "Name: count, Length: 9772, dtype: int64\n",
      "---------------------\n",
      "bureau_422\n",
      "bureau_422\n",
      "0.000000      58830\n",
      "1.000000        130\n",
      "3.000000        108\n",
      "2.000000        108\n",
      "4.000000         93\n",
      "              ...  \n",
      "7.409091          1\n",
      "7.422222          1\n",
      "7.423077          1\n",
      "7.431373          1\n",
      "306.000000        1\n",
      "Name: count, Length: 6466, dtype: int64\n",
      "---------------------\n",
      "bureau_424\n",
      "bureau_424\n",
      "0.000000      51368\n",
      "2.000000        200\n",
      "4.000000        192\n",
      "5.000000        190\n",
      "3.000000        173\n",
      "              ...  \n",
      "1.491228          1\n",
      "6.645161          1\n",
      "1.490513          1\n",
      "6.652174          1\n",
      "198.000000        1\n",
      "Name: count, Length: 6782, dtype: int64\n",
      "---------------------\n",
      "bureau_425\n",
      "bureau_425\n",
      "0.000000      65707\n",
      "1.000000        108\n",
      "2.000000         84\n",
      "0.500000         64\n",
      "3.000000         62\n",
      "              ...  \n",
      "5.387097          1\n",
      "5.387755          1\n",
      "5.388235          1\n",
      "5.388889          1\n",
      "118.500000        1\n",
      "Name: count, Length: 5507, dtype: int64\n",
      "---------------------\n",
      "bureau_426\n",
      "bureau_426\n",
      "0.000000      4297\n",
      "5.000000       581\n",
      "4.000000       464\n",
      "2.500000       411\n",
      "7.000000       405\n",
      "              ... \n",
      "28.391304        1\n",
      "3.484848         1\n",
      "28.401487        1\n",
      "28.409091        1\n",
      "287.000000       1\n",
      "Name: count, Length: 17362, dtype: int64\n",
      "---------------------\n",
      "bureau_427\n",
      "bureau_427\n",
      "0.000000      25098\n",
      "2.000000        318\n",
      "3.000000        304\n",
      "5.000000        281\n",
      "4.000000        274\n",
      "              ...  \n",
      "13.346154         1\n",
      "13.345455         1\n",
      "13.341667         1\n",
      "13.341463         1\n",
      "344.000000        1\n",
      "Name: count, Length: 12874, dtype: int64\n",
      "---------------------\n",
      "bureau_428\n",
      "bureau_428\n",
      "0.000000      999\n",
      "5.000000      346\n",
      "16.000000     297\n",
      "17.000000     284\n",
      "21.000000     284\n",
      "             ... \n",
      "23.306452       1\n",
      "48.920000       1\n",
      "48.923077       1\n",
      "48.925926       1\n",
      "287.000000      1\n",
      "Name: count, Length: 20078, dtype: int64\n",
      "---------------------\n",
      "bureau_429\n",
      "bureau_429\n",
      "5.000000      311\n",
      "17.000000     270\n",
      "24.000000     269\n",
      "25.000000     267\n",
      "21.000000     262\n",
      "             ... \n",
      "47.783784       1\n",
      "47.795455       1\n",
      "47.797143       1\n",
      "47.804878       1\n",
      "344.000000      1\n",
      "Name: count, Length: 21777, dtype: int64\n",
      "---------------------\n",
      "bureau_430\n",
      "bureau_430\n",
      "0.000000      985\n",
      "24.000000     267\n",
      "19.000000     261\n",
      "17.000000     259\n",
      "23.000000     256\n",
      "             ... \n",
      "45.157895       1\n",
      "45.173913       1\n",
      "45.180328       1\n",
      "45.185185       1\n",
      "344.000000      1\n",
      "Name: count, Length: 21873, dtype: int64\n",
      "---------------------\n",
      "bureau_431\n",
      "bureau_431\n",
      " 0.000000     2951\n",
      " 1.000000       74\n",
      "-0.000040        5\n",
      "-0.000031        4\n",
      " 0.500000        4\n",
      "              ... \n",
      " 0.204740        1\n",
      " 0.204693        1\n",
      " 0.204685        1\n",
      " 0.204665        1\n",
      " 50.312312       1\n",
      "Name: count, Length: 74267, dtype: int64\n",
      "---------------------\n",
      "bureau_432\n",
      "bureau_432\n",
      " 0.000000     4385\n",
      " 1.000000      105\n",
      " 0.200000       11\n",
      " 0.005765        8\n",
      "-0.000016        6\n",
      "              ... \n",
      " 0.194440        1\n",
      " 0.194445        1\n",
      " 0.194511        1\n",
      " 0.194521        1\n",
      " 50.312312       1\n",
      "Name: count, Length: 72556, dtype: int64\n",
      "---------------------\n",
      "bureau_439\n",
      "bureau_439\n",
      " 0.000000     3603\n",
      " 1.000000      171\n",
      "-0.000010       11\n",
      "-0.000020       10\n",
      "-0.000040        8\n",
      "              ... \n",
      " 0.098371        1\n",
      " 0.098371        1\n",
      " 0.098375        1\n",
      " 0.098390        1\n",
      " 46.075514       1\n",
      "Name: count, Length: 72746, dtype: int64\n",
      "---------------------\n",
      "bureau_441\n",
      "bureau_441\n",
      " 0.000000        3523\n",
      " 1.000000          84\n",
      "-0.000040           6\n",
      " 0.020350           5\n",
      "-0.000188           4\n",
      "                 ... \n",
      " 0.132108           1\n",
      " 0.132109           1\n",
      " 0.132114           1\n",
      " 0.132115           1\n",
      " 11114.400000       1\n",
      "Name: count, Length: 73479, dtype: int64\n",
      "---------------------\n",
      "bureau_442\n",
      "bureau_442\n",
      " 0.000000      1654\n",
      " 1.000000       200\n",
      " 0.003665         9\n",
      "-0.000040         7\n",
      " 0.666667         7\n",
      "               ... \n",
      " 0.352933         1\n",
      " 0.352912         1\n",
      " 0.352842         1\n",
      " 0.352842         1\n",
      " 233.225352       1\n",
      "Name: count, Length: 75120, dtype: int64\n",
      "---------------------\n",
      "bureau_443\n",
      "bureau_443\n",
      "0.000000     2159\n",
      "1.000000      309\n",
      "0.200000       14\n",
      "0.000121        9\n",
      "0.500000        9\n",
      "             ... \n",
      "0.348460        1\n",
      "0.348464        1\n",
      "0.348482        1\n",
      "0.348490        1\n",
      "50.312312       1\n",
      "Name: count, Length: 73991, dtype: int64\n",
      "---------------------\n",
      "bureau_450\n",
      "bureau_450\n",
      " 0.000000     2794\n",
      " 1.000000      220\n",
      "-0.000040       12\n",
      "-0.000067       10\n",
      "-0.000100        9\n",
      "              ... \n",
      " 0.119512        1\n",
      " 0.119513        1\n",
      " 0.119518        1\n",
      " 0.119540        1\n",
      " 78.986643       1\n",
      "Name: count, Length: 73367, dtype: int64\n",
      "---------------------\n",
      "bureau_452\n",
      "bureau_452\n",
      " 0.000000      2114\n",
      " 1.000000       228\n",
      " 0.003665         9\n",
      "-0.000040         9\n",
      " 0.500000         8\n",
      "               ... \n",
      " 0.215979         1\n",
      " 0.215968         1\n",
      " 0.215907         1\n",
      " 0.215879         1\n",
      " 262.236560       1\n",
      "Name: count, Length: 74445, dtype: int64\n",
      "---------------------\n",
      "onus_attribute_2\n",
      "onus_attribute_2\n",
      " 0.000000    10871\n",
      " 1.000000      124\n",
      "-0.000040       52\n",
      "-0.000010       42\n",
      "-0.000200       32\n",
      "             ...  \n",
      " 0.049699        1\n",
      " 0.049700        1\n",
      " 0.049706        1\n",
      " 0.049706        1\n",
      " 1.243536        1\n",
      "Name: count, Length: 60768, dtype: int64\n",
      "---------------------\n",
      "onus_attribute_5\n",
      "onus_attribute_5\n",
      "0.0           31293\n",
      "1.0            2700\n",
      "5000.0          363\n",
      "10000.0         346\n",
      "2000.0          257\n",
      "              ...  \n",
      "8436.0            1\n",
      "8435.0            1\n",
      "8432.0            1\n",
      "8427.0            1\n",
      "14490615.0        1\n",
      "Name: count, Length: 27738, dtype: int64\n",
      "---------------------\n",
      "onus_attribute_10\n",
      "onus_attribute_10\n",
      "3.0    43402\n",
      "1.0    17046\n",
      "2.0    16986\n",
      "4.0        8\n",
      "1.8        1\n",
      "2.4        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "onus_attribute_13\n",
      "onus_attribute_13\n",
      "4.0    24158\n",
      "1.0    17046\n",
      "2.0    16976\n",
      "3.0    14616\n",
      "5.0     4026\n",
      "6.0      621\n",
      "7.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "onus_attribute_16\n",
      "onus_attribute_16\n",
      "4.0    24158\n",
      "1.0    17046\n",
      "2.0    16976\n",
      "3.0    14616\n",
      "5.0     4026\n",
      "7.0      552\n",
      "6.0       69\n",
      "8.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "onus_attribute_17\n",
      "onus_attribute_17\n",
      "0.000000    4787\n",
      "0.333333      36\n",
      "0.500000      31\n",
      "1.000000      19\n",
      "0.003333      16\n",
      "            ... \n",
      "0.039099       1\n",
      "0.039099       1\n",
      "0.039100       1\n",
      "0.039100       1\n",
      "1.179903       1\n",
      "Name: count, Length: 70190, dtype: int64\n",
      "---------------------\n",
      "onus_attribute_18\n",
      "onus_attribute_18\n",
      " 0.0    76067\n",
      "-1.0     1126\n",
      "-2.0      212\n",
      "-3.0       39\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "onus_attribute_19\n",
      "onus_attribute_19\n",
      "3.0    60366\n",
      "2.0    17066\n",
      "4.0       11\n",
      "1.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "onus_attribute_20\n",
      "onus_attribute_20\n",
      "0.000000    2970\n",
      "0.500000      33\n",
      "0.333333      18\n",
      "0.200000      18\n",
      "0.400000      15\n",
      "            ... \n",
      "0.037570       1\n",
      "0.037571       1\n",
      "0.037573       1\n",
      "0.037574       1\n",
      "1.116656       1\n",
      "Name: count, Length: 72545, dtype: int64\n",
      "---------------------\n",
      "onus_attribute_21\n",
      "onus_attribute_21\n",
      " 0.0    75893\n",
      "-1.0     1213\n",
      "-2.0      267\n",
      "-3.0       64\n",
      "-4.0        5\n",
      "-5.0        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "onus_attribute_22\n",
      "onus_attribute_22\n",
      "5.0    24157\n",
      "2.0    17044\n",
      "3.0    16977\n",
      "4.0    14616\n",
      "6.0     4644\n",
      "7.0        5\n",
      "1.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "onus_attribute_23\n",
      "onus_attribute_23\n",
      "0.000000    2966\n",
      "0.500000      33\n",
      "0.200000      18\n",
      "0.333333      18\n",
      "0.400000      15\n",
      "            ... \n",
      "0.037570       1\n",
      "0.037570       1\n",
      "0.037571       1\n",
      "0.037573       1\n",
      "1.116656       1\n",
      "Name: count, Length: 72550, dtype: int64\n",
      "---------------------\n",
      "onus_attribute_24\n",
      "onus_attribute_24\n",
      " 0.0    75890\n",
      "-1.0     1215\n",
      "-2.0      264\n",
      "-3.0       67\n",
      "-4.0        6\n",
      "-5.0        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "onus_attribute_25\n",
      "onus_attribute_25\n",
      "5.0    24157\n",
      "2.0    17044\n",
      "3.0    16977\n",
      "4.0    14616\n",
      "6.0     4027\n",
      "8.0      551\n",
      "7.0       69\n",
      "9.0        2\n",
      "1.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "onus_attribute_31\n",
      "onus_attribute_31\n",
      "0.0    76448\n",
      "1.0      961\n",
      "2.0       35\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "onus_attribute_32\n",
      "onus_attribute_32\n",
      "0.0    58839\n",
      "1.0     8131\n",
      "2.0     5307\n",
      "3.0     5167\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "onus_attribute_33\n",
      "onus_attribute_33\n",
      "0.0    65068\n",
      "1.0     6320\n",
      "2.0     3639\n",
      "3.0     2417\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "onus_attribute_34\n",
      "onus_attribute_34\n",
      "0.0    75500\n",
      "1.0     1560\n",
      "2.0      384\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "onus_attribute_35\n",
      "onus_attribute_35\n",
      "0.0    65827\n",
      "1.0     5617\n",
      "2.0     3260\n",
      "3.0     2740\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "onus_attribute_36\n",
      "onus_attribute_36\n",
      "0.0    71205\n",
      "1.0     4748\n",
      "2.0     1125\n",
      "3.0      366\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "onus_attribute_37\n",
      "onus_attribute_37\n",
      "0.0    76309\n",
      "1.0     1077\n",
      "2.0       52\n",
      "3.0        6\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "onus_attribute_38\n",
      "onus_attribute_38\n",
      "0.0    57554\n",
      "1.0     7924\n",
      "2.0     5059\n",
      "3.0     3440\n",
      "4.0     2224\n",
      "5.0     1124\n",
      "6.0      119\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "onus_attribute_39\n",
      "onus_attribute_39\n",
      "0.0    63992\n",
      "1.0     6176\n",
      "2.0     3537\n",
      "3.0     2134\n",
      "4.0     1075\n",
      "5.0      478\n",
      "6.0       52\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "onus_attribute_40\n",
      "onus_attribute_40\n",
      "0.0    75065\n",
      "1.0     1657\n",
      "2.0      553\n",
      "3.0      142\n",
      "4.0       24\n",
      "5.0        3\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "onus_attribute_41\n",
      "onus_attribute_41\n",
      "0.0    65498\n",
      "1.0     5723\n",
      "2.0     3223\n",
      "3.0     1858\n",
      "4.0     1072\n",
      "6.0       39\n",
      "5.0       31\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "onus_attribute_42\n",
      "onus_attribute_42\n",
      "0.0    70790\n",
      "1.0     4971\n",
      "2.0     1161\n",
      "3.0      407\n",
      "4.0      108\n",
      "5.0        4\n",
      "6.0        3\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_1\n",
      "bureau_enquiry_1\n",
      "0.0     34892\n",
      "1.0     15433\n",
      "2.0      9210\n",
      "3.0      5686\n",
      "4.0      3680\n",
      "5.0      2296\n",
      "6.0      1493\n",
      "7.0       968\n",
      "8.0       651\n",
      "9.0       438\n",
      "0.4       369\n",
      "0.2       351\n",
      "10.0      300\n",
      "0.6       242\n",
      "0.8       213\n",
      "11.0      193\n",
      "12.0      149\n",
      "1.4       122\n",
      "1.2       113\n",
      "1.6       104\n",
      "13.0       95\n",
      "1.8        62\n",
      "14.0       56\n",
      "15.0       52\n",
      "2.2        34\n",
      "2.4        33\n",
      "16.0       24\n",
      "17.0       24\n",
      "18.0       20\n",
      "2.6        15\n",
      "19.0       14\n",
      "2.8        14\n",
      "21.0       13\n",
      "3.2        12\n",
      "22.0       11\n",
      "3.4        11\n",
      "20.0        9\n",
      "3.8         7\n",
      "3.6         4\n",
      "4.6         4\n",
      "4.4         4\n",
      "4.8         3\n",
      "23.0        3\n",
      "28.0        3\n",
      "24.0        2\n",
      "29.0        2\n",
      "34.0        2\n",
      "7.6         1\n",
      "6.6         1\n",
      "5.6         1\n",
      "5.2         1\n",
      "26.0        1\n",
      "33.0        1\n",
      "39.0        1\n",
      "42.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_2\n",
      "bureau_enquiry_2\n",
      "0.0     71382\n",
      "1.0      3854\n",
      "2.0      1164\n",
      "3.0       372\n",
      "0.2       255\n",
      "4.0       141\n",
      "0.4       101\n",
      "5.0        47\n",
      "1.2        40\n",
      "6.0        27\n",
      "0.6        26\n",
      "0.8        12\n",
      "7.0         9\n",
      "8.0         5\n",
      "1.4         3\n",
      "9.0         3\n",
      "14.0        1\n",
      "2.2         1\n",
      "16.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_3\n",
      "bureau_enquiry_3\n",
      "0.0     37167\n",
      "1.0     15121\n",
      "2.0      8642\n",
      "3.0      5296\n",
      "4.0      3352\n",
      "5.0      2106\n",
      "6.0      1398\n",
      "7.0       886\n",
      "8.0       597\n",
      "0.2       410\n",
      "9.0       380\n",
      "0.4       335\n",
      "10.0      263\n",
      "0.6       247\n",
      "0.8       190\n",
      "11.0      171\n",
      "12.0      132\n",
      "1.4        95\n",
      "1.2        92\n",
      "13.0       87\n",
      "1.6        87\n",
      "1.8        61\n",
      "14.0       49\n",
      "15.0       41\n",
      "2.2        32\n",
      "17.0       26\n",
      "2.4        25\n",
      "16.0       19\n",
      "18.0       17\n",
      "19.0       13\n",
      "3.2        11\n",
      "22.0       11\n",
      "2.8        10\n",
      "20.0       10\n",
      "2.6        10\n",
      "21.0        8\n",
      "3.4         8\n",
      "3.6         7\n",
      "3.8         4\n",
      "28.0        4\n",
      "4.8         3\n",
      "4.6         3\n",
      "4.4         3\n",
      "34.0        2\n",
      "23.0        2\n",
      "29.0        1\n",
      "26.0        1\n",
      "33.0        1\n",
      "38.0        1\n",
      "7.4         1\n",
      "24.0        1\n",
      "6.6         1\n",
      "5.6         1\n",
      "5.2         1\n",
      "4.2         1\n",
      "42.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_4\n",
      "bureau_enquiry_4\n",
      "0.0     75849\n",
      "1.0      1015\n",
      "2.0       318\n",
      "3.0        99\n",
      "0.2        72\n",
      "0.4        48\n",
      "4.0        20\n",
      "5.0        10\n",
      "0.6         5\n",
      "6.0         5\n",
      "0.8         1\n",
      "7.0         1\n",
      "10.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_5\n",
      "bureau_enquiry_5\n",
      "0.0     64556\n",
      "1.0      8122\n",
      "2.0      2391\n",
      "3.0       905\n",
      "4.0       405\n",
      "0.2       320\n",
      "5.0       215\n",
      "0.4       118\n",
      "6.0       113\n",
      "0.6        79\n",
      "7.0        58\n",
      "8.0        38\n",
      "9.0        28\n",
      "0.8        23\n",
      "1.2        16\n",
      "10.0       15\n",
      "11.0        9\n",
      "13.0        6\n",
      "1.4         5\n",
      "1.6         3\n",
      "12.0        3\n",
      "15.0        3\n",
      "2.6         2\n",
      "1.8         2\n",
      "14.0        2\n",
      "18.0        2\n",
      "2.2         1\n",
      "17.0        1\n",
      "20.0        1\n",
      "21.0        1\n",
      "22.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_6\n",
      "bureau_enquiry_6\n",
      "0.0     74775\n",
      "1.0      1762\n",
      "2.0       462\n",
      "0.2       161\n",
      "3.0       146\n",
      "1.2        40\n",
      "4.0        32\n",
      "0.4        31\n",
      "5.0        12\n",
      "0.6        11\n",
      "6.0         7\n",
      "0.8         2\n",
      "7.0         2\n",
      "14.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_8\n",
      "bureau_enquiry_8\n",
      "0.0    76701\n",
      "1.0      570\n",
      "2.0      103\n",
      "0.2       34\n",
      "3.0       21\n",
      "0.4        8\n",
      "4.0        3\n",
      "5.0        3\n",
      "6.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_9\n",
      "bureau_enquiry_9\n",
      "0.0    77019\n",
      "1.0      355\n",
      "2.0       48\n",
      "0.2       14\n",
      "0.4        3\n",
      "3.0        3\n",
      "4.0        2\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_10\n",
      "bureau_enquiry_10\n",
      "0.0     47061\n",
      "1.0     14373\n",
      "2.0      6851\n",
      "3.0      3573\n",
      "4.0      1966\n",
      "5.0       998\n",
      "6.0       569\n",
      "0.2       444\n",
      "0.4       357\n",
      "7.0       280\n",
      "0.6       208\n",
      "0.8       182\n",
      "8.0       150\n",
      "9.0        81\n",
      "1.4        72\n",
      "1.2        69\n",
      "10.0       51\n",
      "1.6        50\n",
      "11.0       23\n",
      "1.8        17\n",
      "12.0       14\n",
      "14.0        9\n",
      "2.2         7\n",
      "13.0        6\n",
      "2.4         6\n",
      "2.8         5\n",
      "2.6         5\n",
      "16.0        4\n",
      "18.0        2\n",
      "17.0        2\n",
      "3.8         2\n",
      "4.2         2\n",
      "3.6         2\n",
      "15.0        1\n",
      "3.4         1\n",
      "19.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_11\n",
      "bureau_enquiry_11\n",
      "0.0     22366\n",
      "1.0     12924\n",
      "2.0      9599\n",
      "3.0      7231\n",
      "4.0      5500\n",
      "        ...  \n",
      "43.0        1\n",
      "42.0        1\n",
      "41.0        1\n",
      "8.2         1\n",
      "72.0        1\n",
      "Name: count, Length: 84, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_12\n",
      "bureau_enquiry_12\n",
      "0.0     66010\n",
      "1.0      6558\n",
      "2.0      2396\n",
      "3.0      1037\n",
      "4.0       406\n",
      "0.2       334\n",
      "5.0       176\n",
      "0.4       147\n",
      "0.6       109\n",
      "6.0        81\n",
      "1.2        47\n",
      "7.0        42\n",
      "0.8        33\n",
      "8.0        27\n",
      "9.0        11\n",
      "1.4         7\n",
      "10.0        6\n",
      "11.0        5\n",
      "1.6         3\n",
      "12.0        2\n",
      "14.0        2\n",
      "2.8         1\n",
      "4.2         1\n",
      "1.8         1\n",
      "16.0        1\n",
      "18.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_13\n",
      "bureau_enquiry_13\n",
      "0.0     24979\n",
      "1.0     13137\n",
      "2.0      9289\n",
      "3.0      6821\n",
      "4.0      5075\n",
      "        ...  \n",
      "9.4         1\n",
      "8.8         1\n",
      "8.2         1\n",
      "7.4         1\n",
      "71.0        1\n",
      "Name: count, Length: 87, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_14\n",
      "bureau_enquiry_14\n",
      "0.0     74242\n",
      "1.0      1966\n",
      "2.0       662\n",
      "3.0       242\n",
      "0.2       139\n",
      "4.0        65\n",
      "0.4        57\n",
      "5.0        25\n",
      "0.6        20\n",
      "6.0        15\n",
      "0.8         5\n",
      "7.0         2\n",
      "10.0        2\n",
      "8.0         1\n",
      "11.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_15\n",
      "bureau_enquiry_15\n",
      "0.0     57112\n",
      "1.0     10626\n",
      "2.0      4359\n",
      "3.0      1986\n",
      "4.0      1052\n",
      "5.0       567\n",
      "0.2       332\n",
      "6.0       324\n",
      "7.0       226\n",
      "0.4       161\n",
      "8.0       115\n",
      "0.6       107\n",
      "9.0        90\n",
      "0.8        81\n",
      "10.0       58\n",
      "11.0       34\n",
      "12.0       31\n",
      "1.2        28\n",
      "1.4        27\n",
      "13.0       22\n",
      "14.0       16\n",
      "1.8        16\n",
      "1.6        11\n",
      "16.0        9\n",
      "15.0        8\n",
      "17.0        5\n",
      "2.2         5\n",
      "23.0        4\n",
      "22.0        3\n",
      "21.0        3\n",
      "19.0        3\n",
      "18.0        3\n",
      "2.4         3\n",
      "2.6         3\n",
      "3.4         3\n",
      "25.0        2\n",
      "26.0        1\n",
      "28.0        1\n",
      "27.0        1\n",
      "29.0        1\n",
      "4.6         1\n",
      "20.0        1\n",
      "4.2         1\n",
      "3.2         1\n",
      "31.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_16\n",
      "bureau_enquiry_16\n",
      "0.0     72385\n",
      "1.0      3031\n",
      "2.0       990\n",
      "3.0       386\n",
      "0.2       252\n",
      "4.0       142\n",
      "0.4        68\n",
      "5.0        52\n",
      "0.6        43\n",
      "1.2        41\n",
      "6.0        25\n",
      "7.0         8\n",
      "0.8         8\n",
      "8.0         7\n",
      "14.0        2\n",
      "1.8         1\n",
      "9.0         1\n",
      "11.0        1\n",
      "2.8         1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_18\n",
      "bureau_enquiry_18\n",
      "0.0    75814\n",
      "1.0     1235\n",
      "2.0      238\n",
      "0.2       71\n",
      "3.0       44\n",
      "0.4       24\n",
      "4.0       11\n",
      "5.0        5\n",
      "0.8        1\n",
      "6.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_19\n",
      "bureau_enquiry_19\n",
      "0.0    76613\n",
      "1.0      647\n",
      "2.0      110\n",
      "0.2       29\n",
      "3.0       29\n",
      "4.0        8\n",
      "0.4        7\n",
      "5.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_20\n",
      "bureau_enquiry_20\n",
      "0.0     34907\n",
      "1.0     14487\n",
      "2.0      8777\n",
      "3.0      5746\n",
      "4.0      3867\n",
      "5.0      2669\n",
      "6.0      1751\n",
      "7.0      1214\n",
      "8.0       831\n",
      "9.0       524\n",
      "10.0      368\n",
      "0.4       304\n",
      "0.2       292\n",
      "11.0      238\n",
      "0.6       217\n",
      "0.8       163\n",
      "12.0      155\n",
      "1.2       134\n",
      "13.0      119\n",
      "1.6       108\n",
      "1.4       103\n",
      "1.8        88\n",
      "14.0       50\n",
      "15.0       44\n",
      "2.4        37\n",
      "16.0       36\n",
      "2.2        35\n",
      "17.0       28\n",
      "2.8        20\n",
      "2.6        18\n",
      "3.6        14\n",
      "20.0       13\n",
      "3.2        13\n",
      "18.0       11\n",
      "4.2        10\n",
      "3.4         9\n",
      "19.0        8\n",
      "21.0        5\n",
      "3.8         4\n",
      "22.0        4\n",
      "4.4         4\n",
      "23.0        2\n",
      "24.0        2\n",
      "26.0        2\n",
      "5.2         2\n",
      "5.4         2\n",
      "31.0        1\n",
      "28.0        1\n",
      "27.0        1\n",
      "5.6         1\n",
      "25.0        1\n",
      "7.6         1\n",
      "5.8         1\n",
      "4.8         1\n",
      "33.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_21\n",
      "bureau_enquiry_21\n",
      "0.0     14261\n",
      "1.0      9894\n",
      "2.0      8249\n",
      "3.0      6670\n",
      "4.0      5704\n",
      "        ...  \n",
      "13.8        1\n",
      "13.2        1\n",
      "11.6        1\n",
      "11.4        1\n",
      "81.0        1\n",
      "Name: count, Length: 130, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_22\n",
      "bureau_enquiry_22\n",
      "0.0     58670\n",
      "1.0      9382\n",
      "2.0      4298\n",
      "3.0      2066\n",
      "4.0       980\n",
      "5.0       466\n",
      "0.2       418\n",
      "6.0       261\n",
      "0.4       198\n",
      "0.6       164\n",
      "7.0       140\n",
      "8.0        85\n",
      "0.8        72\n",
      "1.2        63\n",
      "9.0        44\n",
      "10.0       28\n",
      "1.4        20\n",
      "11.0       17\n",
      "1.6        15\n",
      "12.0       12\n",
      "13.0        8\n",
      "16.0        6\n",
      "1.8         6\n",
      "2.4         6\n",
      "14.0        5\n",
      "18.0        2\n",
      "2.8         2\n",
      "21.0        1\n",
      "22.0        1\n",
      "20.0        1\n",
      "19.0        1\n",
      "7.4         1\n",
      "15.0        1\n",
      "4.4         1\n",
      "2.6         1\n",
      "2.2         1\n",
      "34.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_23\n",
      "bureau_enquiry_23\n",
      "0.0     17137\n",
      "1.0     10454\n",
      "2.0      8202\n",
      "3.0      6486\n",
      "4.0      5258\n",
      "        ...  \n",
      "11.8        1\n",
      "12.2        1\n",
      "12.6        1\n",
      "13.4        1\n",
      "81.0        1\n",
      "Name: count, Length: 120, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_24\n",
      "bureau_enquiry_24\n",
      "0.0     71978\n",
      "1.0      3140\n",
      "2.0      1255\n",
      "3.0       459\n",
      "0.2       206\n",
      "4.0       154\n",
      "0.4        86\n",
      "5.0        61\n",
      "6.0        30\n",
      "0.6        27\n",
      "7.0        22\n",
      "0.8        11\n",
      "8.0         4\n",
      "9.0         3\n",
      "11.0        3\n",
      "10.0        2\n",
      "1.2         1\n",
      "1.6         1\n",
      "1.8         1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_25\n",
      "bureau_enquiry_25\n",
      "0.0     50461\n",
      "1.0     11540\n",
      "2.0      5597\n",
      "3.0      3178\n",
      "4.0      1979\n",
      "5.0      1229\n",
      "6.0       774\n",
      "7.0       526\n",
      "0.2       352\n",
      "8.0       340\n",
      "9.0       233\n",
      "10.0      168\n",
      "0.4       160\n",
      "0.6       127\n",
      "11.0      112\n",
      "0.8        82\n",
      "12.0       80\n",
      "1.2        63\n",
      "13.0       54\n",
      "14.0       48\n",
      "15.0       40\n",
      "1.4        39\n",
      "1.6        34\n",
      "16.0       33\n",
      "17.0       24\n",
      "18.0       18\n",
      "1.8        18\n",
      "2.4        17\n",
      "19.0       16\n",
      "2.2        11\n",
      "23.0        9\n",
      "20.0        8\n",
      "2.6         8\n",
      "21.0        7\n",
      "2.8         6\n",
      "3.4         5\n",
      "25.0        5\n",
      "29.0        5\n",
      "22.0        5\n",
      "24.0        4\n",
      "3.8         4\n",
      "4.2         3\n",
      "3.2         3\n",
      "27.0        3\n",
      "4.6         2\n",
      "36.0        2\n",
      "3.6         2\n",
      "30.0        2\n",
      "31.0        1\n",
      "39.0        1\n",
      "34.0        1\n",
      "5.8         1\n",
      "28.0        1\n",
      "26.0        1\n",
      "4.8         1\n",
      "49.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_26\n",
      "bureau_enquiry_26\n",
      "0.0     68877\n",
      "1.0      4677\n",
      "2.0      1842\n",
      "3.0       807\n",
      "0.2       361\n",
      "4.0       318\n",
      "5.0       147\n",
      "0.4       119\n",
      "0.6        74\n",
      "6.0        71\n",
      "1.2        44\n",
      "7.0        36\n",
      "0.8        23\n",
      "8.0        14\n",
      "9.0        10\n",
      "11.0        5\n",
      "1.4         5\n",
      "10.0        5\n",
      "1.6         3\n",
      "14.0        1\n",
      "16.0        1\n",
      "4.2         1\n",
      "2.2         1\n",
      "1.8         1\n",
      "20.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_28\n",
      "bureau_enquiry_28\n",
      "0.0    74298\n",
      "1.0     2242\n",
      "2.0      538\n",
      "3.0      133\n",
      "0.2      123\n",
      "0.4       52\n",
      "4.0       28\n",
      "5.0       12\n",
      "0.6        7\n",
      "0.8        4\n",
      "6.0        4\n",
      "8.0        2\n",
      "7.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_29\n",
      "bureau_enquiry_29\n",
      "0.0    76180\n",
      "1.0      872\n",
      "2.0      204\n",
      "3.0       75\n",
      "0.2       55\n",
      "4.0       28\n",
      "0.4       11\n",
      "5.0        8\n",
      "7.0        4\n",
      "0.6        3\n",
      "6.0        3\n",
      "8.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_30\n",
      "bureau_enquiry_30\n",
      "0.0     25934\n",
      "1.0     12718\n",
      "2.0      8712\n",
      "3.0      6303\n",
      "4.0      4963\n",
      "        ...  \n",
      "6.8         1\n",
      "41.0        1\n",
      "45.0        1\n",
      "46.0        1\n",
      "53.0        1\n",
      "Name: count, Length: 78, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_31\n",
      "bureau_enquiry_31\n",
      "0.0      6267\n",
      "1.0      6112\n",
      "2.0      5387\n",
      "3.0      4955\n",
      "4.0      4470\n",
      "         ... \n",
      "27.6        1\n",
      "27.8        1\n",
      "29.2        1\n",
      "96.0        1\n",
      "172.0       1\n",
      "Name: count, Length: 220, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_32\n",
      "bureau_enquiry_32\n",
      "0.0     49516\n",
      "1.0     11861\n",
      "2.0      6352\n",
      "3.0      3501\n",
      "4.0      1985\n",
      "5.0      1144\n",
      "6.0       692\n",
      "7.0       409\n",
      "0.2       407\n",
      "8.0       260\n",
      "0.6       217\n",
      "0.4       186\n",
      "9.0       169\n",
      "10.0      115\n",
      "0.8       101\n",
      "1.2        98\n",
      "11.0       86\n",
      "12.0       59\n",
      "1.4        51\n",
      "13.0       36\n",
      "1.6        31\n",
      "1.8        25\n",
      "15.0       18\n",
      "14.0       17\n",
      "2.4        17\n",
      "17.0       10\n",
      "16.0       10\n",
      "2.8        10\n",
      "18.0        9\n",
      "2.2         9\n",
      "3.2         6\n",
      "2.6         4\n",
      "20.0        4\n",
      "22.0        4\n",
      "21.0        3\n",
      "24.0        3\n",
      "32.0        2\n",
      "4.2         2\n",
      "39.0        2\n",
      "23.0        2\n",
      "26.0        1\n",
      "33.0        1\n",
      "30.0        1\n",
      "27.0        1\n",
      "4.8         1\n",
      "19.0        1\n",
      "4.6         1\n",
      "8.6         1\n",
      "3.6         1\n",
      "5.6         1\n",
      "43.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_33\n",
      "bureau_enquiry_33\n",
      "0.0      8158\n",
      "1.0      6980\n",
      "2.0      5749\n",
      "3.0      5135\n",
      "4.0      4374\n",
      "         ... \n",
      "21.8        1\n",
      "22.6        1\n",
      "23.4        1\n",
      "24.8        1\n",
      "165.0       1\n",
      "Name: count, Length: 204, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_34\n",
      "bureau_enquiry_34\n",
      "0.0     68116\n",
      "1.0      4911\n",
      "2.0      2229\n",
      "3.0       924\n",
      "4.0       375\n",
      "0.2       302\n",
      "5.0       172\n",
      "0.4       143\n",
      "6.0        78\n",
      "0.6        53\n",
      "7.0        39\n",
      "0.8        31\n",
      "8.0        24\n",
      "9.0        10\n",
      "10.0        8\n",
      "1.2         7\n",
      "11.0        4\n",
      "12.0        4\n",
      "13.0        4\n",
      "1.4         3\n",
      "1.8         2\n",
      "2.6         1\n",
      "2.2         1\n",
      "1.6         1\n",
      "14.0        1\n",
      "15.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_35\n",
      "bureau_enquiry_35\n",
      "0.0     36664\n",
      "1.0     11177\n",
      "2.0      6808\n",
      "3.0      4913\n",
      "4.0      3646\n",
      "        ...  \n",
      "9.2         1\n",
      "8.4         1\n",
      "7.6         1\n",
      "7.2         1\n",
      "96.0        1\n",
      "Name: count, Length: 101, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_36\n",
      "bureau_enquiry_36\n",
      "0.0     64645\n",
      "1.0      6007\n",
      "2.0      2942\n",
      "3.0      1503\n",
      "4.0       722\n",
      "5.0       391\n",
      "0.2       357\n",
      "6.0       184\n",
      "0.4       149\n",
      "0.6       129\n",
      "7.0       103\n",
      "1.2        62\n",
      "0.8        61\n",
      "8.0        59\n",
      "9.0        36\n",
      "10.0       18\n",
      "1.4        16\n",
      "11.0       16\n",
      "1.6         8\n",
      "12.0        6\n",
      "15.0        5\n",
      "13.0        4\n",
      "1.8         4\n",
      "16.0        3\n",
      "18.0        2\n",
      "14.0        2\n",
      "2.4         2\n",
      "26.0        2\n",
      "2.8         1\n",
      "5.4         1\n",
      "3.4         1\n",
      "17.0        1\n",
      "2.2         1\n",
      "22.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_38\n",
      "bureau_enquiry_38\n",
      "0.0    71014\n",
      "1.0     4368\n",
      "2.0     1158\n",
      "3.0      355\n",
      "0.2      255\n",
      "4.0       94\n",
      "0.4       89\n",
      "5.0       45\n",
      "0.6       22\n",
      "6.0       19\n",
      "0.8        9\n",
      "7.0        5\n",
      "8.0        4\n",
      "9.0        3\n",
      "1.2        2\n",
      "1.4        1\n",
      "1.6        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_39\n",
      "bureau_enquiry_39\n",
      "0.0     75887\n",
      "1.0       956\n",
      "2.0       281\n",
      "3.0       127\n",
      "0.2        65\n",
      "4.0        45\n",
      "5.0        30\n",
      "6.0        16\n",
      "0.4        14\n",
      "7.0         6\n",
      "8.0         4\n",
      "0.8         3\n",
      "0.6         3\n",
      "9.0         3\n",
      "10.0        2\n",
      "1.2         1\n",
      "11.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_40\n",
      "bureau_enquiry_40\n",
      "0.0     14310\n",
      "1.0     10231\n",
      "2.0      7933\n",
      "3.0      6365\n",
      "4.0      5258\n",
      "        ...  \n",
      "11.4        1\n",
      "12.4        1\n",
      "13.2        1\n",
      "13.6        1\n",
      "90.0        1\n",
      "Name: count, Length: 127, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_41\n",
      "bureau_enquiry_41\n",
      "1.0      4369\n",
      "2.0      4083\n",
      "3.0      3781\n",
      "4.0      3551\n",
      "0.0      3515\n",
      "         ... \n",
      "28.2        1\n",
      "27.8        1\n",
      "27.6        1\n",
      "38.8        1\n",
      "252.0       1\n",
      "Name: count, Length: 290, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_42\n",
      "bureau_enquiry_42\n",
      "0.0     42297\n",
      "1.0     13110\n",
      "2.0      7840\n",
      "3.0      4654\n",
      "4.0      2843\n",
      "        ...  \n",
      "6.2         1\n",
      "5.8         1\n",
      "5.6         1\n",
      "4.6         1\n",
      "59.0        1\n",
      "Name: count, Length: 67, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_43\n",
      "bureau_enquiry_43\n",
      "1.0      5209\n",
      "0.0      4968\n",
      "2.0      4583\n",
      "3.0      4066\n",
      "4.0      3777\n",
      "         ... \n",
      "37.4        1\n",
      "38.8        1\n",
      "21.6        1\n",
      "25.6        1\n",
      "241.0       1\n",
      "Name: count, Length: 279, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_44\n",
      "bureau_enquiry_44\n",
      "0.0     64659\n",
      "1.0      6295\n",
      "2.0      3090\n",
      "3.0      1401\n",
      "4.0       605\n",
      "0.2       344\n",
      "5.0       309\n",
      "0.4       178\n",
      "6.0       159\n",
      "0.6       116\n",
      "7.0        79\n",
      "8.0        52\n",
      "0.8        38\n",
      "9.0        30\n",
      "10.0       17\n",
      "1.2        16\n",
      "1.4        13\n",
      "11.0       11\n",
      "12.0        7\n",
      "13.0        7\n",
      "2.2         4\n",
      "1.6         3\n",
      "2.8         2\n",
      "14.0        2\n",
      "15.0        1\n",
      "16.0        1\n",
      "17.0        1\n",
      "3.4         1\n",
      "2.4         1\n",
      "1.8         1\n",
      "24.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_45\n",
      "bureau_enquiry_45\n",
      "0.0      30221\n",
      "1.0      10225\n",
      "2.0       6696\n",
      "3.0       5180\n",
      "4.0       3987\n",
      "         ...  \n",
      "12.8         1\n",
      "64.0         1\n",
      "15.6         1\n",
      "14.6         1\n",
      "134.0        1\n",
      "Name: count, Length: 138, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_46\n",
      "bureau_enquiry_46\n",
      "0.0     61268\n",
      "1.0      6996\n",
      "2.0      3709\n",
      "3.0      1976\n",
      "4.0      1073\n",
      "5.0       588\n",
      "6.0       360\n",
      "0.2       357\n",
      "0.4       218\n",
      "7.0       194\n",
      "0.6       155\n",
      "8.0       117\n",
      "0.8        87\n",
      "9.0        69\n",
      "1.4        61\n",
      "1.2        45\n",
      "10.0       36\n",
      "11.0       33\n",
      "1.6        19\n",
      "12.0       16\n",
      "13.0       10\n",
      "16.0        9\n",
      "1.8         9\n",
      "14.0        5\n",
      "17.0        4\n",
      "15.0        4\n",
      "19.0        3\n",
      "2.8         3\n",
      "2.2         3\n",
      "21.0        2\n",
      "3.6         2\n",
      "2.6         2\n",
      "18.0        2\n",
      "24.0        1\n",
      "26.0        1\n",
      "22.0        1\n",
      "5.4         1\n",
      "20.0        1\n",
      "3.8         1\n",
      "3.2         1\n",
      "2.4         1\n",
      "43.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_48\n",
      "bureau_enquiry_48\n",
      "0.0     67371\n",
      "1.0      6527\n",
      "2.0      2018\n",
      "3.0       670\n",
      "0.2       273\n",
      "4.0       209\n",
      "0.4       123\n",
      "5.0        90\n",
      "0.6        62\n",
      "6.0        33\n",
      "0.8        30\n",
      "7.0        12\n",
      "1.2         7\n",
      "8.0         6\n",
      "9.0         5\n",
      "1.4         2\n",
      "11.0        2\n",
      "10.0        1\n",
      "2.2         1\n",
      "1.6         1\n",
      "16.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_49\n",
      "bureau_enquiry_49\n",
      "0.0     75507\n",
      "1.0      1144\n",
      "2.0       328\n",
      "3.0       175\n",
      "4.0        79\n",
      "0.2        75\n",
      "5.0        48\n",
      "0.4        19\n",
      "7.0        18\n",
      "6.0        17\n",
      "8.0        11\n",
      "0.6         5\n",
      "9.0         4\n",
      "11.0        3\n",
      "13.0        3\n",
      "1.2         2\n",
      "0.8         2\n",
      "12.0        2\n",
      "10.0        1\n",
      "14.0        1\n",
      "Name: count, dtype: int64\n",
      "---------------------\n",
      "bureau_enquiry_50\n",
      "bureau_enquiry_50\n",
      "0.0      9430\n",
      "1.0      8392\n",
      "2.0      7044\n",
      "3.0      5882\n",
      "4.0      5194\n",
      "         ... \n",
      "18.4        1\n",
      "18.8        1\n",
      "19.4        1\n",
      "20.8        1\n",
      "102.0       1\n",
      "Name: count, Length: 165, dtype: int64\n",
      "---------------------\n"
     ]
    }
   ],
   "source": [
    "# taking all the features which are not binary \n",
    "multiclass_features = [feature for feature in train_df.columns if feature not in binary_features]\n",
    "\n",
    "for column in multiclass_features:\n",
    "    print(column)\n",
    "    print(train_df.value_counts(column))\n",
    "    print('---------------------')\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fisher Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "def calculate_fisher_score(X, y):\n",
    "    \"\"\"\n",
    "    Calculate Fisher Score for each feature in the dataset.\n",
    "\n",
    "    Parameters:\n",
    "    - X (pd.DataFrame or np.ndarray): Feature matrix.\n",
    "    - y (pd.Series or np.ndarray): Target labels.\n",
    "\n",
    "    Returns:\n",
    "    - pd.Series: Fisher Scores for each feature.\n",
    "    \"\"\"\n",
    "    # Convert to pandas DataFrame for easier manipulation if needed\n",
    "    if isinstance(X, np.ndarray):\n",
    "        X = pd.DataFrame(X)\n",
    "    if isinstance(y, np.ndarray):\n",
    "        y = pd.Series(y)\n",
    "\n",
    "    fisher_scores = {}\n",
    "\n",
    "    overall_mean = X.mean()\n",
    "    unique_classes = y.unique()\n",
    "\n",
    "    for feature in X.columns:\n",
    "        numerator = 0\n",
    "        denominator = 0\n",
    "\n",
    "        for cls in unique_classes:\n",
    "            class_data = X[feature][y == cls]\n",
    "            n_c = len(class_data)\n",
    "            mean_c = class_data.mean()\n",
    "            var_c = class_data.var()\n",
    "\n",
    "            numerator += n_c * (mean_c - overall_mean[feature]) ** 2\n",
    "            denominator += n_c * var_c\n",
    "\n",
    "        fisher_scores[feature] = numerator / (denominator + 1e-8)  # Add small value to avoid division by zero\n",
    "\n",
    "    return pd.Series(fisher_scores).sort_values(ascending=False)\n",
    "\n",
    "\n",
    "def select_top_features(X, fisher_scores, num_features):\n",
    "    top_features = fisher_scores.nlargest(num_features).index\n",
    "    return top_features\n",
    "\n",
    "X = train_df.drop(columns=['bad_flag'])\n",
    "y = train_df['bad_flag']\n",
    "\n",
    "# Calculate Fisher Scores\n",
    "fisher_scores = calculate_fisher_score(X, y)\n",
    "\n",
    "# Select top 2 features\n",
    "selected_features = select_top_features(X, fisher_scores, num_features=10)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['onus_attribute_2', 'onus_attribute_17', 'onus_attribute_23',\n",
       "       'onus_attribute_20', 'onus_attribute_26', 'onus_attribute_32',\n",
       "       'onus_attribute_29', 'onus_attribute_33', 'onus_attribute_35',\n",
       "       'onus_attribute_38'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "onus_attribute_2     1.236232e-02\n",
       "onus_attribute_17    1.132458e-02\n",
       "onus_attribute_23    1.075556e-02\n",
       "onus_attribute_20    1.073751e-02\n",
       "onus_attribute_26    1.033013e-02\n",
       "                         ...     \n",
       "bureau_441           1.376285e-08\n",
       "bureau_49            1.544848e-09\n",
       "bureau_32            1.231330e-09\n",
       "bureau_53            9.134177e-10\n",
       "bureau_221           2.051607e-10\n",
       "Length: 472, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fisher_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance using RF and XGBOOST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [07:46:39] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "\n",
    "def get_feature_importance_random_forest(train_df):\n",
    "    X_train = train_df.drop(columns=['bad_flag'])\n",
    "    y_train = train_df['bad_flag']\n",
    "    rf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "    rf.fit(X_train, y_train)\n",
    "    importance = rf.feature_importances_\n",
    "    return pd.Series(importance, index=X_train.columns).sort_values(ascending=False)\n",
    "\n",
    "def get_feature_importance_xgboost(train_df):\n",
    "    X_train = train_df.drop(columns=['bad_flag'])\n",
    "    y_train = train_df['bad_flag']\n",
    "    xgb = XGBClassifier(use_label_encoder=False, eval_metric=\"logloss\", random_state=42)\n",
    "    xgb.fit(X_train, y_train)\n",
    "    importance = xgb.feature_importances_\n",
    "    return pd.Series(importance, index=X_train.columns).sort_values(ascending=False)\n",
    "\n",
    "# Compute feature importance\n",
    "rf_importance = get_feature_importance_random_forest(train_df)\n",
    "xgb_importance = get_feature_importance_xgboost(train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize all scores to [0, 1]\n",
    "fisher_scores_normalized = (fisher_scores - fisher_scores.min()) / (fisher_scores.max() - fisher_scores.min())\n",
    "rf_importance_normalized = (rf_importance - rf_importance.min()) / (rf_importance.max() - rf_importance.min())\n",
    "xgb_importance_normalized = (xgb_importance - xgb_importance.min()) / (xgb_importance.max() - xgb_importance.min())\n",
    "\n",
    "# Combine the scores (simple average, could add weights if needed)\n",
    "combined_scores = (fisher_scores_normalized + rf_importance_normalized + xgb_importance_normalized) / 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "onus_attribute_2     0.012362\n",
       "onus_attribute_17    0.011325\n",
       "onus_attribute_23    0.010756\n",
       "onus_attribute_20    0.010738\n",
       "onus_attribute_26    0.010330\n",
       "onus_attribute_32    0.010139\n",
       "onus_attribute_29    0.009280\n",
       "onus_attribute_33    0.009074\n",
       "onus_attribute_35    0.008638\n",
       "onus_attribute_38    0.008583\n",
       "dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fisher_scores.sort_values(ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "onus_attribute_2     0.013551\n",
       "onus_attribute_20    0.012201\n",
       "onus_attribute_17    0.011964\n",
       "bureau_450           0.010887\n",
       "bureau_439           0.010759\n",
       "onus_attribute_23    0.010640\n",
       "account_number       0.010118\n",
       "bureau_452           0.009995\n",
       "bureau_443           0.009693\n",
       "bureau_43            0.009494\n",
       "dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_importance.sort_values(ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "onus_attribute_2     0.734477\n",
       "onus_attribute_26    0.655289\n",
       "onus_attribute_17    0.618087\n",
       "onus_attribute_20    0.606284\n",
       "onus_attribute_23    0.573023\n",
       "bureau_439           0.399762\n",
       "bureau_450           0.371270\n",
       "onus_attribute_32    0.368731\n",
       "onus_attribute_29    0.365117\n",
       "bureau_432           0.344471\n",
       "dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_scores.sort_values(ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Is Binning Required??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "multiclass_features.remove('account_number')\n",
    "for column in multiclass_features:\n",
    "    threshold = 0.04\n",
    "    value_counts = train_df[column].value_counts(normalize=True)\n",
    "    rare_categories = value_counts[value_counts < threshold].index\n",
    "    train_df[column] = train_df[column].replace(rare_categories, 'OTHERS')\n",
    "\n",
    "for column in multiclass_features:\n",
    "    threshold = 0.04\n",
    "    value_counts = test_df[column].value_counts(normalize=True)\n",
    "    rare_categories = value_counts[value_counts < threshold].index\n",
    "    test_df[column] = test_df[column].replace(rare_categories, 'OTHERS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account_number</th>\n",
       "      <th>bureau_1</th>\n",
       "      <th>bureau_2</th>\n",
       "      <th>bureau_3</th>\n",
       "      <th>bureau_5</th>\n",
       "      <th>bureau_6</th>\n",
       "      <th>bureau_7</th>\n",
       "      <th>bureau_8</th>\n",
       "      <th>bureau_9</th>\n",
       "      <th>bureau_10</th>\n",
       "      <th>...</th>\n",
       "      <th>bureau_enquiry_41</th>\n",
       "      <th>bureau_enquiry_42</th>\n",
       "      <th>bureau_enquiry_43</th>\n",
       "      <th>bureau_enquiry_44</th>\n",
       "      <th>bureau_enquiry_45</th>\n",
       "      <th>bureau_enquiry_46</th>\n",
       "      <th>bureau_enquiry_48</th>\n",
       "      <th>bureau_enquiry_49</th>\n",
       "      <th>bureau_enquiry_50</th>\n",
       "      <th>bad_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47642.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>95032.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>...</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>3.0</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40510.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>1.0</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51692.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>3.0</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27459.0</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>...</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>1.0</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77439</th>\n",
       "      <td>6266.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>0.0</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77440</th>\n",
       "      <td>54887.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77441</th>\n",
       "      <td>76821.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>1.0</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77442</th>\n",
       "      <td>861.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>0.0</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>0.0</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77443</th>\n",
       "      <td>15796.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>3.0</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>9.0</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>...</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>2.0</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77444 rows × 473 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       account_number bureau_1 bureau_2 bureau_3 bureau_5 bureau_6 bureau_7  \\\n",
       "0             47642.0      2.0      0.0      0.0      0.0      0.0      1.0   \n",
       "1             95032.0      0.0   OTHERS      1.0      1.0   OTHERS   OTHERS   \n",
       "2             40510.0      0.0      0.0      1.0      0.0      0.0      7.0   \n",
       "3             51692.0      0.0      0.0      2.0      0.0      0.0      3.0   \n",
       "4             27459.0   OTHERS   OTHERS      2.0      1.0      0.0      8.0   \n",
       "...               ...      ...      ...      ...      ...      ...      ...   \n",
       "77439          6266.0      0.0      4.0      1.0      0.0      0.0      5.0   \n",
       "77440         54887.0      0.0      0.0      0.0      0.0      0.0      1.0   \n",
       "77441         76821.0      0.0      0.0      1.0      0.0      0.0   OTHERS   \n",
       "77442           861.0      0.0      1.0      0.0      0.0      0.0   OTHERS   \n",
       "77443         15796.0      1.0      4.0      0.0      0.0   OTHERS      3.0   \n",
       "\n",
       "      bureau_8 bureau_9 bureau_10  ... bureau_enquiry_41 bureau_enquiry_42  \\\n",
       "0          2.0      1.0       2.0  ...               5.0               2.0   \n",
       "1       OTHERS   OTHERS    OTHERS  ...            OTHERS               3.0   \n",
       "2          1.0   OTHERS       1.0  ...            OTHERS               1.0   \n",
       "3       OTHERS      3.0    OTHERS  ...               0.0               0.0   \n",
       "4       OTHERS   OTHERS    OTHERS  ...            OTHERS               1.0   \n",
       "...        ...      ...       ...  ...               ...               ...   \n",
       "77439      1.0   OTHERS       1.0  ...            OTHERS               0.0   \n",
       "77440      1.0      2.0       1.0  ...               4.0               1.0   \n",
       "77441      1.0   OTHERS       1.0  ...               6.0               0.0   \n",
       "77442      0.0   OTHERS       0.0  ...            OTHERS               0.0   \n",
       "77443   OTHERS      9.0    OTHERS  ...            OTHERS               2.0   \n",
       "\n",
       "      bureau_enquiry_43 bureau_enquiry_44 bureau_enquiry_45 bureau_enquiry_46  \\\n",
       "0                   3.0            OTHERS               0.0               0.0   \n",
       "1                OTHERS               0.0               4.0               2.0   \n",
       "2                OTHERS               0.0               3.0               1.0   \n",
       "3                   0.0               0.0               0.0               0.0   \n",
       "4                OTHERS               1.0               2.0               0.0   \n",
       "...                 ...               ...               ...               ...   \n",
       "77439            OTHERS               0.0               4.0               0.0   \n",
       "77440               3.0               0.0               0.0               1.0   \n",
       "77441               6.0               0.0               0.0               0.0   \n",
       "77442            OTHERS               0.0               0.0               0.0   \n",
       "77443            OTHERS               0.0               4.0               0.0   \n",
       "\n",
       "      bureau_enquiry_48 bureau_enquiry_49 bureau_enquiry_50 bad_flag  \n",
       "0                   0.0               0.0               3.0        0  \n",
       "1                   0.0               0.0            OTHERS        0  \n",
       "2                   0.0               0.0               7.0        0  \n",
       "3                   0.0               0.0               0.0        0  \n",
       "4                   0.0               0.0            OTHERS        0  \n",
       "...                 ...               ...               ...      ...  \n",
       "77439               0.0               0.0               7.0        0  \n",
       "77440               0.0               0.0               3.0        0  \n",
       "77441               0.0               0.0               6.0        0  \n",
       "77442               0.0               0.0            OTHERS        0  \n",
       "77443               0.0               0.0            OTHERS        0  \n",
       "\n",
       "[77444 rows x 473 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of features: 10\n"
     ]
    }
   ],
   "source": [
    "cnt =0\n",
    "features = []\n",
    "for col in train_df.columns:\n",
    "    if train_df[col].nunique() >= 11:\n",
    "        cnt+=1\n",
    "        features.append(col)\n",
    "\n",
    "print(f'Number of features: {cnt}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import category_encoders as ce\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train_df.drop(columns=['bad_flag'])\n",
    "y_train = train_df['bad_flag']\n",
    "X_test = test_df.drop(columns=['bad_flag'])\n",
    "y_test = test_df['bad_flag']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### target Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = ce.TargetEncoder(cols=train_df.drop(columns=['bad_flag','account_number']).columns)\n",
    "X_train_encoded = encoder.fit_transform(X_train, y_train)\n",
    "X_test_encoded = encoder.fit(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ordinal Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import OrdinalEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the OrdinalEncoder\n",
    "encoder = OrdinalEncoder()\n",
    "\n",
    "# Fit the encoder on training data and transform both train and test sets\n",
    "X_train_encoded = pd.DataFrame(encoder.fit_transform(X_train), columns=X_train.drop(columns=['account_number']).columns, index=X_train.index)\n",
    "X_test_encoded = pd.DataFrame(encoder.transform(X_test), columns=X_test.drop(columns=['account_number']).columns, index=X_test.index)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. ANOVA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest, f_classif\n",
    "\n",
    "X_train = train_df.drop(columns=['bad_flag'])\n",
    "y_train = train_df['bad_flag']\n",
    "X_test = test_df.drop(columns=['bad_flag'])\n",
    "y_test = test_df['bad_flag']\n",
    "\n",
    "selector = SelectKBest(f_classif, k=64)  # Adjust k as needed\n",
    "X_train_selected = selector.fit_transform(X_train, y_train)\n",
    "selected_features = X_train.columns[selector.get_support()]\n",
    "X_test_selected = selector.transform(X_test)\n",
    "\n",
    "X_train_ANOVA = pd.DataFrame(X_train_selected, columns=selected_features)\n",
    "X_test_ANOVA = pd.DataFrame(X_test_selected, columns=selected_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. sequential backward Elimination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.datasets import make_classification\n",
    "from mlxtend.feature_selection import SequentialFeatureSelector as SFS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
    "from sklearn.metrics import f1_score\n",
    "import numpy as np\n",
    "from sklearn.metrics import roc_curve, f1_score, roc_auc_score\n",
    "\n",
    "\n",
    "# Define hyperparameters for the RandomForestClassifier\n",
    "rf_params = {\n",
    "    'n_estimators': 200,      # Number of trees in the forest\n",
    "    'max_depth': 10,          # Maximum depth of the trees\n",
    "    'min_samples_split': 5,   # Minimum number of samples required to split an internal node\n",
    "    'min_samples_leaf': 2,    # Minimum number of samples required at each leaf node\n",
    "    'max_features': 'sqrt',   # Number of features to consider at each split\n",
    "    'random_state': 42\n",
    "}\n",
    "# Define a custom scoring function\n",
    "def custom_scoring(estimator, X, y):\n",
    "    # Get predicted probabilities\n",
    "    y_prob = estimator.predict_proba(X)[:, 1]\n",
    "    \n",
    "    # Compute ROC curve\n",
    "    fpr, tpr, thresholds = roc_curve(y, y_prob)\n",
    "    \n",
    "    # Compute Youden's J statistic and find the best threshold\n",
    "    j_scores = tpr - fpr\n",
    "    best_threshold_idx = np.argmax(j_scores)\n",
    "    best_threshold = thresholds[best_threshold_idx]\n",
    "    \n",
    "    # Apply the best threshold to make predictions\n",
    "    y_pred_best = (y_prob >= best_threshold).astype(int)\n",
    "    \n",
    "    # Calculate F1 score using the best threshold\n",
    "    f1 = f1_score(y, y_pred_best)\n",
    "    return f1\n",
    "\n",
    "\n",
    "# Initialize cuML RandomForestClassifier\n",
    "rf_classifier = RandomForestClassifier(**rf_params)\n",
    "\n",
    "# SFS doesn't support cuML directly, so convert input to NumPy\n",
    "X_train_np = np.array(X_train)\n",
    "y_train_np = np.array(y_train)\n",
    "\n",
    "# Sequential Backward Feature Selection (SBFS)\n",
    "sfs = SFS(rf_classifier, \n",
    "          k_features='best', \n",
    "          forward=False, \n",
    "          floating=False, \n",
    "          scoring=custom_scoring, \n",
    "          cv=5, \n",
    "          n_jobs=-1, \n",
    "          verbose=2)\n",
    "\n",
    "# Fit the feature selector\n",
    "sfs.fit(X_train_np, y_train_np)\n",
    "\n",
    "# Get the selected features\n",
    "selected_features = sfs.k_feature_idx_\n",
    "print(f\"Selected features: {selected_features}\")\n",
    "\n",
    "\n",
    "'''Not Necessary'''\n",
    "# Train the model on the selected features\n",
    "X_train_selected = X_train[:, selected_features]\n",
    "X_test_selected = X_test[:, selected_features]\n",
    "rf_classifier.fit(X_train_selected, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_prob_test = rf_classifier.predict_proba(X_test_selected)[:, 1]\n",
    "\n",
    "# Use the best threshold from the entire training set to evaluate on test data\n",
    "fpr, tpr, thresholds = roc_curve(y_train, rf_classifier.predict_proba(X_train_selected)[:, 1])\n",
    "j_scores = tpr - fpr\n",
    "best_threshold_idx = np.argmax(j_scores)\n",
    "best_threshold = thresholds[best_threshold_idx]\n",
    "\n",
    "y_test_pred = (y_prob_test >= best_threshold).astype(int)\n",
    "\n",
    "# Evaluate performance on the test set\n",
    "f1_test = f1_score(y_test, y_test_pred)\n",
    "print(f\"F1 Score on Test Set with Best Threshold: {f1_test}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Auto Encoders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "import torch\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x224d94da6d0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set random seeds for reproducibility\n",
    "torch.manual_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda\n"
     ]
    }
   ],
   "source": [
    "# Check for GPU\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print(f\"Using device: {device}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the sample size for each class\n",
    "sample_fraction = 0.01  # Specify the fraction of the dataset to sample\n",
    "class_counts = train_df['bad_flag'].value_counts()\n",
    "sample_sizes = (class_counts * sample_fraction).astype(int)\n",
    "\n",
    "# Sample each class separately\n",
    "sampled_train_df = train_df.groupby('bad_flag').apply(\n",
    "    lambda x: x.sample(n=sample_sizes[x.name], random_state=42)\n",
    ").reset_index(drop=True)\n",
    "\n",
    "sampled_test_df = test_df.groupby('bad_flag').apply(\n",
    "    lambda x: x.sample(n=sample_sizes[x.name], random_state=42)\n",
    ").reset_index(drop=True)\n",
    "\n",
    "train_account = sampled_train_df['account_number']\n",
    "sampled_train_df.drop(columns=['account_number'], inplace=True)\n",
    "test_account = sampled_test_df['account_number']\n",
    "sampled_test_df.drop(columns=['account_number'], inplace=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = sampled_train_df.drop(columns=['bad_flag'])\n",
    "y_train = sampled_train_df['bad_flag']\n",
    "X_test = sampled_test_df.drop(columns=['bad_flag'])\n",
    "y_test = sampled_test_df['bad_flag']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sarth\\AppData\\Local\\Temp\\ipykernel_56668\\1195507923.py:38: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  self.features = torch.tensor(features, dtype=torch.float32)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Temp\\ipykernel_56668\\1195507923.py:39: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  self.labels = torch.tensor(labels, dtype=torch.long)  # Use dtype=torch.float32 if labels are continuous\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100, Train Loss: 10.4170, Val Loss: 7.1632\n",
      "Epoch 2/100, Train Loss: 7.0557, Val Loss: 6.9896\n",
      "Epoch 3/100, Train Loss: 6.9914, Val Loss: 6.9861\n",
      "Epoch 4/100, Train Loss: 6.9968, Val Loss: 6.9760\n",
      "Epoch 5/100, Train Loss: 6.9911, Val Loss: 6.9824\n",
      "Epoch 6/100, Train Loss: 6.9945, Val Loss: 6.9755\n",
      "Epoch 7/100, Train Loss: 6.9937, Val Loss: 6.9826\n",
      "Epoch 8/100, Train Loss: 6.9921, Val Loss: 6.9740\n",
      "Epoch 9/100, Train Loss: 6.9817, Val Loss: 6.9658\n",
      "Epoch 10/100, Train Loss: 6.9771, Val Loss: 6.9712\n",
      "Epoch 11/100, Train Loss: 6.9694, Val Loss: 6.9543\n",
      "Epoch 12/100, Train Loss: 6.9555, Val Loss: 6.9600\n",
      "Epoch 13/100, Train Loss: 6.9461, Val Loss: 6.9523\n",
      "Epoch 14/100, Train Loss: 6.9241, Val Loss: 6.9166\n",
      "Epoch 15/100, Train Loss: 6.8846, Val Loss: 6.8993\n",
      "Epoch 16/100, Train Loss: 6.8395, Val Loss: 6.8811\n",
      "Epoch 17/100, Train Loss: 6.7941, Val Loss: 6.8502\n",
      "Epoch 18/100, Train Loss: 6.7485, Val Loss: 6.8282\n",
      "Epoch 19/100, Train Loss: 6.6996, Val Loss: 6.8173\n",
      "Epoch 20/100, Train Loss: 6.6500, Val Loss: 6.7813\n",
      "Epoch 21/100, Train Loss: 6.5864, Val Loss: 6.7415\n",
      "Epoch 22/100, Train Loss: 6.5280, Val Loss: 6.7258\n",
      "Epoch 23/100, Train Loss: 6.4809, Val Loss: 6.7234\n",
      "Epoch 24/100, Train Loss: 6.4207, Val Loss: 6.6810\n",
      "Epoch 25/100, Train Loss: 6.3744, Val Loss: 6.6439\n",
      "Epoch 26/100, Train Loss: 6.3208, Val Loss: 6.6193\n",
      "Epoch 27/100, Train Loss: 6.2665, Val Loss: 6.6191\n",
      "Epoch 28/100, Train Loss: 6.2291, Val Loss: 6.6130\n",
      "Epoch 29/100, Train Loss: 6.1975, Val Loss: 6.6011\n",
      "Epoch 30/100, Train Loss: 6.1592, Val Loss: 6.5958\n",
      "Epoch 31/100, Train Loss: 6.1028, Val Loss: 6.6118\n",
      "Epoch 32/100, Train Loss: 6.0533, Val Loss: 6.5843\n",
      "Epoch 33/100, Train Loss: 6.0095, Val Loss: 6.6067\n",
      "Epoch 34/100, Train Loss: 5.9664, Val Loss: 6.5825\n",
      "Epoch 35/100, Train Loss: 5.9289, Val Loss: 6.6252\n",
      "Epoch 36/100, Train Loss: 5.8823, Val Loss: 6.6081\n",
      "Epoch 37/100, Train Loss: 5.8310, Val Loss: 6.5841\n",
      "Epoch 38/100, Train Loss: 5.7740, Val Loss: 6.6071\n",
      "Epoch 39/100, Train Loss: 5.7338, Val Loss: 6.6221\n",
      "Epoch 40/100, Train Loss: 5.7096, Val Loss: 6.6249\n",
      "Epoch 41/100, Train Loss: 5.6374, Val Loss: 6.6996\n",
      "Epoch 42/100, Train Loss: 5.6009, Val Loss: 6.7350\n",
      "Epoch 43/100, Train Loss: 5.5913, Val Loss: 6.7016\n",
      "Epoch 44/100, Train Loss: 5.5245, Val Loss: 6.6804\n",
      "Epoch 45/100, Train Loss: 5.4767, Val Loss: 6.7519\n",
      "Epoch 46/100, Train Loss: 5.4253, Val Loss: 6.7305\n",
      "Epoch 47/100, Train Loss: 5.3735, Val Loss: 6.7505\n",
      "Epoch 48/100, Train Loss: 5.3202, Val Loss: 6.7764\n",
      "Epoch 49/100, Train Loss: 5.2785, Val Loss: 6.8117\n",
      "Epoch 50/100, Train Loss: 5.2328, Val Loss: 6.8546\n",
      "Epoch 51/100, Train Loss: 5.2002, Val Loss: 6.8703\n",
      "Epoch 52/100, Train Loss: 5.1520, Val Loss: 6.8958\n",
      "Epoch 53/100, Train Loss: 5.0816, Val Loss: 6.9270\n",
      "Epoch 54/100, Train Loss: 5.0378, Val Loss: 6.9479\n",
      "Epoch 55/100, Train Loss: 5.0049, Val Loss: 7.0108\n",
      "Epoch 56/100, Train Loss: 4.9448, Val Loss: 6.9797\n",
      "Epoch 57/100, Train Loss: 4.9244, Val Loss: 6.9913\n",
      "Epoch 58/100, Train Loss: 4.8539, Val Loss: 7.0923\n",
      "Epoch 59/100, Train Loss: 4.8263, Val Loss: 7.0652\n",
      "Epoch 60/100, Train Loss: 4.7739, Val Loss: 7.0568\n",
      "Epoch 61/100, Train Loss: 4.7305, Val Loss: 7.1640\n",
      "Epoch 62/100, Train Loss: 4.6968, Val Loss: 7.2180\n",
      "Epoch 63/100, Train Loss: 4.6514, Val Loss: 7.1973\n",
      "Epoch 64/100, Train Loss: 4.6224, Val Loss: 7.2546\n",
      "Epoch 65/100, Train Loss: 4.5563, Val Loss: 7.2642\n",
      "Epoch 66/100, Train Loss: 4.4971, Val Loss: 7.3096\n",
      "Epoch 67/100, Train Loss: 4.4667, Val Loss: 7.3330\n",
      "Epoch 68/100, Train Loss: 4.4240, Val Loss: 7.3730\n",
      "Epoch 69/100, Train Loss: 4.3868, Val Loss: 7.3907\n",
      "Epoch 70/100, Train Loss: 4.3453, Val Loss: 7.4505\n",
      "Epoch 71/100, Train Loss: 4.2992, Val Loss: 7.4822\n",
      "Epoch 72/100, Train Loss: 4.2539, Val Loss: 7.4607\n",
      "Epoch 73/100, Train Loss: 4.2019, Val Loss: 7.5223\n",
      "Epoch 74/100, Train Loss: 4.1751, Val Loss: 7.5833\n",
      "Epoch 75/100, Train Loss: 4.1386, Val Loss: 7.5735\n",
      "Epoch 76/100, Train Loss: 4.0914, Val Loss: 7.6573\n",
      "Epoch 77/100, Train Loss: 4.0530, Val Loss: 7.6957\n",
      "Epoch 78/100, Train Loss: 4.0135, Val Loss: 7.6864\n",
      "Epoch 79/100, Train Loss: 3.9831, Val Loss: 7.7348\n",
      "Epoch 80/100, Train Loss: 3.9612, Val Loss: 7.7340\n",
      "Epoch 81/100, Train Loss: 3.9238, Val Loss: 7.7995\n",
      "Epoch 82/100, Train Loss: 3.8792, Val Loss: 7.7865\n",
      "Epoch 83/100, Train Loss: 3.8563, Val Loss: 7.9175\n",
      "Epoch 84/100, Train Loss: 3.8060, Val Loss: 7.8862\n",
      "Epoch 85/100, Train Loss: 3.7702, Val Loss: 7.9260\n",
      "Epoch 86/100, Train Loss: 3.7338, Val Loss: 7.9303\n",
      "Epoch 87/100, Train Loss: 3.7188, Val Loss: 8.0050\n",
      "Epoch 88/100, Train Loss: 3.6820, Val Loss: 7.9674\n",
      "Epoch 89/100, Train Loss: 3.6483, Val Loss: 7.9907\n",
      "Epoch 90/100, Train Loss: 3.6230, Val Loss: 8.0342\n",
      "Epoch 91/100, Train Loss: 3.5913, Val Loss: 8.0755\n",
      "Epoch 92/100, Train Loss: 3.5616, Val Loss: 8.0963\n",
      "Epoch 93/100, Train Loss: 3.5317, Val Loss: 8.1440\n",
      "Epoch 94/100, Train Loss: 3.5210, Val Loss: 8.2117\n",
      "Epoch 95/100, Train Loss: 3.4990, Val Loss: 8.1649\n",
      "Epoch 96/100, Train Loss: 3.4505, Val Loss: 8.2211\n",
      "Epoch 97/100, Train Loss: 3.4131, Val Loss: 8.2698\n",
      "Epoch 98/100, Train Loss: 3.4060, Val Loss: 8.3043\n",
      "Epoch 99/100, Train Loss: 3.3762, Val Loss: 8.3165\n",
      "Epoch 100/100, Train Loss: 3.3460, Val Loss: 8.3810\n",
      "Model training complete. Best model loaded.\n",
      "Extracted features shape: (800, 32)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sarth\\AppData\\Local\\Temp\\ipykernel_56668\\1195507923.py:134: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  model.load_state_dict(torch.load(\"best_autoencoder.pth\"))\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "import pandas as pd\n",
    "\n",
    "# Assuming df is a pandas DataFrame\n",
    "X = df.drop(columns=['Target'])\n",
    "y = df['Target']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Initialize the OrdinalEncoder with handling for unknown categories\n",
    "ordinal_encoder = OrdinalEncoder(handle_unknown=\"use_encoded_value\", unknown_value=-1)\n",
    "\n",
    "# Fit the encoder on the training data and transform both training and testing sets\n",
    "X_train_encoded = ordinal_encoder.fit_transform(X_train)\n",
    "X_test_encoded = ordinal_encoder.transform(X_test)\n",
    "\n",
    "# Encode the target column separately\n",
    "target_encoder = OrdinalEncoder(handle_unknown=\"use_encoded_value\", unknown_value=-1)\n",
    "y_train_encoded = target_encoder.fit_transform(y_train.values.reshape(-1, 1))\n",
    "y_test_encoded = target_encoder.transform(y_test.values.reshape(-1, 1))\n",
    "\n",
    "train_data = torch.tensor(X_train_encoded, dtype=torch.float32)\n",
    "val_data = torch.tensor(X_test_encoded, dtype=torch.float32)\n",
    "\n",
    "class CustomDataset(Dataset):\n",
    "    def __init__(self, features, labels):\n",
    "        # Convert features and labels to NumPy arrays if they're pandas objects\n",
    "        if isinstance(features, pd.DataFrame):\n",
    "            features = features.values\n",
    "        if isinstance(labels, pd.Series):\n",
    "            labels = labels.values\n",
    "        \n",
    "        # Convert to PyTorch tensors\n",
    "        self.features = torch.tensor(features, dtype=torch.float32)\n",
    "        self.labels = torch.tensor(labels, dtype=torch.long)  # Use dtype=torch.float32 if labels are continuous\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.features)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.features[idx], self.labels[idx]\n",
    "    \n",
    "train_dataset = CustomDataset(train_data, train_data)\n",
    "test_dataset = CustomDataset(val_data, val_data)\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, pin_memory=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False, pin_memory=True)\n",
    "\n",
    "class Autoencoder(nn.Module):\n",
    "    def __init__(self, input_dim, encoding_dim):\n",
    "        super(Autoencoder, self).__init__()\n",
    "        self.encoder = nn.Sequential(\n",
    "            nn.Linear(input_dim, 512),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(512, 128),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(128, encoding_dim)\n",
    "        )\n",
    "        self.decoder = nn.Sequential(\n",
    "            nn.Linear(encoding_dim, 128),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(128, 512),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(512, input_dim)\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        encoded = self.encoder(x)\n",
    "        decoded = self.decoder(encoded)\n",
    "        return encoded, decoded\n",
    "\n",
    "# Hyperparameters\n",
    "input_dim = X_train_encoded.shape[1]\n",
    "encoding_dim = 32  # Latent space dimension\n",
    "lr = 0.001\n",
    "weight_decay = 1e-5  # Regularization term\n",
    "batch_size = 32\n",
    "epochs = 100\n",
    "patience = 10  # Early stopping patience\n",
    "\n",
    "# Initialize model, loss function, and optimizer\n",
    "model = Autoencoder(input_dim=input_dim, encoding_dim=encoding_dim)\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)\n",
    "\n",
    "# Early stopping setup\n",
    "best_val_loss = float('inf')\n",
    "patience_counter = 0\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    model.train()\n",
    "    train_loss = 0.0\n",
    "\n",
    "    for batch, _ in train_loader:\n",
    "        optimizer.zero_grad()\n",
    "        _, decoded = model(batch)\n",
    "        loss = criterion(decoded, batch)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        train_loss += loss.item()\n",
    "\n",
    "    train_loss /= len(train_loader)\n",
    "\n",
    "    # Validation\n",
    "    model.eval()\n",
    "    val_loss = 0.0\n",
    "    with torch.no_grad():\n",
    "        for batch, _ in test_loader:\n",
    "            _, decoded = model(batch)\n",
    "            loss = criterion(decoded, batch)\n",
    "            val_loss += loss.item()\n",
    "\n",
    "    val_loss /= len(test_loader)\n",
    "\n",
    "    print(f\"Epoch {epoch+1}/{epochs}, Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}\")\n",
    "\n",
    "    # Check for early stopping\n",
    "    # if val_loss < best_val_loss:\n",
    "    #     best_val_loss = val_loss\n",
    "    #     patience_counter = 0\n",
    "    #     torch.save(model.state_dict(), \"best_autoencoder.pth\")  # Save the best model\n",
    "    # else:\n",
    "    #     patience_counter += 1\n",
    "    #     if patience_counter >= patience:\n",
    "    #         print(\"Early stopping triggered.\")\n",
    "    #         break\n",
    "  \n",
    "torch.save(model.state_dict(), \"best_autoencoder.pth\")      \n",
    "# Load the best model\n",
    "model.load_state_dict(torch.load(\"best_autoencoder.pth\"))\n",
    "print(\"Model training complete. Best model loaded.\")\n",
    "\n",
    "# Feature extraction\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    train_features = model.encoder(train_data).numpy()\n",
    "    val_features = model.encoder(val_data).numpy()\n",
    "\n",
    "print(\"Extracted features shape:\", train_features.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "import pandas as pd\n",
    "\n",
    "# Assuming df is a pandas DataFrame\n",
    "X = df.drop(columns=['Target'])\n",
    "y = df['Target']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Initialize the OrdinalEncoder with categories starting from 1\n",
    "ordinal_encoder = OrdinalEncoder(handle_unknown=\"use_encoded_value\", unknown_value=-1)\n",
    "\n",
    "# Fit the encoder on the training data and transform both training and testing sets\n",
    "X_train_encoded = ordinal_encoder.fit_transform(X_train) + 1\n",
    "X_test_encoded = ordinal_encoder.transform(X_test) + 1\n",
    "\n",
    "# Encode the target column separately\n",
    "target_encoder = OrdinalEncoder(handle_unknown=\"use_encoded_value\", unknown_value=-1)\n",
    "y_train_encoded = target_encoder.fit_transform(y_train.values.reshape(-1, 1)) + 1\n",
    "y_test_encoded = target_encoder.transform(y_test.values.reshape(-1, 1)) + 1\n",
    "\n",
    "train_data = torch.tensor(X_train_encoded, dtype=torch.float32)\n",
    "val_data = torch.tensor(X_test_encoded, dtype=torch.float32)\n",
    "\n",
    "class CustomDataset(Dataset):\n",
    "    def __init__(self, features):\n",
    "        # Convert features to NumPy arrays if they're pandas objects\n",
    "        if isinstance(features, pd.DataFrame):\n",
    "            features = features.values\n",
    "        \n",
    "        # Convert to PyTorch tensors\n",
    "        self.features = torch.tensor(features, dtype=torch.float32)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.features)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.features[idx]\n",
    "\n",
    "train_dataset = CustomDataset(train_data)\n",
    "test_dataset = CustomDataset(val_data)\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, pin_memory=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False, pin_memory=True)\n",
    "\n",
    "class Autoencoder(nn.Module):\n",
    "    def __init__(self, input_dim, encoding_dim, max_categories):\n",
    "        super(Autoencoder, self).__init__()\n",
    "        self.encoder = nn.Sequential(\n",
    "            nn.Linear(input_dim, 128),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(128, 64),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(64, encoding_dim)\n",
    "        )\n",
    "        self.decoder = nn.ModuleList([\n",
    "            nn.Sequential(\n",
    "                nn.Linear(encoding_dim, 64),\n",
    "                nn.ReLU(),\n",
    "                nn.Linear(64, 128),\n",
    "                nn.ReLU(),\n",
    "                nn.Linear(128, num_classes),\n",
    "                nn.Softmax(dim=-1)\n",
    "            ) for num_classes in max_categories\n",
    "        ])\n",
    "\n",
    "    def forward(self, x):\n",
    "        encoded = self.encoder(x)\n",
    "        decoded = torch.stack([decoder(encoded) for decoder in self.decoder], dim=1)\n",
    "        return encoded, decoded\n",
    "\n",
    "# Determine the maximum number of categories for each feature\n",
    "max_categories = (X_train_encoded.max(axis=0) + 1).astype(int).tolist()\n",
    "\n",
    "# Hyperparameters\n",
    "input_dim = X_train_encoded.shape[1]\n",
    "encoding_dim = 16  # Latent space dimension\n",
    "lr = 0.001\n",
    "weight_decay = 1e-5  # Regularization term\n",
    "batch_size = 32\n",
    "epochs = 100\n",
    "patience = 10  # Early stopping patience\n",
    "\n",
    "# Initialize model, loss function, and optimizer\n",
    "model = Autoencoder(input_dim=input_dim, encoding_dim=encoding_dim, max_categories=max_categories)\n",
    "criterion = nn.CrossEntropyLoss()  # Use CrossEntropyLoss for categorical data\n",
    "optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)\n",
    "\n",
    "# Early stopping setup\n",
    "best_val_loss = float('inf')\n",
    "patience_counter = 0\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    model.train()\n",
    "    train_loss = 0.0\n",
    "\n",
    "    for batch in train_loader:\n",
    "        optimizer.zero_grad()\n",
    "        _, decoded = model(batch)\n",
    "\n",
    "        # Apply CrossEntropyLoss to each feature independently\n",
    "        loss = 0\n",
    "        for i in range(input_dim):\n",
    "            loss += criterion(decoded[:, i, :], batch[:, i].long() - 1)  # Adjust indices for 1-based encoding\n",
    "        \n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        train_loss += loss.item()\n",
    "\n",
    "    train_loss /= len(train_loader)\n",
    "\n",
    "    # Validation\n",
    "    model.eval()\n",
    "    val_loss = 0.0\n",
    "    with torch.no_grad():\n",
    "        for batch in test_loader:\n",
    "            _, decoded = model(batch)\n",
    "            loss = 0\n",
    "            for i in range(input_dim):\n",
    "                loss += criterion(decoded[:, i, :], batch[:, i].long() - 1)\n",
    "            val_loss += loss.item()\n",
    "\n",
    "    val_loss /= len(test_loader)\n",
    "\n",
    "    print(f\"Epoch {epoch+1}/{epochs}, Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}\")\n",
    "\n",
    "    # Check for early stopping\n",
    "    if val_loss < best_val_loss:\n",
    "        best_val_loss = val_loss\n",
    "        patience_counter = 0\n",
    "        torch.save(model.state_dict(), \"best_autoencoder.pth\")  # Save the best model\n",
    "    else:\n",
    "        patience_counter += 1\n",
    "        if patience_counter >= patience:\n",
    "            print(\"Early stopping triggered.\")\n",
    "            break\n",
    "        \n",
    "# Load the best model\n",
    "model.load_state_dict(torch.load(\"best_autoencoder.pth\"))\n",
    "print(\"Model training complete. Best model loaded.\")\n",
    "\n",
    "# Feature extraction\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    train_features = model.encoder(train_data).numpy()\n",
    "    val_features = model.encoder(val_data).numpy()\n",
    "\n",
    "print(\"Extracted features shape:\", train_features.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Building\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('data/processed/train.csv')\n",
    "test_df = pd.read_csv('data/processed/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account_number</th>\n",
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       "      <th>bureau_2</th>\n",
       "      <th>bureau_3</th>\n",
       "      <th>bureau_5</th>\n",
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       "      <th>bureau_enquiry_49</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77444 rows × 473 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       account_number  bureau_1  bureau_2  bureau_3  bureau_5  bureau_6  \\\n",
       "0             47642.0       2.0       0.0       0.0       0.0       0.0   \n",
       "1             95032.0       0.0       6.0       1.0       1.0       4.0   \n",
       "2             40510.0       0.0       0.0       1.0       0.0       0.0   \n",
       "3             51692.0       0.0       0.0       2.0       0.0       0.0   \n",
       "4             27459.0       3.0       5.0       2.0       1.0       0.0   \n",
       "...               ...       ...       ...       ...       ...       ...   \n",
       "77439          6266.0       0.0       4.0       1.0       0.0       0.0   \n",
       "77440         54887.0       0.0       0.0       0.0       0.0       0.0   \n",
       "77441         76821.0       0.0       0.0       1.0       0.0       0.0   \n",
       "77442           861.0       0.0       1.0       0.0       0.0       0.0   \n",
       "77443         15796.0       1.0       4.0       0.0       0.0       2.0   \n",
       "\n",
       "       bureau_7  bureau_8  bureau_9  bureau_10  ...  bureau_enquiry_41  \\\n",
       "0           1.0       2.0       1.0        2.0  ...                5.0   \n",
       "1          13.0       7.0      31.0        6.0  ...               17.0   \n",
       "2           7.0       1.0      36.0        1.0  ...               15.0   \n",
       "3           3.0       6.0       3.0        6.0  ...                0.0   \n",
       "4           8.0       6.0      15.0        6.0  ...               22.0   \n",
       "...         ...       ...       ...        ...  ...                ...   \n",
       "77439       5.0       1.0      27.0        1.0  ...               14.0   \n",
       "77440       1.0       1.0       2.0        1.0  ...                4.0   \n",
       "77441      12.0       1.0      14.0        1.0  ...                6.0   \n",
       "77442      17.0       0.0      20.0        0.0  ...               12.0   \n",
       "77443       3.0       6.0       9.0        6.0  ...               36.0   \n",
       "\n",
       "       bureau_enquiry_42  bureau_enquiry_43  bureau_enquiry_44  \\\n",
       "0                    2.0                3.0                2.0   \n",
       "1                    3.0               14.0                0.0   \n",
       "2                    1.0               14.0                0.0   \n",
       "3                    0.0                0.0                0.0   \n",
       "4                    1.0               21.0                1.0   \n",
       "...                  ...                ...                ...   \n",
       "77439                0.0               14.0                0.0   \n",
       "77440                1.0                3.0                0.0   \n",
       "77441                0.0                6.0                0.0   \n",
       "77442                0.0               12.0                0.0   \n",
       "77443                2.0               34.0                0.0   \n",
       "\n",
       "       bureau_enquiry_45  bureau_enquiry_46  bureau_enquiry_48  \\\n",
       "0                    0.0                0.0                0.0   \n",
       "1                    4.0                2.0                0.0   \n",
       "2                    3.0                1.0                0.0   \n",
       "3                    0.0                0.0                0.0   \n",
       "4                    2.0                0.0                0.0   \n",
       "...                  ...                ...                ...   \n",
       "77439                4.0                0.0                0.0   \n",
       "77440                0.0                1.0                0.0   \n",
       "77441                0.0                0.0                0.0   \n",
       "77442                0.0                0.0                0.0   \n",
       "77443                4.0                0.0                0.0   \n",
       "\n",
       "       bureau_enquiry_49  bureau_enquiry_50  bad_flag  \n",
       "0                    0.0                3.0         0  \n",
       "1                    0.0               10.0         0  \n",
       "2                    0.0                7.0         0  \n",
       "3                    0.0                0.0         0  \n",
       "4                    0.0               18.0         0  \n",
       "...                  ...                ...       ...  \n",
       "77439                0.0                7.0         0  \n",
       "77440                0.0                3.0         0  \n",
       "77441                0.0                6.0         0  \n",
       "77442                0.0               11.0         0  \n",
       "77443                0.0               18.0         0  \n",
       "\n",
       "[77444 rows x 473 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OVERSAMPLING Using SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2025-01-13 13:56:51,458] A new study created in memory with name: no-name-177f4b7b-c8b7-4ce9-a1c2-a66eb4460235\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:56:52] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:56:53,939] Trial 0 finished with value: 0.7973209643752279 and parameters: {'max_depth': 9, 'eta': 0.13228187354453635, 'subsample': 0.9187910846452179, 'colsample_bytree': 0.6676562252912284, 'min_child_weight': 5, 'gamma': 3.6055777720871336, 'lambda': 0.5647342412785339, 'alpha': 5.238452993284178, 'scale_pos_weight': 1.4490351308704557, 'n_estimators': 189}. Best is trial 0 with value: 0.7973209643752279.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:56:54] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:56:59,264] Trial 1 finished with value: 0.7916004182477051 and parameters: {'max_depth': 8, 'eta': 0.022300525357483447, 'subsample': 0.7973693654959171, 'colsample_bytree': 0.6518477092579006, 'min_child_weight': 16, 'gamma': 0.6497516671969178, 'lambda': 2.36514599244678, 'alpha': 1.6938455236538852, 'scale_pos_weight': 9.112873582089307, 'n_estimators': 380}. Best is trial 0 with value: 0.7973209643752279.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:56:59] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:57:04,504] Trial 2 finished with value: 0.7860843401541076 and parameters: {'max_depth': 11, 'eta': 0.04574630710794914, 'subsample': 0.5737500417272698, 'colsample_bytree': 0.5053353343411073, 'min_child_weight': 18, 'gamma': 4.90788810844721, 'lambda': 4.714324036241207, 'alpha': 8.48253589895169, 'scale_pos_weight': 7.96674297160121, 'n_estimators': 286}. Best is trial 0 with value: 0.7973209643752279.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:57:04] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:57:08,779] Trial 3 finished with value: 0.779498878723685 and parameters: {'max_depth': 9, 'eta': 0.09273218603957645, 'subsample': 0.7662301436188699, 'colsample_bytree': 0.6038290960783664, 'min_child_weight': 1, 'gamma': 0.8991182132955094, 'lambda': 5.403031408192359, 'alpha': 9.540454004699535, 'scale_pos_weight': 9.051476108325046, 'n_estimators': 194}. Best is trial 0 with value: 0.7973209643752279.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:57:09] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:57:15,495] Trial 4 finished with value: 0.7764057662313477 and parameters: {'max_depth': 14, 'eta': 0.10426900440403049, 'subsample': 0.7565297272149174, 'colsample_bytree': 0.9196763587644364, 'min_child_weight': 4, 'gamma': 0.7067825910133702, 'lambda': 1.155508169806051, 'alpha': 5.759379491724915, 'scale_pos_weight': 3.105629867708419, 'n_estimators': 382}. Best is trial 0 with value: 0.7973209643752279.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:57:15] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:57:19,922] Trial 5 finished with value: 0.7898393548587347 and parameters: {'max_depth': 15, 'eta': 0.005906442964796473, 'subsample': 0.6692483865526726, 'colsample_bytree': 0.7802115936910443, 'min_child_weight': 8, 'gamma': 1.529634966649186, 'lambda': 2.398362493102205, 'alpha': 9.145540757116365, 'scale_pos_weight': 6.514757621021337, 'n_estimators': 123}. Best is trial 0 with value: 0.7973209643752279.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:57:20] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:57:22,690] Trial 6 finished with value: 0.8018314826454361 and parameters: {'max_depth': 7, 'eta': 0.11910035549469906, 'subsample': 0.832664727111832, 'colsample_bytree': 0.7104881123079096, 'min_child_weight': 19, 'gamma': 4.533997930754879, 'lambda': 6.9218604622629085, 'alpha': 8.011786271152076, 'scale_pos_weight': 1.8071359521958301, 'n_estimators': 300}. Best is trial 6 with value: 0.8018314826454361.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:57:23] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:57:27,815] Trial 7 finished with value: 0.7909896330438966 and parameters: {'max_depth': 6, 'eta': 0.005475979951595051, 'subsample': 0.686124333057482, 'colsample_bytree': 0.7174654252355892, 'min_child_weight': 19, 'gamma': 0.7497279481160596, 'lambda': 5.565595991570808, 'alpha': 8.17344685235416, 'scale_pos_weight': 1.3176053998835147, 'n_estimators': 498}. Best is trial 6 with value: 0.8018314826454361.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:57:28] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:57:32,561] Trial 8 finished with value: 0.7931096794468887 and parameters: {'max_depth': 5, 'eta': 0.006026302353303401, 'subsample': 0.6613627245457787, 'colsample_bytree': 0.7193915019875988, 'min_child_weight': 4, 'gamma': 0.8475140298967476, 'lambda': 1.498704545392321, 'alpha': 3.5431822484886846, 'scale_pos_weight': 1.322350837358607, 'n_estimators': 452}. Best is trial 6 with value: 0.8018314826454361.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:57:33] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:57:36,469] Trial 9 finished with value: 0.7612401160850772 and parameters: {'max_depth': 4, 'eta': 0.27121565799178704, 'subsample': 0.8306391361818738, 'colsample_bytree': 0.6565611476456708, 'min_child_weight': 17, 'gamma': 4.923878200785332, 'lambda': 5.598237048610395, 'alpha': 4.239670619721673, 'scale_pos_weight': 5.092367191925177, 'n_estimators': 491}. Best is trial 6 with value: 0.8018314826454361.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:57:37] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:57:38,686] Trial 10 finished with value: 0.7871045464068719 and parameters: {'max_depth': 12, 'eta': 0.02373840645779114, 'subsample': 0.9906957744513369, 'colsample_bytree': 0.8403001072499616, 'min_child_weight': 13, 'gamma': 3.079209673769248, 'lambda': 9.93693831121028, 'alpha': 7.446200926777182, 'scale_pos_weight': 3.6161213546427673, 'n_estimators': 61}. Best is trial 6 with value: 0.8018314826454361.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:57:39] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:57:40,934] Trial 11 finished with value: 0.7844063055303364 and parameters: {'max_depth': 7, 'eta': 0.2949789136581894, 'subsample': 0.9217171168352809, 'colsample_bytree': 0.5545860704900857, 'min_child_weight': 8, 'gamma': 3.6749385913544264, 'lambda': 8.257370950128218, 'alpha': 5.931081320777862, 'scale_pos_weight': 2.96305248071459, 'n_estimators': 223}. Best is trial 6 with value: 0.8018314826454361.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:57:41] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:57:43,409] Trial 12 finished with value: 0.7999386013920897 and parameters: {'max_depth': 11, 'eta': 0.11463600596059907, 'subsample': 0.8952451974953572, 'colsample_bytree': 0.8238009023372139, 'min_child_weight': 12, 'gamma': 4.00158980618531, 'lambda': 7.828641597293458, 'alpha': 6.676518633292433, 'scale_pos_weight': 1.0793496747671734, 'n_estimators': 298}. Best is trial 6 with value: 0.8018314826454361.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:57:43] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:57:48,227] Trial 13 finished with value: 0.7925785281405436 and parameters: {'max_depth': 11, 'eta': 0.06113334439287468, 'subsample': 0.8960047652952505, 'colsample_bytree': 0.9994635879384886, 'min_child_weight': 13, 'gamma': 3.9898977560544373, 'lambda': 7.230847351063957, 'alpha': 6.65515999224861, 'scale_pos_weight': 4.5351354184754795, 'n_estimators': 307}. Best is trial 6 with value: 0.8018314826454361.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:57:48] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:57:51,032] Trial 14 finished with value: 0.7890129470555827 and parameters: {'max_depth': 3, 'eta': 0.16179982315854022, 'subsample': 0.8671593695635187, 'colsample_bytree': 0.8130382257032818, 'min_child_weight': 13, 'gamma': 2.3752847943922353, 'lambda': 7.64228202195904, 'alpha': 2.8182806347467877, 'scale_pos_weight': 2.3033418406864388, 'n_estimators': 339}. Best is trial 6 with value: 0.8018314826454361.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:57:51] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:57:54,923] Trial 15 finished with value: 0.7853325224449255 and parameters: {'max_depth': 12, 'eta': 0.07036327777046024, 'subsample': 0.9902044333506204, 'colsample_bytree': 0.8849297383094437, 'min_child_weight': 20, 'gamma': 4.260057116150707, 'lambda': 9.475893546044418, 'alpha': 0.06495762026366592, 'scale_pos_weight': 6.502468439624124, 'n_estimators': 250}. Best is trial 6 with value: 0.8018314826454361.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:57:55] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:58:01,778] Trial 16 finished with value: 0.8011400935819539 and parameters: {'max_depth': 10, 'eta': 0.029520453064056956, 'subsample': 0.5000499238946585, 'colsample_bytree': 0.7561986423232355, 'min_child_weight': 10, 'gamma': 2.729589687799156, 'lambda': 6.520561651435843, 'alpha': 7.07346188338065, 'scale_pos_weight': 4.069231622749562, 'n_estimators': 378}. Best is trial 6 with value: 0.8018314826454361.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:58:02] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:58:07,780] Trial 17 finished with value: 0.7972528730280668 and parameters: {'max_depth': 7, 'eta': 0.023357988089222362, 'subsample': 0.5677616844202709, 'colsample_bytree': 0.7424348870933823, 'min_child_weight': 9, 'gamma': 2.1247195249248634, 'lambda': 3.9067537840071465, 'alpha': 7.42330574438535, 'scale_pos_weight': 4.08559099835859, 'n_estimators': 412}. Best is trial 6 with value: 0.8018314826454361.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:58:08] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:58:13,219] Trial 18 finished with value: 0.7973379387139077 and parameters: {'max_depth': 10, 'eta': 0.010870083534285138, 'subsample': 0.5192546342428405, 'colsample_bytree': 0.5955434929330706, 'min_child_weight': 15, 'gamma': 3.0392480955639645, 'lambda': 6.257953339026085, 'alpha': 8.063697536130798, 'scale_pos_weight': 2.3897070717577757, 'n_estimators': 363}. Best is trial 6 with value: 0.8018314826454361.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:58:13] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:58:18,859] Trial 19 finished with value: 0.7933505210636994 and parameters: {'max_depth': 6, 'eta': 0.041788697519230866, 'subsample': 0.7085945058872583, 'colsample_bytree': 0.7673402344954785, 'min_child_weight': 11, 'gamma': 1.6579776666036514, 'lambda': 6.587212092352466, 'alpha': 9.996227477728798, 'scale_pos_weight': 5.96967982080136, 'n_estimators': 415}. Best is trial 6 with value: 0.8018314826454361.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:58:19] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:58:27,914] Trial 20 finished with value: 0.8012844239588426 and parameters: {'max_depth': 13, 'eta': 0.016563413256811182, 'subsample': 0.6223414357772703, 'colsample_bytree': 0.6888554502670041, 'min_child_weight': 6, 'gamma': 2.706727201443057, 'lambda': 4.0010195990037465, 'alpha': 4.591188551817126, 'scale_pos_weight': 4.8351099369683235, 'n_estimators': 330}. Best is trial 6 with value: 0.8018314826454361.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:58:28] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:58:36,819] Trial 21 finished with value: 0.7974538491980352 and parameters: {'max_depth': 13, 'eta': 0.01623961771875871, 'subsample': 0.605164068868698, 'colsample_bytree': 0.6956208830696358, 'min_child_weight': 6, 'gamma': 2.7980812904477523, 'lambda': 3.6886571620007174, 'alpha': 4.595570352240621, 'scale_pos_weight': 5.216766259638701, 'n_estimators': 326}. Best is trial 6 with value: 0.8018314826454361.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:58:37] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:58:43,734] Trial 22 finished with value: 0.7954458334303296 and parameters: {'max_depth': 15, 'eta': 0.012470448178463895, 'subsample': 0.512970059623766, 'colsample_bytree': 0.6090063961327755, 'min_child_weight': 10, 'gamma': 1.5815825539670632, 'lambda': 4.2576726239547416, 'alpha': 6.622174489499489, 'scale_pos_weight': 7.4603084106620035, 'n_estimators': 253}. Best is trial 6 with value: 0.8018314826454361.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:58:44] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:58:49,810] Trial 23 finished with value: 0.7942871165739381 and parameters: {'max_depth': 8, 'eta': 0.027862447496490462, 'subsample': 0.6206073445960074, 'colsample_bytree': 0.7704669935657449, 'min_child_weight': 7, 'gamma': 0.1182463794757278, 'lambda': 8.818640440805712, 'alpha': 2.7193565447589307, 'scale_pos_weight': 4.4564719046250385, 'n_estimators': 342}. Best is trial 6 with value: 0.8018314826454361.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:58:50] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:58:59,320] Trial 24 finished with value: 0.7986536924521421 and parameters: {'max_depth': 13, 'eta': 0.009470031637772472, 'subsample': 0.5401859435969186, 'colsample_bytree': 0.6879786975144884, 'min_child_weight': 1, 'gamma': 3.376473585107339, 'lambda': 6.74964091067736, 'alpha': 5.474451857968, 'scale_pos_weight': 2.3380939163586056, 'n_estimators': 438}. Best is trial 6 with value: 0.8018314826454361.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:58:59] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:59:04,276] Trial 25 finished with value: 0.7971365745590551 and parameters: {'max_depth': 9, 'eta': 0.030982346424610063, 'subsample': 0.6347088134568036, 'colsample_bytree': 0.629337641610992, 'min_child_weight': 10, 'gamma': 2.1977007025996977, 'lambda': 2.6598005706362704, 'alpha': 7.501111642229456, 'scale_pos_weight': 3.1877713668625045, 'n_estimators': 273}. Best is trial 6 with value: 0.8018314826454361.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:59:04] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:59:12,201] Trial 26 finished with value: 0.8020848367747593 and parameters: {'max_depth': 10, 'eta': 0.01603531860511072, 'subsample': 0.7267557387132025, 'colsample_bytree': 0.7216338084184113, 'min_child_weight': 15, 'gamma': 2.786538339792345, 'lambda': 6.0498728044707715, 'alpha': 8.82304768116133, 'scale_pos_weight': 5.927954293990842, 'n_estimators': 393}. Best is trial 26 with value: 0.8020848367747593.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:59:12] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:59:21,908] Trial 27 finished with value: 0.7968629482195374 and parameters: {'max_depth': 13, 'eta': 0.016591162154696446, 'subsample': 0.7140791067216425, 'colsample_bytree': 0.5643948709935125, 'min_child_weight': 15, 'gamma': 1.868738611544761, 'lambda': 3.5192943690982714, 'alpha': 8.878419883593072, 'scale_pos_weight': 7.260955849099874, 'n_estimators': 410}. Best is trial 26 with value: 0.8020848367747593.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:59:22] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:59:28,459] Trial 28 finished with value: 0.800206407958346 and parameters: {'max_depth': 7, 'eta': 0.007843252132711823, 'subsample': 0.7818776420701181, 'colsample_bytree': 0.7083135557006295, 'min_child_weight': 18, 'gamma': 4.540037162177577, 'lambda': 5.1462637316402535, 'alpha': 3.9679749131113526, 'scale_pos_weight': 5.810645842429396, 'n_estimators': 463}. Best is trial 26 with value: 0.8020848367747593.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:59:29] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:59:33,581] Trial 29 finished with value: 0.7967348162115604 and parameters: {'max_depth': 10, 'eta': 0.01637707119780042, 'subsample': 0.8214209935046368, 'colsample_bytree': 0.682276972659887, 'min_child_weight': 20, 'gamma': 3.470443362274577, 'lambda': 6.054619493161325, 'alpha': 5.06374275062435, 'scale_pos_weight': 8.124990441304304, 'n_estimators': 209}. Best is trial 26 with value: 0.8020848367747593.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:59:34] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:59:36,738] Trial 30 finished with value: 0.7984150817484151 and parameters: {'max_depth': 8, 'eta': 0.05575669868734335, 'subsample': 0.7334745005230299, 'colsample_bytree': 0.7960221890575864, 'min_child_weight': 16, 'gamma': 2.6225327994556715, 'lambda': 4.461914393118773, 'alpha': 6.0916642401588925, 'scale_pos_weight': 6.506296320088074, 'n_estimators': 156}. Best is trial 26 with value: 0.8020848367747593.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:59:37] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:59:44,451] Trial 31 finished with value: 0.7905178434248201 and parameters: {'max_depth': 10, 'eta': 0.03261772791933909, 'subsample': 0.5814864758072407, 'colsample_bytree': 0.7330859562522134, 'min_child_weight': 3, 'gamma': 2.9432166913943436, 'lambda': 7.30478629622439, 'alpha': 7.809794976537207, 'scale_pos_weight': 3.898187576072338, 'n_estimators': 372}. Best is trial 26 with value: 0.8020848367747593.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:59:45] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:59:48,185] Trial 32 finished with value: 0.7779962132675312 and parameters: {'max_depth': 9, 'eta': 0.1560662810840952, 'subsample': 0.8277363846055107, 'colsample_bytree': 0.6479477294280627, 'min_child_weight': 14, 'gamma': 2.496053082846157, 'lambda': 6.841575242169178, 'alpha': 8.856057949904988, 'scale_pos_weight': 4.768081458834174, 'n_estimators': 323}. Best is trial 26 with value: 0.8020848367747593.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:59:48] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 13:59:55,154] Trial 33 finished with value: 0.8021565169820984 and parameters: {'max_depth': 12, 'eta': 0.017298265605109896, 'subsample': 0.6382490773437288, 'colsample_bytree': 0.8604477185854154, 'min_child_weight': 6, 'gamma': 3.2885101440334834, 'lambda': 3.15797888771168, 'alpha': 6.989010030590853, 'scale_pos_weight': 1.9250708786681745, 'n_estimators': 391}. Best is trial 33 with value: 0.8021565169820984.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:59:55] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:00:01,652] Trial 34 finished with value: 0.8004673277929092 and parameters: {'max_depth': 12, 'eta': 0.014000381312400818, 'subsample': 0.6395721817529628, 'colsample_bytree': 0.862518341204397, 'min_child_weight': 5, 'gamma': 3.3193244828616795, 'lambda': 3.1624074830213424, 'alpha': 8.428970912649575, 'scale_pos_weight': 1.7286663394112074, 'n_estimators': 402}. Best is trial 33 with value: 0.8021565169820984.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:00:02] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:00:07,725] Trial 35 finished with value: 0.8006629691707212 and parameters: {'max_depth': 14, 'eta': 0.01952001450456765, 'subsample': 0.6921090187316317, 'colsample_bytree': 0.9348470895143848, 'min_child_weight': 6, 'gamma': 4.245868194741664, 'lambda': 0.5980937748474533, 'alpha': 9.513087519294668, 'scale_pos_weight': 1.9189472259241462, 'n_estimators': 352}. Best is trial 33 with value: 0.8021565169820984.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:00:08] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:00:17,392] Trial 36 finished with value: 0.7933828208053014 and parameters: {'max_depth': 11, 'eta': 0.008472006750212795, 'subsample': 0.7382774703235392, 'colsample_bytree': 0.6666731845140648, 'min_child_weight': 3, 'gamma': 3.800501903260308, 'lambda': 1.7799341161413178, 'alpha': 4.851047779982628, 'scale_pos_weight': 8.351870849206033, 'n_estimators': 284}. Best is trial 33 with value: 0.8021565169820984.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:00:17] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:00:25,564] Trial 37 finished with value: 0.7967492686484935 and parameters: {'max_depth': 14, 'eta': 0.02004829475566096, 'subsample': 0.787444383123307, 'colsample_bytree': 0.9278448933014896, 'min_child_weight': 17, 'gamma': 1.2356356426000368, 'lambda': 4.801543512083787, 'alpha': 6.3268977845095, 'scale_pos_weight': 9.887169793529141, 'n_estimators': 260}. Best is trial 33 with value: 0.8021565169820984.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:00:26] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:00:30,855] Trial 38 finished with value: 0.7968922410782876 and parameters: {'max_depth': 12, 'eta': 0.047393893192094895, 'subsample': 0.6595398512869506, 'colsample_bytree': 0.9731540665748823, 'min_child_weight': 8, 'gamma': 4.4850379759895045, 'lambda': 3.004950372200931, 'alpha': 8.62107001125246, 'scale_pos_weight': 2.8677687731641908, 'n_estimators': 318}. Best is trial 33 with value: 0.8021565169820984.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:00:31] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:00:37,527] Trial 39 finished with value: 0.7774082221756641 and parameters: {'max_depth': 13, 'eta': 0.07809594669820677, 'subsample': 0.596557273411724, 'colsample_bytree': 0.797233642858183, 'min_child_weight': 18, 'gamma': 3.210605472808254, 'lambda': 5.729999015362282, 'alpha': 9.39270704685346, 'scale_pos_weight': 6.983428044231972, 'n_estimators': 431}. Best is trial 33 with value: 0.8021565169820984.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:00:38] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:00:41,844] Trial 40 finished with value: 0.7984518433161844 and parameters: {'max_depth': 5, 'eta': 0.0071516659886224155, 'subsample': 0.766019381667448, 'colsample_bytree': 0.8835832005816171, 'min_child_weight': 3, 'gamma': 4.739179488382071, 'lambda': 2.089819528267414, 'alpha': 9.94290142376431, 'scale_pos_weight': 5.64120307009938, 'n_estimators': 391}. Best is trial 33 with value: 0.8021565169820984.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:00:42] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:00:48,370] Trial 41 finished with value: 0.7905940824545474 and parameters: {'max_depth': 10, 'eta': 0.033245780319189656, 'subsample': 0.5497281171401206, 'colsample_bytree': 0.74525756011576, 'min_child_weight': 11, 'gamma': 2.7547796232462187, 'lambda': 6.134116535802958, 'alpha': 7.049396862663584, 'scale_pos_weight': 3.3482789831423805, 'n_estimators': 381}. Best is trial 33 with value: 0.8021565169820984.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:00:48] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:00:58,409] Trial 42 finished with value: 0.7934706023853311 and parameters: {'max_depth': 11, 'eta': 0.026586259587361105, 'subsample': 0.8084878507924985, 'colsample_bytree': 0.7588889009036504, 'min_child_weight': 7, 'gamma': 2.3103707370930904, 'lambda': 4.992065884679444, 'alpha': 5.492760258366757, 'scale_pos_weight': 5.061147705374484, 'n_estimators': 470}. Best is trial 33 with value: 0.8021565169820984.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:00:58] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:01:03,045] Trial 43 finished with value: 0.7612206198446508 and parameters: {'max_depth': 8, 'eta': 0.21986537307080808, 'subsample': 0.6707386714288773, 'colsample_bytree': 0.7135769777316446, 'min_child_weight': 9, 'gamma': 1.9206846989833244, 'lambda': 8.154518724363863, 'alpha': 7.969824318654022, 'scale_pos_weight': 1.5971463903022087, 'n_estimators': 369}. Best is trial 33 with value: 0.8021565169820984.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:01:04] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:01:11,365] Trial 44 finished with value: 0.7984472844937962 and parameters: {'max_depth': 10, 'eta': 0.013158572895190564, 'subsample': 0.5052145843002915, 'colsample_bytree': 0.6388350232743915, 'min_child_weight': 5, 'gamma': 2.699352894816903, 'lambda': 4.1518796379511675, 'alpha': 3.534969740934053, 'scale_pos_weight': 4.060239422496852, 'n_estimators': 353}. Best is trial 33 with value: 0.8021565169820984.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:01:11] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:01:14,842] Trial 45 finished with value: 0.8000897215044502 and parameters: {'max_depth': 9, 'eta': 0.020053051309132215, 'subsample': 0.5601383823208854, 'colsample_bytree': 0.8379935820953364, 'min_child_weight': 19, 'gamma': 3.5748075520026426, 'lambda': 5.384195274038616, 'alpha': 6.836701706265312, 'scale_pos_weight': 1.109162609169811, 'n_estimators': 296}. Best is trial 33 with value: 0.8021565169820984.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:01:15] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:01:22,100] Trial 46 finished with value: 0.794728158390949 and parameters: {'max_depth': 12, 'eta': 0.03963566077469297, 'subsample': 0.7131067293018438, 'colsample_bytree': 0.6741008564430861, 'min_child_weight': 12, 'gamma': 3.125552392078285, 'lambda': 7.083868504715147, 'alpha': 9.061097423447874, 'scale_pos_weight': 2.6359217477573815, 'n_estimators': 434}. Best is trial 33 with value: 0.8021565169820984.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:01:22] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:01:26,466] Trial 47 finished with value: 0.7922821076890845 and parameters: {'max_depth': 14, 'eta': 0.010457794407390758, 'subsample': 0.8559181816115604, 'colsample_bytree': 0.5092291565748632, 'min_child_weight': 7, 'gamma': 2.8766218435167445, 'lambda': 5.9051133397861575, 'alpha': 7.134837178997057, 'scale_pos_weight': 2.025000321617253, 'n_estimators': 232}. Best is trial 33 with value: 0.8021565169820984.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:01:27] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:01:30,885] Trial 48 finished with value: 0.796031884597776 and parameters: {'max_depth': 6, 'eta': 0.024403143444049175, 'subsample': 0.9434968978088907, 'colsample_bytree': 0.7294733285236482, 'min_child_weight': 2, 'gamma': 2.5219442042544475, 'lambda': 4.638631952931104, 'alpha': 8.407796647973194, 'scale_pos_weight': 6.174859560185526, 'n_estimators': 334}. Best is trial 33 with value: 0.8021565169820984.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:01:31] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:01:37,488] Trial 49 finished with value: 0.7746874781758503 and parameters: {'max_depth': 11, 'eta': 0.08978843464756127, 'subsample': 0.5930345475117399, 'colsample_bytree': 0.7803190518087108, 'min_child_weight': 9, 'gamma': 3.7098353777969466, 'lambda': 6.447232792721498, 'alpha': 7.675636618338812, 'scale_pos_weight': 5.374720018172974, 'n_estimators': 389}. Best is trial 33 with value: 0.8021565169820984.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:01:38] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:01:43,341] Trial 50 finished with value: 0.7984304071513374 and parameters: {'max_depth': 8, 'eta': 0.014502698155634175, 'subsample': 0.537099722705381, 'colsample_bytree': 0.7024389517025822, 'min_child_weight': 14, 'gamma': 1.9927656004633996, 'lambda': 3.0311579006462726, 'alpha': 5.762815399674191, 'scale_pos_weight': 3.6579433148459266, 'n_estimators': 306}. Best is trial 33 with value: 0.8021565169820984.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:01:44] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:01:50,007] Trial 51 finished with value: 0.8027008597745033 and parameters: {'max_depth': 14, 'eta': 0.01869016069752332, 'subsample': 0.6960334758782868, 'colsample_bytree': 0.9214904290761772, 'min_child_weight': 6, 'gamma': 4.14298648492744, 'lambda': 0.695566296051147, 'alpha': 9.737455647156077, 'scale_pos_weight': 1.8587129225964407, 'n_estimators': 361}. Best is trial 51 with value: 0.8027008597745033.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:01:50] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:01:55,609] Trial 52 finished with value: 0.8021151965919407 and parameters: {'max_depth': 15, 'eta': 0.02043160976123357, 'subsample': 0.6790773138014139, 'colsample_bytree': 0.9554366791804076, 'min_child_weight': 5, 'gamma': 3.8896605152757378, 'lambda': 1.202825471639215, 'alpha': 9.34080614624655, 'scale_pos_weight': 1.3583413231389507, 'n_estimators': 357}. Best is trial 51 with value: 0.8027008597745033.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:01:56] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:02:01,722] Trial 53 finished with value: 0.8028620674938506 and parameters: {'max_depth': 15, 'eta': 0.019159047922049463, 'subsample': 0.6808418337434767, 'colsample_bytree': 0.9543694092704157, 'min_child_weight': 4, 'gamma': 4.13912653519969, 'lambda': 0.131617244187466, 'alpha': 9.331537505496158, 'scale_pos_weight': 1.45959247762842, 'n_estimators': 360}. Best is trial 53 with value: 0.8028620674938506.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:02:02] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:02:07,686] Trial 54 finished with value: 0.79756665580309 and parameters: {'max_depth': 15, 'eta': 0.011332274270548367, 'subsample': 0.6847484481516255, 'colsample_bytree': 0.9530967972517996, 'min_child_weight': 4, 'gamma': 4.086500464421992, 'lambda': 0.03211218900274071, 'alpha': 9.242822877123782, 'scale_pos_weight': 1.4658166513448687, 'n_estimators': 358}. Best is trial 53 with value: 0.8028620674938506.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:02:08] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:02:12,653] Trial 55 finished with value: 0.8023529343296785 and parameters: {'max_depth': 15, 'eta': 0.019940741166227682, 'subsample': 0.7446359276041256, 'colsample_bytree': 0.9050463928689632, 'min_child_weight': 5, 'gamma': 4.999121942056909, 'lambda': 1.1764314123747237, 'alpha': 9.747149690954874, 'scale_pos_weight': 1.0265345100557002, 'n_estimators': 394}. Best is trial 53 with value: 0.8028620674938506.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:02:13] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:02:17,857] Trial 56 finished with value: 0.8027731219591685 and parameters: {'max_depth': 15, 'eta': 0.02030641606648888, 'subsample': 0.748900873675866, 'colsample_bytree': 0.9085417469140389, 'min_child_weight': 5, 'gamma': 4.956575926994933, 'lambda': 1.0819915677736889, 'alpha': 9.855050121727487, 'scale_pos_weight': 1.0149505517104431, 'n_estimators': 423}. Best is trial 53 with value: 0.8028620674938506.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:02:18] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:02:23,377] Trial 57 finished with value: 0.8029502370587642 and parameters: {'max_depth': 15, 'eta': 0.020192619101571573, 'subsample': 0.7532559911840444, 'colsample_bytree': 0.9015539233571785, 'min_child_weight': 5, 'gamma': 4.799246471184046, 'lambda': 0.9369864483458712, 'alpha': 9.691207053262051, 'scale_pos_weight': 1.0365816151368894, 'n_estimators': 485}. Best is trial 57 with value: 0.8029502370587642.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:02:24] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:02:28,313] Trial 58 finished with value: 0.8029748740989051 and parameters: {'max_depth': 14, 'eta': 0.02412554693204964, 'subsample': 0.7510427517792699, 'colsample_bytree': 0.9060768468731001, 'min_child_weight': 2, 'gamma': 4.970469980214026, 'lambda': 0.5983879553184585, 'alpha': 9.669497895463556, 'scale_pos_weight': 1.080836931042514, 'n_estimators': 485}. Best is trial 58 with value: 0.8029748740989051.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:02:28] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:02:32,736] Trial 59 finished with value: 0.8035157250273529 and parameters: {'max_depth': 14, 'eta': 0.023922106232947996, 'subsample': 0.7507017977533865, 'colsample_bytree': 0.9022124547472382, 'min_child_weight': 4, 'gamma': 4.969911891597847, 'lambda': 0.6760257302538082, 'alpha': 9.800301905110874, 'scale_pos_weight': 1.0556778260189643, 'n_estimators': 492}. Best is trial 59 with value: 0.8035157250273529.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:02:33] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:02:40,478] Trial 60 finished with value: 0.80296022766953 and parameters: {'max_depth': 14, 'eta': 0.024188520453158162, 'subsample': 0.7558625887126394, 'colsample_bytree': 0.9110199685991903, 'min_child_weight': 2, 'gamma': 4.755597210481382, 'lambda': 0.482921560366132, 'alpha': 9.687313562807313, 'scale_pos_weight': 2.1815160741957897, 'n_estimators': 489}. Best is trial 59 with value: 0.8035157250273529.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:02:41] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:02:48,279] Trial 61 finished with value: 0.8026554655430624 and parameters: {'max_depth': 14, 'eta': 0.02445508009364259, 'subsample': 0.7570997535911738, 'colsample_bytree': 0.9078624254769083, 'min_child_weight': 2, 'gamma': 4.75477684624711, 'lambda': 0.47549596533698113, 'alpha': 9.7121454736503, 'scale_pos_weight': 2.1366045191618337, 'n_estimators': 481}. Best is trial 59 with value: 0.8035157250273529.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:02:49] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:02:54,399] Trial 62 finished with value: 0.802180378052471 and parameters: {'max_depth': 14, 'eta': 0.0354161164810651, 'subsample': 0.779500651201674, 'colsample_bytree': 0.9893817902378526, 'min_child_weight': 2, 'gamma': 4.75548855497358, 'lambda': 0.7059810898739967, 'alpha': 9.65942696524107, 'scale_pos_weight': 1.5546191707304875, 'n_estimators': 497}. Best is trial 59 with value: 0.8035157250273529.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:02:55] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:03:00,114] Trial 63 finished with value: 0.8005940048575708 and parameters: {'max_depth': 15, 'eta': 0.02721446282894698, 'subsample': 0.6958677695300678, 'colsample_bytree': 0.8968925556012359, 'min_child_weight': 1, 'gamma': 4.453918899247085, 'lambda': 0.09131648109825885, 'alpha': 9.986558248525109, 'scale_pos_weight': 1.2381612908790678, 'n_estimators': 455}. Best is trial 59 with value: 0.8035157250273529.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:03:00] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:03:07,732] Trial 64 finished with value: 0.800402728309705 and parameters: {'max_depth': 14, 'eta': 0.023682826121349553, 'subsample': 0.799466347213416, 'colsample_bytree': 0.8714852228424679, 'min_child_weight': 4, 'gamma': 4.638216060191378, 'lambda': 1.617527031103989, 'alpha': 9.113970630270376, 'scale_pos_weight': 2.5117415328760018, 'n_estimators': 480}. Best is trial 59 with value: 0.8035157250273529.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:03:08] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:03:12,713] Trial 65 finished with value: 0.7993370308292789 and parameters: {'max_depth': 15, 'eta': 0.046160990286660035, 'subsample': 0.7548116037042453, 'colsample_bytree': 0.9403297977413633, 'min_child_weight': 4, 'gamma': 4.296257556327814, 'lambda': 0.9400420786213146, 'alpha': 9.508152111025542, 'scale_pos_weight': 1.6643354705361733, 'n_estimators': 500}. Best is trial 59 with value: 0.8035157250273529.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:03:13] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:03:20,882] Trial 66 finished with value: 0.799263022712635 and parameters: {'max_depth': 13, 'eta': 0.021952354311535766, 'subsample': 0.7231291382051028, 'colsample_bytree': 0.9135498908680699, 'min_child_weight': 3, 'gamma': 4.8823158754439, 'lambda': 0.32952751376449657, 'alpha': 8.301218435380068, 'scale_pos_weight': 2.7321671567181847, 'n_estimators': 446}. Best is trial 59 with value: 0.8035157250273529.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:03:21] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:03:27,378] Trial 67 finished with value: 0.7949599793592041 and parameters: {'max_depth': 14, 'eta': 0.02938135557687542, 'subsample': 0.7036423167368232, 'colsample_bytree': 0.9709767579508531, 'min_child_weight': 2, 'gamma': 4.351186079492285, 'lambda': 0.887683064495948, 'alpha': 1.2138034953586327, 'scale_pos_weight': 1.3065678060515533, 'n_estimators': 422}. Best is trial 59 with value: 0.8035157250273529.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:03:28] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:03:37,266] Trial 68 finished with value: 0.8003480224410456 and parameters: {'max_depth': 15, 'eta': 0.01794897731943433, 'subsample': 0.7732072878750872, 'colsample_bytree': 0.8462013674437449, 'min_child_weight': 1, 'gamma': 4.099029740994212, 'lambda': 2.0796997315189953, 'alpha': 8.729467182642917, 'scale_pos_weight': 2.2314900397930684, 'n_estimators': 476}. Best is trial 59 with value: 0.8035157250273529.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:03:38] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:03:41,509] Trial 69 finished with value: 0.8018402123053284 and parameters: {'max_depth': 13, 'eta': 0.03706686618323407, 'subsample': 0.6537251060396405, 'colsample_bytree': 0.923403120156582, 'min_child_weight': 4, 'gamma': 4.853187391203789, 'lambda': 0.27473705277605426, 'alpha': 9.039785642445787, 'scale_pos_weight': 1.019553106476991, 'n_estimators': 452}. Best is trial 59 with value: 0.8035157250273529.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:03:42] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:03:50,235] Trial 70 finished with value: 0.8024130719867154 and parameters: {'max_depth': 15, 'eta': 0.01522107960107519, 'subsample': 0.7453253978390922, 'colsample_bytree': 0.8910514247858902, 'min_child_weight': 3, 'gamma': 4.612140388257759, 'lambda': 1.3816444323351607, 'alpha': 9.77754807946318, 'scale_pos_weight': 1.936426463512308, 'n_estimators': 488}. Best is trial 59 with value: 0.8035157250273529.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:03:50] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:03:57,762] Trial 71 finished with value: 0.8006777125963174 and parameters: {'max_depth': 14, 'eta': 0.02475518823058687, 'subsample': 0.7593103811122328, 'colsample_bytree': 0.9126395437573146, 'min_child_weight': 2, 'gamma': 4.7666043718473015, 'lambda': 0.46585479264362195, 'alpha': 9.593496004052248, 'scale_pos_weight': 2.182233341704584, 'n_estimators': 464}. Best is trial 59 with value: 0.8035157250273529.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:03:58] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:04:04,714] Trial 72 finished with value: 0.8069376547089725 and parameters: {'max_depth': 14, 'eta': 0.022362406595541125, 'subsample': 0.724852641974008, 'colsample_bytree': 0.9488629724005575, 'min_child_weight': 2, 'gamma': 4.901190049422654, 'lambda': 0.8979542923921714, 'alpha': 9.29610848983614, 'scale_pos_weight': 1.6915078657214022, 'n_estimators': 481}. Best is trial 72 with value: 0.8069376547089725.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:04:05] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:04:12,577] Trial 73 finished with value: 0.8031425805650612 and parameters: {'max_depth': 14, 'eta': 0.018317871478066447, 'subsample': 0.7186412072942986, 'colsample_bytree': 0.9641907583392957, 'min_child_weight': 1, 'gamma': 4.6102881265336615, 'lambda': 0.8707109244362714, 'alpha': 9.26295797918955, 'scale_pos_weight': 1.7403380333849494, 'n_estimators': 463}. Best is trial 72 with value: 0.8069376547089725.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:04:13] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:04:18,796] Trial 74 finished with value: 0.8024053122890332 and parameters: {'max_depth': 15, 'eta': 0.022133427411657726, 'subsample': 0.7318287592047065, 'colsample_bytree': 0.9466386279127256, 'min_child_weight': 1, 'gamma': 4.94443358718026, 'lambda': 1.010990330091414, 'alpha': 9.22548102087121, 'scale_pos_weight': 1.4333290972868933, 'n_estimators': 467}. Best is trial 72 with value: 0.8069376547089725.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:04:19] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:04:27,675] Trial 75 finished with value: 0.80011426154837 and parameters: {'max_depth': 13, 'eta': 0.01240200144259609, 'subsample': 0.7215985410300988, 'colsample_bytree': 0.9766963204276564, 'min_child_weight': 1, 'gamma': 4.416694731328429, 'lambda': 1.7957390464183305, 'alpha': 8.69349659162232, 'scale_pos_weight': 1.6709622177120131, 'n_estimators': 487}. Best is trial 72 with value: 0.8069376547089725.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:04:28] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:04:32,630] Trial 76 finished with value: 0.8019477811144478 and parameters: {'max_depth': 14, 'eta': 0.02917043499950969, 'subsample': 0.7926626788501806, 'colsample_bytree': 0.9600624363790474, 'min_child_weight': 3, 'gamma': 4.6098002981494615, 'lambda': 1.5137163428335267, 'alpha': 8.968934218015004, 'scale_pos_weight': 1.2026533048809533, 'n_estimators': 445}. Best is trial 72 with value: 0.8069376547089725.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:04:33] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:04:38,996] Trial 77 finished with value: 0.8034609221624726 and parameters: {'max_depth': 15, 'eta': 0.017433317146300017, 'subsample': 0.7695463546778312, 'colsample_bytree': 0.9919078505550939, 'min_child_weight': 2, 'gamma': 4.990888047327173, 'lambda': 2.2841226784121473, 'alpha': 9.362408211684315, 'scale_pos_weight': 1.4400822626372254, 'n_estimators': 426}. Best is trial 72 with value: 0.8069376547089725.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:04:39] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:04:48,958] Trial 78 finished with value: 0.8026470268718331 and parameters: {'max_depth': 13, 'eta': 0.014763857134494388, 'subsample': 0.7720088266501292, 'colsample_bytree': 0.9975196101918686, 'min_child_weight': 2, 'gamma': 4.660460956847268, 'lambda': 2.5572651081616224, 'alpha': 8.101140413200408, 'scale_pos_weight': 2.332712208515681, 'n_estimators': 460}. Best is trial 72 with value: 0.8069376547089725.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:04:49] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:04:55,346] Trial 79 finished with value: 0.8032490824157491 and parameters: {'max_depth': 14, 'eta': 0.03161653499125957, 'subsample': 0.7987052316459242, 'colsample_bytree': 0.985854730337305, 'min_child_weight': 1, 'gamma': 4.826572059920739, 'lambda': 2.098312192855216, 'alpha': 9.355426626698018, 'scale_pos_weight': 1.7187025160133969, 'n_estimators': 500}. Best is trial 72 with value: 0.8069376547089725.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:04:56] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:04:58,640] Trial 80 finished with value: 0.7910893451591126 and parameters: {'max_depth': 13, 'eta': 0.053838952723488466, 'subsample': 0.8477410376881173, 'colsample_bytree': 0.9840515606582815, 'min_child_weight': 1, 'gamma': 4.868492474930703, 'lambda': 0.7742170176821793, 'alpha': 8.466262337018716, 'scale_pos_weight': 3.128945974813151, 'n_estimators': 76}. Best is trial 72 with value: 0.8069376547089725.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:04:59] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:05:05,898] Trial 81 finished with value: 0.7997218148380938 and parameters: {'max_depth': 14, 'eta': 0.022025503885686613, 'subsample': 0.8196454844835466, 'colsample_bytree': 0.9330274747569709, 'min_child_weight': 2, 'gamma': 4.498767370925026, 'lambda': 1.9934914453188848, 'alpha': 9.264899787590112, 'scale_pos_weight': 1.7734883736794527, 'n_estimators': 475}. Best is trial 72 with value: 0.8069376547089725.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:05:06] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:05:11,140] Trial 82 finished with value: 0.8020837698163279 and parameters: {'max_depth': 15, 'eta': 0.033440495539673314, 'subsample': 0.8065088829098598, 'colsample_bytree': 0.9572243694828384, 'min_child_weight': 4, 'gamma': 4.997197960910565, 'lambda': 1.3102093064313496, 'alpha': 9.431516355675598, 'scale_pos_weight': 1.4453770617038537, 'n_estimators': 499}. Best is trial 72 with value: 0.8069376547089725.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:05:11] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:05:17,531] Trial 83 finished with value: 0.8012444615157795 and parameters: {'max_depth': 14, 'eta': 0.017504593083592337, 'subsample': 0.7084667368970845, 'colsample_bytree': 0.9690009421417843, 'min_child_weight': 3, 'gamma': 4.804563538036537, 'lambda': 0.32124808335978977, 'alpha': 8.768023592665601, 'scale_pos_weight': 1.2771909342937597, 'n_estimators': 487}. Best is trial 72 with value: 0.8069376547089725.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:05:18] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:05:23,979] Trial 84 finished with value: 0.7988741648625369 and parameters: {'max_depth': 15, 'eta': 0.041792067645028956, 'subsample': 0.7350210442644115, 'colsample_bytree': 0.9415014185800312, 'min_child_weight': 2, 'gamma': 4.66092631722242, 'lambda': 2.3374341144014377, 'alpha': 9.517613030815687, 'scale_pos_weight': 2.465518771515674, 'n_estimators': 439}. Best is trial 72 with value: 0.8069376547089725.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:05:24] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:05:31,296] Trial 85 finished with value: 0.7986486486486487 and parameters: {'max_depth': 14, 'eta': 0.027966565657371774, 'subsample': 0.7661229627643378, 'colsample_bytree': 0.8760191353674188, 'min_child_weight': 1, 'gamma': 4.227045628198239, 'lambda': 0.08911249408687849, 'alpha': 8.993373553253825, 'scale_pos_weight': 2.033412904375819, 'n_estimators': 471}. Best is trial 72 with value: 0.8069376547089725.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:05:32] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:05:37,625] Trial 86 finished with value: 0.8035122331633959 and parameters: {'max_depth': 14, 'eta': 0.025625317703296662, 'subsample': 0.7867872653441433, 'colsample_bytree': 0.9864028647696211, 'min_child_weight': 3, 'gamma': 4.559408052522404, 'lambda': 1.6396004443873675, 'alpha': 8.567456100058077, 'scale_pos_weight': 1.6340099725266186, 'n_estimators': 456}. Best is trial 72 with value: 0.8069376547089725.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:05:38] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:05:44,048] Trial 87 finished with value: 0.8041775302434219 and parameters: {'max_depth': 13, 'eta': 0.02627308238493945, 'subsample': 0.7880155701520584, 'colsample_bytree': 0.999290916250498, 'min_child_weight': 3, 'gamma': 4.554283078217938, 'lambda': 1.7000030447154435, 'alpha': 8.568627859904346, 'scale_pos_weight': 1.6994028994360288, 'n_estimators': 456}. Best is trial 72 with value: 0.8069376547089725.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:05:44] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:05:52,635] Trial 88 finished with value: 0.7977331983145937 and parameters: {'max_depth': 13, 'eta': 0.02596764496047174, 'subsample': 0.8358390891635021, 'colsample_bytree': 0.9974370984486319, 'min_child_weight': 3, 'gamma': 4.381464385614315, 'lambda': 2.3443246553256483, 'alpha': 7.783784588233698, 'scale_pos_weight': 2.8708187424474, 'n_estimators': 455}. Best is trial 72 with value: 0.8069376547089725.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:05:53] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:05:58,544] Trial 89 finished with value: 0.7994900908660599 and parameters: {'max_depth': 12, 'eta': 0.031999643784441506, 'subsample': 0.7887769321268111, 'colsample_bytree': 0.9833173946115399, 'min_child_weight': 1, 'gamma': 4.584148457862398, 'lambda': 1.8049921728724159, 'alpha': 8.480415263596388, 'scale_pos_weight': 1.6925939823544218, 'n_estimators': 428}. Best is trial 72 with value: 0.8069376547089725.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:05:59] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:06:05,607] Trial 90 finished with value: 0.7998223029230782 and parameters: {'max_depth': 14, 'eta': 0.0359915156059885, 'subsample': 0.8171891877658317, 'colsample_bytree': 0.9666536364767473, 'min_child_weight': 2, 'gamma': 4.495889569623003, 'lambda': 1.4595600027119824, 'alpha': 8.179528379247264, 'scale_pos_weight': 2.5921783803711316, 'n_estimators': 409}. Best is trial 72 with value: 0.8069376547089725.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:06:06] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:06:12,471] Trial 91 finished with value: 0.8019853186519854 and parameters: {'max_depth': 13, 'eta': 0.03045511495379561, 'subsample': 0.7778544048845446, 'colsample_bytree': 0.9853430234340801, 'min_child_weight': 3, 'gamma': 4.726249871433576, 'lambda': 2.6734498732372893, 'alpha': 9.991968952889831, 'scale_pos_weight': 1.9219174543723676, 'n_estimators': 488}. Best is trial 72 with value: 0.8069376547089725.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:06:13] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:06:18,276] Trial 92 finished with value: 0.8017302185906837 and parameters: {'max_depth': 14, 'eta': 0.022286061362983747, 'subsample': 0.7968710021825688, 'colsample_bytree': 0.9361141134383801, 'min_child_weight': 2, 'gamma': 4.829519192374132, 'lambda': 1.6333070248210353, 'alpha': 9.14683302417709, 'scale_pos_weight': 1.2767595628184925, 'n_estimators': 464}. Best is trial 72 with value: 0.8069376547089725.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:06:19] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:06:24,525] Trial 93 finished with value: 0.8033212476041933 and parameters: {'max_depth': 14, 'eta': 0.0257907208876628, 'subsample': 0.8070033333142251, 'colsample_bytree': 0.994976528117118, 'min_child_weight': 3, 'gamma': 4.889351304128176, 'lambda': 0.9651911952177488, 'alpha': 8.89363070500423, 'scale_pos_weight': 1.6092695796867602, 'n_estimators': 444}. Best is trial 72 with value: 0.8069376547089725.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:06:25] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:06:38,447] Trial 94 finished with value: 0.795618486703758 and parameters: {'max_depth': 14, 'eta': 0.02516843549422776, 'subsample': 0.8102322290618968, 'colsample_bytree': 0.9755370416777618, 'min_child_weight': 3, 'gamma': 0.16092381823121382, 'lambda': 0.6813321862454835, 'alpha': 8.587896222024536, 'scale_pos_weight': 2.116804577721191, 'n_estimators': 448}. Best is trial 72 with value: 0.8069376547089725.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:06:39] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:06:45,598] Trial 95 finished with value: 0.8025056063815753 and parameters: {'max_depth': 13, 'eta': 0.016017124025682606, 'subsample': 0.830671112658329, 'colsample_bytree': 0.9955041666763486, 'min_child_weight': 1, 'gamma': 4.911718415185534, 'lambda': 2.038816591250394, 'alpha': 8.810608326916284, 'scale_pos_weight': 1.7630438281491643, 'n_estimators': 440}. Best is trial 72 with value: 0.8069376547089725.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:06:46] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:06:50,076] Trial 96 finished with value: 0.7987562174577678 and parameters: {'max_depth': 14, 'eta': 0.04072823496769696, 'subsample': 0.8795699294530789, 'colsample_bytree': 0.9849942610982417, 'min_child_weight': 2, 'gamma': 4.5822708703892445, 'lambda': 1.2669426386547733, 'alpha': 8.998066610806267, 'scale_pos_weight': 1.6255715205509345, 'n_estimators': 475}. Best is trial 72 with value: 0.8069376547089725.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:06:50] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:06:56,864] Trial 97 finished with value: 0.8031365667993575 and parameters: {'max_depth': 13, 'eta': 0.027328075238262146, 'subsample': 0.7406857825169104, 'colsample_bytree': 0.963125736751346, 'min_child_weight': 4, 'gamma': 4.990830260355908, 'lambda': 0.46172345209719773, 'alpha': 9.436916712161043, 'scale_pos_weight': 2.299735212937283, 'n_estimators': 500}. Best is trial 72 with value: 0.8069376547089725.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:06:57] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:07:01,987] Trial 98 finished with value: 0.8048314787655874 and parameters: {'max_depth': 12, 'eta': 0.02799292855869316, 'subsample': 0.7846727331315995, 'colsample_bytree': 0.9999482029285681, 'min_child_weight': 4, 'gamma': 4.9860291380704265, 'lambda': 1.1883407728226296, 'alpha': 8.236425996783359, 'scale_pos_weight': 1.5073816303119996, 'n_estimators': 461}. Best is trial 72 with value: 0.8069376547089725.\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:07:02] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "[I 2025-01-13 14:07:08,974] Trial 99 finished with value: 0.7971105795718199 and parameters: {'max_depth': 12, 'eta': 0.027426320602196966, 'subsample': 0.8007122660176049, 'colsample_bytree': 0.9641081789562107, 'min_child_weight': 4, 'gamma': 3.9203758447458785, 'lambda': 1.1383008955481184, 'alpha': 7.506613399976575, 'scale_pos_weight': 2.3635769957729345, 'n_estimators': 420}. Best is trial 72 with value: 0.8069376547089725.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Hyperparameters: {'max_depth': 14, 'eta': 0.022362406595541125, 'subsample': 0.724852641974008, 'colsample_bytree': 0.9488629724005575, 'min_child_weight': 2, 'gamma': 4.901190049422654, 'lambda': 0.8979542923921714, 'alpha': 9.29610848983614, 'scale_pos_weight': 1.6915078657214022, 'n_estimators': 481}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:07:09] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\common\\error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n",
      "\n",
      "    E.g. tree_method = \"hist\", device = \"cuda\"\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:07:09] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "c:\\Users\\sarth\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [14:07:11] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\common\\error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n",
      "\n",
      "    E.g. tree_method = \"hist\", device = \"cuda\"\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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REZHZrVtXeiFbvz5w7VrlvSoqFY1Gg61btyImJgYAUK9ePYwYMcIqhxY8jsUsERERlUl+PnDunHYeVUMUFMjw11/uqF5dBqUS2L8f+PDDosffeguoU0e7bWsLhIaykDUF+WN/qT169ED37t312qwVi1kiIiJ6Jo0G6NMHOHiwPM9WAuhe4iPPPw/897/a8apkGgUFBVAqtSXfwIED0bp1a9Qp/O2hEmAxS0REVEX9/Tfw9dfauVWfJTW1vIVs6f7v/4A5c1jImkp+fj6ioqKQmZmJF198ETKZDDY2NpWqkAVYzBIREVU6SUnaIQHP8tJLwLFjhr/+pEnPvjnrcRqNGrGxsfD394dcroBCAQwZAvToYfi5qWzu3buHiIgIJCcnQyaTIT4+HrVr15Y6lkmwmCUiIqpExo4F/vc/073+Sy8Bv/xi2HNUKg2ioi5i0KC6sLGxkJUCKikhBE6fPo0dO3agoKAALi4uCAsLq7SFLMBiloiIyGrk5wM//QTExZX8+MOH5StklUogNvbZx9naaudfJcuUl5eHrVu34sKFCwCAhg0bYvjw4XC0ttUcDMRiloiIyAoIAUyeDCxfXvbnhIc/+xgHB2DKFKBWrfJnI8sQERGBv//+GzKZDH369EHnzp0hqwIDklnMEhERWbAFC4D33itaJass5HJg+3YgONh0ucjy9O7dGw8ePMCwYcMq9bCCJ7GYJSIikpBaDURGAvHxJT/+zjvF2+bP1y7hWpq6dQE/P6PEIwuWm5uLuLg4NGrUCADg6+uL119/vVLMHWsIFrNEREQS+v57YMaMsh3btat2CdjJk02biSzf3bt3ERERgbS0NEycOBF+j357qWqFLMBiloiIyOzmzwe++QbIywPu3Cnbc55/Hli92rS5yPIJIXDs2DFER0dDo9GgWrVqUkeSHItZIiIiE9FogAMHtPO+FlKpgNdfL/n4338vud3FRbv6FlVtOTk52Lx5M/766y8AQNOmTTF06FDY29tLnExaLGaJiIhM5PPPgdmzn35MzZqAh4f2am2XLubJRdYnPj5eN6xAoVAgODgY7dq1qxKzFTwLi1kiIqoSdu0CvvoKyMoy3zmPHn364z/+WPpVWqLH3bp1C2lpaXB3d8fIkSPh6+srdSSLwWKWiIgqPbUa6N9f2gz/+Y92yqxCdesCoaHS5SHr0rlzZwBAUFAQ7OzsJE5jWVjMEhFRpdezp3TntrUFZs0C3n1XugxkfW7fvo39+/dj1KhRsLW1hUwmQxeOQykRi1kiIqpUFi8Gdu4s2k9NBQ4eLNrv3x/YscPssYjKRAiBgwcPYu/evRBC4MCBA+jDu/+eisUsERFZrfv3tdNbFbp8+dlzsC5aZNpMROWVlZWFDRs24Pr16wCAli1bolu3bhKnsnwsZomIyCpNm6Zd6rWs7O2B334D6tQxXSai8rp58yYiIyORmZkJpVKJQYMGoXXr1pytoAxYzBIRkdXJyHh2Ifvhh8BrrxXtu7lp52slsjTnzp3Dxo0bIYSAl5cXwsPD4e3tLXUsq8FiloiIrM633+rvh4fr7zdpArz/PuDsbL5MROXl7+8PBwcHBAQEYODAgbC1tZU6klVhMUtERFZj5UoZpk8H0tKK2iZMAH79VbJIROWSkpICDw8PAICLiwumTp0KF350UC7yZx9CREQkHbUa2LpVhk2bGmDCBKVeIQtoF0IgshYajQZ79+7FTz/9hEuXLunaWciWH6/MEhGRRVu9Ghg7VgmghV57ly7Aiy8Cnp7S5CIyVHp6OtavX49bt24B0C5R26xZM4lTWT8Ws0REZLF+/BF4443i7SEhwObN5s9DVF5///03NmzYgOzsbNja2iIkJAQtWrR49hPpmVjMEhGRRUlPB3bv1s4f+2Qh+/77anTvrgDnkCdroVarsXfvXhw6dAgA4OPjg/DwcN14Wao4FrNERGQxNBqgXTvg6tXijw0YEItPP60FW1uF+YMRldOtW7d0hWy7du0QHBwMpZLllzHxb5OIiCzG/fslF7KvvaZG//7nIJPVMn8oogqoX78+unbtCl9fX46PNREWs0REZBEePCi+1Ow33wBeXkBIiAb79kmTi8gQarUa+/btQ1BQEFxdXQEAfTguxqRYzBIRkeSuXAFatwZyc4vaevcGZszQbqtUksQiMkhqaioiIiJw584d3L59G+PHj+dytGbAYpaIiMwuNhb417+0V2MBICKi+DGvvmreTEQVcfnyZWzevBm5ubmwt7dHx44dWciaCYtZIiIyKSGAO3f024YPB86eLfn4unWBf/4TGDHC9NmIKqqgoADR0dE4fvw4AKBWrVoICwtDtWrVpA1WhbCYJSIik8nPB4KCgPPny3a8vz8QEwO4uZk2F5ExpKenY82aNUhISAAAdO7cGb1794ZCwRk3zInFLBERVVhyMvDLL9rZCB538ODTC9lRo7Q3eRXy9QVYB5C1sLe3R0FBARwcHDBs2DAEBARIHalKYjFLREQV9uGH2mL2WcLDi7Zr1QJmzwb4aSxZk4KCAigUCshkMtja2mL06NGwsbHRzVxA5sdiloiIyuT0aSAsDLh50/DnKpXaVb26dzd6LCKzSU5ORkREBFq0aIGuXbsCAFfysgAsZomICEePAocPP/2Yd94p22vt368tXh/n7w/4+JQvG5ElOHfuHLZu3QqVSoWsrCy0b98etra2UscisJglIqryLl8GOnUy7DkODkCrVvpttrbAa68B3boZLxuR1FQqFbZv346YmBgAQL169TBixAgWshaExSwRURW3dathx3t6AnFxgL29afIQWYr79+8jIiICSUlJAIAePXqge/fukMvlEiejx7GYJSKqYoTQDim4e1e7/8UXRY+1agV89FHpz5XLgZ49WchS5ZeXl4dff/0Vubm5cHZ2xogRI+Dv7y91LCoBi1kioirmv/8tffzrZ58BISHmzUNkiezs7NCrVy9cuXIFw4cPh7Ozs9SRqBQsZomIKrGbN4H33tMOCyh09GjJx9raAh07miUWkUW6d+8ehBDweXS3Yrt27dCuXTsuS2vhWMwSEVVi8+YB69aV/viHHwIeHoBMBvTtC3h5mS0akcUQQuD06dPYsWMHXFxcMGXKFNjZ2bGItRIsZomIKrHvviu5XaEAXn1Vf7wsUVWUl5eHrVu34sKFCwC088aq1WqJU5EhWMwSEVUy8+cDe/cWb09PB1xczJ+HyFIlJiZi3bp1ePDgAWQyGXr37o0uXbrwiqyVYTFLRGSlsrOBBw/022JigNdfL/l4FrJEWkIInDx5Ejt37oRarYarqyvCw8NRu3ZtqaNRObCYJSKyQgcOAEOGaK+2lsVbb5k2D5G1uXr1KtRqNQICAhAaGgpHR0epI1E5sZglIrIi6enA4sVlW1r2s8+Al1/Wzgnr6WnyaERWQyaTYdiwYbh06RKCgoI4rMDKsZglIrIiX3+tLVIf16AB0KaNflvz5tqC18HBfNmILJUQAseOHUNycjKGDBkCAHByckK7du0kTkbGwGKWiMgKqNXaxQy2b9dv9/cHLl8GbGykyUVk6XJycrB582b89ddfAIDmzZtzJa9KhsUsEZEFO3sW2L0bOHaseCEbFQX07s1Clqg08fHxiIiIQFpaGhQKBYKDg1GvXj2pY5GRsZglIrJQCQlAUBBQUFD8sZ9/BgYONH8mImsghMCRI0ewe/duaDQauLu7Izw8HH5+flJHIxNgMUtEZIHi44HSZgnaskU7kwERlWzz5s04c+YMAO2wgiFDhsDe3l7aUGQyLGaJiCzQ0qX6+23aAB98AAQEAK1aSZOJyFo0b94cFy5cQP/+/REYGMjZCio5FrNERBbo8RkL5HLt1diaNaXLQ2TJhBBISUmB56M56Bo2bIi33noLzs7OEicjc5BLHYCIiLTi44ExY4BOnQCVqqj9zh0WskSlycrKwsqVK7F48WI8fPhQ185CturglVkiIiPKyAAuXCjfcz/8EPjzz+LtPj4VikRUad28eRORkZHIzMyEUqlEUlIS3N3dpY5FZsZilojISJKSgEaNyr7E7LPY2wNz5hjntYgqE41GgwMHDmDfvn0QQsDT0xMjR46Et7e31NFIAixmiYgMoFJpV+E6fbr4YxERxjmHnZ12aIGHh3Fej6gyyczMxPr16xEbGwsAaN26NQYOHAhbW1uJk5FUWMwSUZUmBHD3rvbPsti4UTsc4FkUCuDNNw3PI5drV/piIUtUsqNHjyI2NhY2NjYYPHgwWnF6jyqPxSwRVVlqNdCli3Z1LWOqUQM4fBioX9+4r0tEQM+ePZGRkYFu3brpZi+gqo3FLBFVKfn5wG+/AZcva5eKrUghu3w50KtX8XZvb4CfeBIZR3p6Oo4ePYq+fftCLpdDqVRi+PDhUsciC8JiloiqlLVrgVdfLfmx8PCyv07HjsCLL2qHBRCRafz999/YsGEDsrOzYWdnhx49ekgdiSwQi1kiqjKOHZNh3Lji7XI5sHUrMHCg+TMRUXFqtRp79+7FoUOHAAA+Pj5o0aKFxKnIUrGYJaJK78QJGTZtaoClS/X/y/vxR6BtW6BOHS5KQGQp0tLSEBkZibi4OABAUFAQ+vfvD6WSJQuVjP8yiKhSu34d6NJFCUD/qs7zzwPTpgFcsp3Icty4cQMRERHIycmBnZ0dQkJC0Lx5c6ljkYVjMUtElc6mTcAHH2gXL7hzp/jjfn7am7dYyBJZFmdnZ6hUKvj6+iI8PBzVq1eXOhJZARazRGT10tKAPXuAggLt/qhRJR/Xo4cGb74pR69egI2N+fIRUeny8/N1Cx54e3tj3Lhx8PX15bACKjP+SyEiq6ZWA23aAI8WAypGOxZWoEaNJKxeXR2+vpx+gMhS/PXXX9iyZQuef/551K5dGwB0fxKVFf9XJyKrtWgR0LBh6YVs795AfDwQG1uAjz8+Cs6vTmQZCgoKsH37dqxduxbZ2dk4evSo1JHIiklezM6fPx/+/v6wt7dHYGAgDhw48NTjV65ciVatWsHR0RG+vr6YMGECUlJSzJSWiKSk0QCnTwNHjgDbtwNTpgA3b+of88032q+ff9bOKUtEluXBgwf49ddfcfz4cQBAp06dMGLECIlTkTWTdJjB2rVrMX36dMyfPx9dunTBwoULMXDgQFy6dAl16tQpdvzBgwcxbtw4/Pe//0VISAju3LmDqVOnYtKkSdiwYYME3wERmVNICBAVVfJjfn7A6tVA9+7mzUREZXfp0iVERUUhPz8fDg4OGDZsGAICAqSORVZO0mL222+/xcSJEzFp0iQAwLx587Bz504sWLAAc+fOLXb80aNHUa9ePbz55psAAH9/f0yZMgVfffWVWXMTkekdOgQsXAjk5Gj31erSC9kPPwS++MJ82YjIcBkZGdi4cSMA7bjYsLAwuLm5SRuKKgXJitn8/HycOnUKs2bN0msPDg7G4cOHS3xO586d8dFHHyEqKgoDBw5EUlISIiIiMHjw4FLPk5eXh7y8PN1+eno6AEClUkGlUhnhO3m2wvOY63xkXOw/08vPB5KS9Nt69VJCpSp97qy33lID0N7gNWGCBk/rHvah9WMfWjeVSgVnZ2cEBATA09MT3bt3h1wuZ39aEXO/Bw05j0wIIUyYpVR3795FzZo1cejQIXTu3FnX/uWXX+K3337DlStXSnxeREQEJkyYgNzcXBQUFGDo0KGIiIiATSnz7HzyySeYM2dOsfZVq1bB0dHRON8MEZXbvXsOmDWrOx4+tC/T8UqlGlOnnkPfvrdNnIyIKio1NRUuLi5QKBQAACEEZJzgmcogOzsbY8aMQVpaGlxdXZ96rORTcz35j/pp/9AvXbqEN998Ex9//DH69++PhIQEzJw5E1OnTsWSJUtKfM4HH3yAGTNm6PbT09NRu3ZtBAcHP/Mvx1hUKhWio6PRr1+/UotuslzsP9NJTQXat1fi4cPSf7i1aiWwYUOBbt/FBXBza4EnV/R6Gvah9WMfWpfC/rp58yaaNm2K/v37448//kBwcDD7z0qZ+z1Y+El6WUhWzHp6ekKhUCAxMVGvPSkpCTVq1CjxOXPnzkWXLl0wc+ZMAEDLli3h5OSEbt264fPPP4evr2+x59jZ2cHOzq5Yu42NjdnfUFKck4yH/Wd833xTfDaC8PCi7erVgRkzZPD3N87fO/vQ+rEPLd/9+/cRERGBpEdjh7y8vHR9xv6zfubqQ0POIVkxa2tri8DAQERHR2P48OG69ujoaISGhpb4nOzs7GIrgjz+0QURWY///Q/497/12xISAB8fafIQUcWdOXMGUVFRUKlUcHJywogRI1C/fn2OjSWTknSYwYwZMzB27FgEBQWhU6dOWLRoEW7fvo2pU6cC0A4RuHPnDpYvXw4ACAkJweTJk7FgwQLdMIPp06ejffv28PPzk/JbIaKnOHgQeDSlpM477+jvx8ezkCWyVvn5+YiKisLZs2cBaGcbGjFiBJydnSVORlWBpMXs6NGjkZKSgk8//RQJCQlo0aIFoqKiULduXQBAQkICbt8uusnj5ZdfRkZGBn788Ue88847qFatGnr37o1/P3l5h4gsxunTQLduTz9m8eLCZWeJyBqpVCpcv34dMpkMPXv2RNeuXSGXS74uE1URkt8ANm3aNEybNq3Ex5YtW1as7Y033sAbb7xh4lREVBHffaf9ys8H7tx5+rH9+wMTJ5onFxGZhpOTE8LDwyGEQL169aSOQ1WM5MUsEVUuDx4A06eX/NiwYcCYMUX7jo5A377mSEVExpSXl4dt27ahYcOGaNmyJQDoPlUlMjcWs0RkNEIAgYH6bYXDB4KCgBUrAA6hI7JuiYmJWLduHR48eIBr166hcePGJc4aRGQuLGaJqEI0GuCDD4D9+7VXZR+famv6dOC//5UqGREZkxACJ0+exM6dO6FWq+Hq6oqwsDAWsiQ5FrNEVCEHDwJffVXyY198Yd4sRGQaubm52LJlCy5dugQACAgIQGhoKFfSJIvAYpaIKmTy5OJtNWpohxTw5xyR9cvPz8eiRYvw8OFDyOVy9O3bFx07duSytGQxWMwSUakiI4F16wC1uvRjrl4t2l6/HnhsDRQiqgRsbW3RtGlTXLx4EeHh4ahVq5bUkYj0sJglomJSUrRTaj2+tGxZhISYJg8RmVdOTg5UKhVcXV0BAL1790bXrl3h4OAgcTKi4jijMRHp+fBDwMsLaNWq7M+RyYBZswAlfz0msnrx8fFYuHAhfv/9d6gffSyjUChYyJLF4o8eItIzd27xtrFjgS+/LP05Dg6Ah4fpMhGR6QkhcOTIEezevRsajQZyuRwZGRmoVq2a1NGInorFLBHpnDypvx8eDtSpA7z/PuDtLU0mIjK97OxsbNq0CVcfDYJv1qwZQkJCYG9vL3EyomdjMUtEOn/9VbTt4aG9+YuIKrfbt28jMjIS6enpUCgUGDBgAAIDAzlbAVkNFrNEpHPkSNF2ScMNiKhyEUJg586dSE9PR/Xq1TFy5Ej4+PhIHYvIICxmiQgAEBcHzJ9ftM9FfYgqP5lMhhEjRuDQoUPo378/V/Miq8TZDIgIQgCdO+u3dekiTRYiMq2bN2/i6NGjun0PDw8MHTqUhSxZLV6ZJSKcOgXExxftv/EG0KCBdHmIyPg0Gg0OHDiAffv2QQgBX19f1K1bV+pYRBXGYpaIsH69/v6HH0qTg4hMIzMzE+vXr0dsbCwAoFWrVvD19ZU4FZFxsJglIpw6VbQ9YwbA+z+IKo8bN25g/fr1yMrKgo2NDQYNGoTWrVtLHYvIaFjMElVxly8Du3YV7Q8fLl0WIjKuAwcOYM+ePQAAb29vhIeHw8vLS+JURMbFG8CIqrCPPgKaNdNv69RJmixEZHxOTk4AgDZt2mDSpEksZKlS4pVZoipm9Wpg40ZAowEiIvQfCwgAFApJYhGRkeTn58PW1haAtoj19PREnTp1JE5FZDosZomqkFu3gDFjSn7sgw+Al14ybx4iMh6NRoM9e/bgwoULmDJlChwcHCCTyVjIUqXHYpaoirh0CWjevOTHZswAvvzSvHmIyHjS0tIQGRmJuLg4AMClS5cQGBgocSoi82AxS1TJZGQAS5YAt2/rt//3v/r7U6YA//d/gL094OlpvnxEZFxXr17Fxo0bkZOTAzs7O4SEhKB5ab+5ElVCLGaJKpl584CPP376Mc89p70SW726WSIRkQmo1Wrs3r0bR44cAQD4+voiPDwc1fnGpiqGxSxRJTNv3tMf9/AADhwA3NzMEoeITOTPP//UFbLt27dHv379oFTyxzpVPfxXT1RJnD0L7N4NPHhQ1LZxI+DtrX9cy5bAo9l6iMiKde7cGdeuXUOPHj3QtGlTqeMQSYbFLFElkJAABAUBBQX67aGh0uQhIuMrKCjAxYsX0bJlS8hkMjg4OGDKlCmQyWRSRyOSFItZokpg5szihWyXLtJkISLje/jwIdatW4eEhAQUFBToZipgIUvEYpbI6uXnAytXFu23aQPMmQP07StdJiIynkuXLmHz5s3Iy8uDg4MDXFxcpI5EZFFYzBJZueef19/fsgWoWVOaLERkPAUFBdi5cydOnjwJAKhduzbCwsLgxrs3ifSwmCWyUhoNMGsWsGFDUdu4cSxkiSqDlJQUREREIDExEQDQpUsX9OrVCwquN01UDItZIiu1bx/wn//ot/34ozRZiMi40tPTkZiYCEdHRwwfPhwNGzaUOhKRxWIxS2SllizR3//lF4BD6YislxBCd0OXv78/hg0bBn9/f7i6ukqcjMiyyaUOQESGO3dO/6av778HJk2SLg8RVcz9+/exdOlSpKSk6NpatWrFQpaoDFjMElmhf/xDfz84WJocRFRxZ86cwS+//IK4uDhs375d6jhEVofDDIis0IEDRds9ewIBAZJFIaJyys/PR1RUFM6ePQugaGgBERmGxSyRlXl0c7POnj0A500nsi5JSUlYt24dkpOTIZPJ0KNHD3Tr1g1yOT8wJTJUhYvZvLw82NnZGSMLEZVBbm7RdsOGLGSJrE18fDx+++03FBQUwNnZGWFhYahXr57UsYislsHF7M6dO7F69WocOHAAt2/fhkajgaOjI9q2bYvg4GBMmDABfn5+pshKRADWry/a7thRuhxEVD6+vr7w8fGBnZ0dhg8fDicnJ6kjEVm1MhezGzduxPvvv4+0tDQMGjQIM2fORM2aNeHg4IAHDx7gwoUL+OOPP/DZZ5/h5ZdfxmeffQYvLy9TZieqcoQA3nmnaL96demyEFHZ3b9/H9WrV4dCoYBCocCYMWNgb2+vm4qLiMqvzMXsl19+ia+//hqDBw8ucUzPqFGjAAB37tzBd999h+XLl+Odx3/qElGFHT+uv//669LkIKKyEULg1KlT2LFjBzp06IB+/foBABwcHCRORlR5lLmYPf7kT9FS1KxZE1999VW5AxFRyfLziw8raNRImixE9Gx5eXnYsmULLl68CABITk6GRqPhTV5ERmb02QxOnDiBdu3aGftliaq8P/7Q31+2jDd/EVmqu3fvIiIiAg8fPoRcLkefPn3QqVMnDisgMoFyFbOZmZlQKBR6H5OcOXMG//znPxEVFQW1Wm20gESk9fiKXwAwcqQ0OYiodEIIHD9+HNHR0VCr1XBzc0N4eDhq1aoldTSiSsugzzri4+PRpUsXuLm5wc3NDTNmzEB2djbGjRuHdu3awc7ODgcPHjRVVqIq6aefgK5dgVWritp++AFwdJQuExGVLCMjA3v27IFarUaTJk0wZcoUFrJEJmbQldlZs2YhMzMT3333HSIjI/Hdd99h3759aNWqFa5evQp/f39T5SSqcuLjgVOnii9dC2hX/SIiy+Pq6oqQkBBkZWWhffv2HFZAZAYGFbN79+7F77//ji5duiA8PBx+fn4YOXIkZs2aZap8RFXSli3AsGGARlP8sbAwoHlzs0ciohIIIXD06FH4+PjoLui0aNFC4lREVYtBwwwSExPRoEEDAICPjw8cHBwQGhpqkmBEVdGBA8DYscDQocUL2U8/1c4zGxHBG7+ILEFOTg7WrFmDXbt2Yf369cjJyZE6ElGVZPANYAqFQrctl8thb29v1EBEVY1aDSQkaLd79NAWrI/r3x8YMACYONH82YioZHFxcYiIiEB6ejoUCgW6d+/On4dEEjGomBVCoE+fPlAqtU/LyclBSEgIbG1t9Y47ffq08RISVWLp6UCbNsCNGyU/3qULEBUFcFpKIssghMChQ4ewZ88eCCFQvXp1jBw5Ej4+PlJHI6qyDCpmZ8+erbfPIQZEFfP22yUXskFBwKZNgK8vhxQQWQqVSoXff/8df//9NwDt2NghQ4bAzs5O4mREVVuFilkiKr/Ll4Fff9VvCw8HPDyAd94B/PykyUVEJVMqlbC3t4dSqcSAAQPQtm1bzlZAZAEMHjN77NgxbN68GSqVCn379kVwcLApchFVajt3asfBPu7cOeC556TJQ0Ql02g0KCgogK2tLWQyGYYMGYJu3brB29tb6mhE9IhBxeyGDRswcuRI3W+m33zzDb755htMnz7dRPGIKp+zZ4sXsp9/zkKWyNJkZmZiw4YNsLW1xahRoyCTyWBnZ8dClsjCGHRbyZdffomXX34ZqampSE1NxZw5c/D555+bKhtRpZOQALRrp98WHl7ywghEJJ3Y2FgsXLgQN27cwPXr15GcnCx1JCIqhUHF7JUrV/Dee+/pZjOYOXMmUlNT+SYnKqMLFwCVqmi/eXPg998BNzfpMhFREY1Gg71792L58uXIzMyEl5cXJk+eDC8vL6mjEVEpDBpmkJmZiWrVqun27ezs4ODggPT0dHh6eho7G1GlEhcH7NlTtB8UBOzdy9kKiCxFRkYG1q9fj5s3bwIA2rRpg4EDB8LGxkbaYET0VAbfALZz5064PXYZSaPRYPfu3bhw4YKubejQocZJR1RJxMcD9esDBQVFbaNHA87O0mUioiJCCKxZswZ3796FjY0NhgwZgpYtW0odi4jKwOBidvz48cXapkyZotuWyWRQq9UVS0VUyfz+u34hCwBt20qThYiKk8lkGDBgALZv346wsDB4eHhIHYmIysigYlbz5GLxRPRMGRna2QoKeXgAa9YAvXpJl4mIgPT0dCQmJiIgIAAAULt2bUyePJlzxxJZGYNuAHvllVeQkZFhqixElc79+0Dt2sDDh0Vt69cDfftyrCyRlK5du4aff/4Z69atQ1JSkq6dhSyR9TGomP3tt9+Qk5NjqixElcLt29qptkaOBHx8gLS0osecnTm8gEhKarUa0dHRWLVqFXJycuDl5aWboYeIrJNB72AhhKlyEFk9lQq4dw+YPBnYtav440qltp03fRFJIzU1FZGRkYiPjwcAtG/fHv369WMxS2TlDH4H8yMYouLu3AHatwfu3i358Ro1gEOHgAYNzJuLiLT++usvbNq0Cbm5ubCzs0NoaCiaNm0qdSwiMgKDi9mAgIBnFrQPHjwodyAia6HRACtXAjExwOLF2hu9nnT9OmBrC3h7a/8kImkkJCQgNzcXNWvWRFhYGNzd3aWORERGYnAxO2fOHL15ZomqquhoYNy4kh974QXg5Ze1c8sSkTSEELqLLz169ICTkxMCAwOhUCgkTkZExmRwMfv888/D29vbFFmIrMJrrwG//AKUNJ2ykxNw+bJ2BgMiks6lS5dw4sQJvPjii1AqlZDL5Wjfvr3UsYjIBAwqZjlelqq65GTg55+Lt//zn8DAgUCzZgA/uCCSTkFBAXbt2oUTJ04AAI4fP47OnTtLnIqITImzGRAZ4NGS7TodO2q/Pv5YO1sBEUknJSUFERERSExMBAB06dIFHTp0kDgVEZkaVwAjKiONBhgypGi/SRPgyBHp8hBRkQsXLmDLli3Iz8+Ho6Mjhg0bhkaNGkkdi4jMoMyLJkydOhVxcXFlOnbt2rVYuXJluUMRWRqVSobZs+W4d6+obfJk6fIQUZHDhw8jMjIS+fn5qFOnDqZMmcJClqgKKfOVWS8vL7Ro0QKdO3fG0KFDERQUBD8/P9jb2+Phw4e4dOkSDh48iDVr1qBmzZpYtGiRKXMTmdU33wTh6FH9O6DHjJEoDBHpadasGQ4ePIigoCD07NkTcrlBi1sSkZUrczH72Wef4Y033sCSJUvw888/48KFC3qPu7i4oG/fvli8eDGCg4ONHpTI3PLzgXffBY4fV+DYMT+9xzZu1C5VS0TSSEhIgK+vLwCgWrVqeOONN+Dg4CBxKiKSgkG/vnp7e+ODDz7A2bNnkZKSgtOnT+PQoUO4cuUKHj58iIiICIML2fnz58Pf3x/29vYIDAzEgQMHnnp8Xl4ePvroI9StWxd2dnZo0KABfv31V4POSfQsOTnam7p++AE4dkz/bbJ9OxASIlEwoiouPz8fmzZtwqJFi3Dt2jVdOwtZoqqr3PdfV6tWDdWqVavQydeuXYvp06dj/vz56NKlCxYuXIiBAwfi0qVLqFOnTonPGTVqFO7du4clS5agYcOGSEpKQkFBQYVyED0uO1u77OyjG6J1XFwEvvlGhgEDpMlFVNXl5ORg2bJlSE5OhkwmQ3JyMsfGElH5i1lj+PbbbzFx4kRMmjQJADBv3jzs3LkTCxYswNy5c4sdv2PHDuzbtw83btxA9erVAQD16tUzZ2SqhNRq4JtvgEfTUmL/fiApSf+Yzz8/iPfe6wAbGxvzBySq4oQQOHv2LK5evQohBJydnREWFsb//4kIgITFbH5+Pk6dOoVZs2bptQcHB+Pw4cMlPmfz5s0ICgrCV199hRUrVsDJyQlDhw7FZ599VupHTHl5ecjLy9Ptp6enAwBUKhVUKpWRvpunKzyPuc5HZZeUBERFyfD++6W/FX79NQ/u7insPyvG96D1ys/Px44dO3T3adSrVw+hoaFwcnJif1oRvgetn7n70JDzSFbMJicnQ61Wo0aNGnrtNWrU0E14/aQbN27g4MGDsLe3x4YNG5CcnIxp06bhwYMHpY6bnTt3LubMmVOsfdeuXXB0dKz4N2KA6Ohos56Pnu6nn1ohOrpeqY87O+djzpzDqF49DQD7rzJgH1qf1NRU3Hy0Womvry/c3Nywb98+aUNRufE9aP3M1YfZ2dllPlbyNYueXCJXCFHqsrkajQYymQwrV66E26M1Q7/99luEh4fjp59+KvHq7AcffIAZM2bo9tPT01G7dm0EBwfD1dXViN9J6VQqFaKjo9GvXz9+TG0hduyQITq6+D//+fMLMGCAdqU7T08Z7O27sP8qAfahdfvzzz9Rt25dXL58mX1opfgetH7m7sPCT9LLotzFbEFBAf78809cv34dY8aMgYuLC+7evQtXV1c4Ozs/8/menp5QKBTFrsImJSUVu1pbyNfXFzVr1tQVsgDQtGlTCCEQHx9f4o0AdnZ2sLOzK9ZuY2Nj9jeUFOek4m7fBoYO1W8LDweCgoBXX1VCoSj5eew/68c+tHx5eXn4448/0LNnTzg5OQEA+vXrB5VKhcuXL7MPrRz7z/qZqw8NOUe5Zpa+desWnnvuOYSGhuL111/H/fv3AQBfffUV3n333TK9hq2tLQIDA4tdro6Ojkbnzp1LfE6XLl1w9+5dZGZm6tquXr0KuVyOWrVqledboSpo3Tr9/X//W9v2/vsotZAlItNLSEjAwoULcfLkSWzevFnqOERkJcpVzL711lsICgrCw4cP9T7aHz58OHbv3l3m15kxYwYWL16MX3/9FZcvX8bbb7+N27dvY+rUqQC0QwTGjRunO37MmDHw8PDAhAkTcOnSJezfvx8zZ87EK6+8wjkGqcxOnSraHjYMeO89yaIQEbTDy44fP44lS5bg4cOHcHNzQ9euXaWORURWolzDDA4ePIhDhw7B1tZWr71u3bq4c+dOmV9n9OjRSElJwaeffoqEhAS0aNECUVFRqFu3LgDtb+m3b9/WHe/s7Izo6Gi88cYbCAoKgoeHB0aNGoXPP/+8PN8GVVGP/7Mt4wcJRGQiubm52Lx5My5fvgwAaNy4MUJDQ3mBgojKrFzFrEajgVqtLtYeHx8PFxcXg15r2rRpmDZtWomPLVu2rFhbkyZNeDckldu9e8BvvxXtc0laIukkJydj5cqVSE1NhVwuR79+/dChQ4dSbwImIipJuYYZ9OvXD/PmzdPty2QyZGZmYvbs2Rg0aJCxshEZjVoN7N4NjBih326mCS2IqAQuLi6Qy+WoVq0aXnnlFXTs2JGFLBEZrFxXZv/73/+iV69eaNasGXJzczFmzBhcu3YNnp6eWL16tbEzElXYu+8Cj/3+BQBo3x7w8pIkDlGVlZeXB1tbW8hkMtjZ2eGFF16As7Mz7O3tpY5GRFaqXMWsn58fzpw5gzVr1uDUqVPQaDSYOHEiXnzxRY5zIoty9SrwwQfA+vX67XJ58VkNiMi04uLiEBERgc6dO6NDhw4AtNM0EhFVRLmK2f3796Nz586YMGECJkyYoGsvKCjA/v370b17d6MFJKqIr74qXsh+8w3QsydQp44kkYiqHCEEDh8+jN27d0MIgVOnTiEoKAgKzoVHREZQrmK2V69eSEhIgLe3t157WloaevXqVeLNYURSWLJEf//TT4HHFoQjIhPLysrCxo0b8ffffwMAWrRogSFDhrCQJSKjKVcxW9qSsykpKboVW4gsgb09kJur3c7J0e4TkXncunULkZGRyMjIgFKpxIABA9C2bVve5EVERmVQMTvi0a3gMpkML7/8st4ysWq1GufOnSt19S4ic0tM1M5iUIiFLJH5ZGRkYMWKFVCr1fDw8MDIkSNLXaqciKgiDCpm3dzcAGivzLq4uOjd7GVra4uOHTti8uTJxk1IVA5DhwJbthTtt2snXRaiqsjFxQU9e/bE/fv3MXjw4GKL7BARGYtBxezSpUsBAPXq1cO7777LIQVkUaKjgR07tFdkHy9kAd7sRWQOsbGxcHJy0t1P0aVLFwDgsAIiMqlyjZmdPXu2sXMQVcidO8CAAYBGU/yxqVOBd94xfyaiqkKj0WD//v3Yt28fvLy8MGnSJN1cskREplauYhYAIiIi8Pvvv+P27dvIz8/Xe+z06dMVDkZUVu+8A3z7bcmPLV0KvPyyWeMQVSkZGRlYv349bt68CQCoWbMmi1giMqtyLWf7/fffY8KECfD29kZMTAzat28PDw8P3LhxAwMHDjR2RqJSXbtWvJANCQEOHwZiY1nIEpnS9evXsXDhQty8eRM2NjYYPnw4QkNDYWNjI3U0IqpCynVldv78+Vi0aBFeeOEF/Pbbb3jvvfdQv359fPzxx3jw4IGxMxKVSAigb1/9trFjga+/Bp6YApmIjEij0eDPP//EgQMHAAA1atRAeHg4V/MiIkmU68rs7du3dVNwOTg4ICMjAwAwduxYrF692njpiJ5QUAC8+CJQq5b26/btosc++wxYvpyFLJE53H705gsMDMTEiRNZyBKRZMp1ZdbHxwcpKSmoW7cu6tati6NHj6JVq1aIjY2FEMLYGYkAAFlZwIcfAqtWFX/MwQH46CPzZyKqSgoXzJHL5QgLC8Pt27fRvHlzqWMRURVXriuzvXv3xpZHcx9NnDgRb7/9Nvr164fRo0dj+PDhRg1IBGiHFPTsCXz/vX57zZpAq1bAtm0A7zkhMg21Wo3o6Gjs2LFD1+bi4sJClogsQrmuzC5atAiaR3MgTZ06FdWrV8fBgwcREhKCqVOnGjUgEaBdkvbkSf227du103ERkemkpaUhIiIC8fHxAIA2bdrAx8dH4lREREXKVczK5XLI5UUXdUeNGoVRo0YBAO7cuYOaNWsaJx0RtMML/v1v/bZdu4rf/EVExnXlyhVs3LgRubm5sLOzw9ChQ1nIEpHFKfc8s09KTEzEF198gcWLFyMnJ8dYL0tV3MOHQMOGwOOTZPTpA/TrJ10mosqucFjBsWPHAAB+fn4IDw+Hu7u7xMmIiIozaMxsamoqXnzxRXh5ecHPzw/ff/89NBoNPv74Y9SvXx9Hjx7Fr7/+aqqsVAX95z/6hSzA1byITEkIgdWrV+sK2Y4dO+KVV15hIUtEFsugK7Mffvgh9u/fj/Hjx2PHjh14++23sWPHDuTm5mL79u3o0aOHqXJSFTV3rv7+8uVAcLA0WYiqAplMhsDAQNy5cwfDhg1D48aNpY5ERPRUBhWz27Ztw9KlS9G3b19MmzYNDRs2REBAAObNm2eieFSVZWXp71++DDRpIk0WosqsoKAAKSkpqFGjBgCgadOm8Pf3h729vcTJiIiezaBhBnfv3kWzZs0AAPXr14e9vT0mTZpkkmBEmzYVbTs7s5AlMoUHDx5gyZIlWL58OdLT03XtLGSJyFoYdGVWo9HorbmtUCjg5ORk9FBEALBwYdH2iBHS5SCqrC5cuIAtW7YgPz8fDg4OSE1Nhaurq9SxiIgMYlAxK4TAyy+/DDs7OwBAbm4upk6dWqygXb9+vfESUpXl7Fy0/eKL0uUgqmxUKhV27tyJU6dOAQDq1KmDsLAwFrJEZJUMKmbHjx+vt//SSy8ZNQxVXZmZQESE/swF164VbXfubP5MRJVRcnIyIiIicO/ePQBAt27d0LNnT725w4mIrIlBxezSpUtNlYOquLfeAp42q9ujDwOIqIKOHTuGe/fuwcnJCcOHD0eDBg2kjkREVCFGWzSBqLxiY59eyHbqBDw2VJuIKqBfv37QaDTo2bMnXFxcpI5DRFRhLGZJckuW6O///nvRtp0dl60lqoikpCScOnUKAwYMgEwmg62tLUJCQqSORURkNCxmSXIPHxZtT5kCjBwpXRaiykIIgTNnziAqKgoFBQVwd3dHx44dpY5FRGR0LGZJEkIAH3wA7NsH3LhR1D5tmnSZiCqL/Px8bNu2DefOnQMANGjQAM8995zEqYiITIPFLEni8GHg3/8u3s5pi4kq5t69e1i3bh1SUlIgk8nQq1cvdO3aFTKZTOpoREQmUe65WFasWIEuXbrAz88Pt27dAgDMmzcPmx5ftomoFGvW6O/LZMBLLwG8sZqo/C5cuIDFixcjJSUFLi4uGD9+PLp168ZClogqtXIVswsWLMCMGTMwaNAgpKamQq1WAwCqVauGefPmGTMfVVKHDxdtf/45oNEAK1ZIl4eoMqhevTqEEGjYsCGmTp2KunXrSh2JiMjkylXM/vDDD/jll1/w0UcfQaFQ6NqDgoJw/vx5o4Wjyun+feD06aL94GDpshBZu9zcXN22n58fJk6ciDFjxsDR0VHCVERE5lOuYjY2NhZt2rQp1m5nZ4esrKwKh6LKa9o0wNtbv61tW2myEFkzIQSOHz+OefPmISEhQdfu6+vLYQVEVKWUq5j19/fHmTNnirVv374dzZo1q2gmqoSEAFavBhYs0G/v3x947OI+EZVBbm4uIiIisH37duTl5ZX4/zERUVVRrtkMZs6ciddffx25ubm6qwOrV6/G3LlzsXjxYmNnpErg4EFgzBj9tkmTgBkzpMlDZK3u3LmDiIgIpKamQi6Xo1+/fujQoYPUsYiIJFOuYnbChAkoKCjAe++9h+zsbIwZMwY1a9bEd999h+eff97YGakS6N1bf/+LL4APP5QmC5E1EkLg2LFjiI6OhkajQbVq1RAeHo6aNWtKHY2ISFLlnmd28uTJmDx5MpKTk6HRaOD95EBIqvIKCoBt27QzFxQUFLV/+aV2wQQiKrvLly9j586dAICmTZti6NChsLe3lzgVEZH0ylXMzpkzBy+99BIaNGgAT09PY2eiSuK337RDCZ7EoQVEhmvatCkaN26M+vXro127drzJi4jokXLdABYZGYmAgAB07NgRP/74I+7fv2/sXGTl7twpuZD9/nvAzs78eYisjRACp06dgkqlAgDIZDKMHj0a7du3ZyFLRPSYchWz586dw7lz59C7d298++23qFmzJgYNGoRVq1YhOzvb2BnJCr33nv7+nDna4Qb/+Ic0eYisSXZ2NlavXo2tW7ciKipK184iloiouHIvZ9u8eXN8+eWXuHHjBvbu3Qt/f39Mnz4dPj4+xsxHVmj7dmDVqqL9Vq2Ajz8GOnXSLltLRKW7desWfv75Z1y7dg1KpRK1atWCEELqWEREFqvcN4A9zsnJCQ4ODrC1tUVGRoYxXpKs2KJF+vu7dkmTg8iaCCFw8OBB7N27F0IIeHh4YOTIkahRo4bU0YiILFq5i9nY2FisWrUKK1euxNWrV9G9e3d88sknGDlypDHzkRW6dq1o+7vviq/4RUT6srKysGHDBly/fh0A0LJlSwwePBi2trYSJyMisnzlKmY7deqE48eP47nnnsOECRN088wSAcDFi0XbY8dKl4PIWqjVaiQkJECpVGLQoEFo3bo1x8cSEZVRuYrZXr16YfHixWjevLmx85CVS0rS33dxkSYHkaUTQugKVldXV4wcORKOjo6cs5uIyEDlKma//PJLY+egSuLbb/X3lUYZlU1UuWRmZmL9+vVo164dmjZtCgCoV6+etKGIiKxUmUuNGTNm4LPPPoOTkxNmPGPW+2+frGioyvj3v4u2R4yQLgeRpbpx4wbWr1+PrKwspKSkICAgAAqFQupYRERWq8zFbExMjG7y7piYGJMFIuuUlAQsXqzf9uQ+UVWm0Wjw559/4sCBAwCAGjVqIDw8nIUsEVEFlbmY3bt3b4nbRADw/vvAsmX6be7ukkQhsjjp6emIjIzE7du3AQCBgYHo378/bGxsJE5GRGT9yrVowiuvvFLifLJZWVl45ZVXKhyKrEtBQfFCdvx4SaIQWZysrCwsXLgQt2/fhq2tLcLCwjBkyBAWskRERlKuYva3335DTk5OsfacnBwsX768wqHIuhw6pL8fEwMsXSpNFiJL4+TkhObNm8PHxwevvvoqWrRoIXUkIqJKxaB7zdPT0yGEgBACGRkZsLe31z2mVqsRFRXFaWWqoPT0ou3WrbVfRFVZWloa5HI5XB7NTRccHAwAUHJ6DyIiozPof9Zq1apBJpNBJpMhICCg2OMymQxz5swxWjiyfF98Afzf/xXtv/SSdFmILMGVK1ewceNG1KhRA+PGjYNcLmcRS0RkQgb9D1u4Znjv3r0RGRmJ6tWr6x6ztbVF3bp14efnZ/SQZJlSU/ULWQB47J8EUZWiVqvxxx9/4OjRowAAlUqFnJwcODk5SZyMiKhyM6iY7dGjBwAgNjYWderU4XKLVVxcnP7+4MGcW5aqpocPHyIyMhJ37twBAHTs2BF9+/bltFtERGZQ5mL23LlzaNGiBeRyOdLS0nD+/PlSj23ZsqVRwpFlW7CgaDsgANi6VbosRFK5fPkyNm3ahLy8PNjb22PYsGFo3Lix1LGIiKqMMhezrVu3RmJiIry9vdG6dWvIZDIIIYodJ5PJoFarjRqSLNPjxewLL0iXg0gqarUae/fuRV5eHmrVqoWwsDBUq1ZN6lhERFVKmYvZ2NhYeHl56bapavvrL/39ceOkyUEkJYVCgfDwcJw/fx49e/bksAIiIgmUuZitW7duidtUNb3+etF2rVpA/frSZSEyp4sXLyIrKwvt27cHAHh7e6NPnz4SpyIiqrrKvWjCtm3bdPvvvfceqlWrhs6dO+PWrVtGC0eWJysLuHgR2LOnqG3mTOnyEJmLSqXC1q1bERERgR07diAhIUHqSEREhHIWs19++SUcHBwAAEeOHMGPP/6Ir776Cp6ennj77beNGpAsx44dQI0awJMLGI0dK00eInNJTk7GkiVLcOrUKQBA165dUaNGDYlTERERYODUXIXi4uLQsGFDAMDGjRsRHh6OV199FV26dEHPnj2NmY8syIcfaq/MPu6ddwB3d2nyEJnDuXPnsHXrVqhUKjg5OWH48OFo0KCB1LGIiOiRchWzzs7OSElJQZ06dbBr1y7d1Vh7e3vk5OQYNSBZBo0GiIkp2m/bFujRg0MMqHLbtm0bTp48CQCoV68eRowYoVuiloiILEO5itl+/fph0qRJaNOmDa5evYrBgwcD0N4YUa9ePWPmIwvx6Oe5zokTgLxcg1SIrIenpycA7YIx3bt3h5z/6ImILE65itmffvoJ//d//4e4uDhERkbCw8MDAHDq1Cm8wAlHKx0hgClTivY7dmQhS5VXTk6O7p6A9u3bo27duvDx8ZE4FRERlaZcxWy1atXw448/FmufM2dOhQOR5dmzBzhzpmh/0iTJohCZTH5+PqKiohAXF4dXX30VdnZ2kMlkLGSJiCxcuYpZAEhNTcWSJUtw+fJlyGQyNG3aFBMnToSbm5sx85EFWLZMf3/0aEliEJnMvXv3EBERgeTkZMhkMsTGxqJJkyZSxyIiojIo14fFJ0+eRIMGDfDf//4XDx48QHJyMv773/+iQYMGOH36tLEzkoQuXQL+97+i/fnzAWdn6fIQGZMQAqdOncLixYuRnJwMFxcXjB8/noUsEZEVKdeV2bfffhtDhw7FL7/8AqVS+xIFBQWYNGkSpk+fjv379xs1JEnjzz+BXr302x7d60dk9fLy8rB161ZcuHABANCwYUMMHz4cjo6OEicjIiJDlKuYPXnypF4hCwBKpRLvvfcegoKCjBaOpPXDD/r7vr7aL6LKYNeuXbhw4QJkMhn69OmDzp07QyaTSR2LiIgMVK5hBq6urrh9+3ax9ri4OIPnYJw/fz78/f1hb2+PwMBAHDhwoEzPO3ToEJRKJVq3bm3Q+ajsjh8v2g4JAQ4dAmxspMtDZEy9e/dGrVq1MGHCBHTp0oWFLBGRlSpXMTt69GhMnDgRa9euRVxcHOLj47FmzRpMmjTJoKm51q5di+nTp+Ojjz5CTEwMunXrhoEDB5ZYKD8uLS0N48aNQ58+fcoTn8ooPr5oe906wN9fuixEFaVWqxHz2MofTk5OeOWVV1C7dm0JUxERUUWVa5jB119/DZlMhnHjxqGgoAAAYGNjg9deew3/+te/yvw63377LSZOnIhJj+Z6mjdvHnbu3IkFCxZg7ty5pT5vypQpGDNmDBQKBTZu3Fieb4GeISlJf9/WVpocRMaQkJCAK1eu4Pz583BwcMBzzz0HALwaS0RUCZSrmLW1tcV3332HuXPn4vr16xBCoGHDhgbdOJGfn49Tp05h1qxZeu3BwcE4fPhwqc9bunQprl+/jv/973/4/PPPn3mevLw85OXl6fbT09MBACqVCiqVqsx5K6LwPOY6X0UIASQmAvPmyQEoAADVqwvdLy1VkTX1H+kTQuDEiRPYs2cPNBoN3Nzc4Orqyr60QnwfWjf2n/Uzdx8ach6Ditns7GzMnDkTGzduhEqlQt++ffH999/rlnw0RHJyMtRqNWrUqKHXXqNGDSQmJpb4nGvXrmHWrFk4cOCA3s1nTzN37twSF3PYtWuX2e9ajo6ONuv5DCUE8MknnXD2rLdee6tWcYiKiinlWVWHpfcf6SsoKEBcXBzS0tIAAG5ubqhduzbOnDmDM4+vAkJWhe9D68b+s37m6sPs7OwyH2tQMTt79mwsW7YML774Iuzt7bF69Wq89tprWLduncEhCz35MZ8QosSP/tRqNcaMGYM5c+YgICCgzK//wQcfYMaMGbr99PR01K5dG8HBwXB1dS13bkOoVCpER0ejX79+sLHgO6hu3gTOni2eb948XzRtWnWnMbCW/qMid+7cwcaNG5GWlgaFQoGePXsiOTkZwcHB7EMrxfehdWP/WT9z92HhJ+llYVAxu379eixZsgTPP/88AOCll15Cly5doFaroVAoDArp6ekJhUJR7CpsUlJSsau1AJCRkYGTJ08iJiYG//jHPwAAGo0GQggolUrs2rULvXv3LvY8Ozs72NnZFWu3sbEx+xtKinMaIjdXfz88HOjfH2jZ0nIzm5Ol9x8VUalUSEtLg7u7O0aOHAlPT09ERUWxDysB9qF1Y/9ZP3P1oSHnMKiYjYuLQ7du3XT77du3h1KpxN27dw2+I9jW1haBgYGIjo7G8OHDde3R0dEIDQ0tdryrqyvOnz+v1zZ//nzs2bMHERER8Oet9hWmVhdtN2+uncGAyFo8/qlOo0aNMGLECAQEBMDOzo7j9IiIKjGDilm1Wg3bJ25rVyqV5b45aMaMGRg7diyCgoLQqVMnLFq0CLdv38bUqVMBaIcI3LlzB8uXL4dcLkeLFi30nu/t7Q17e/ti7VQ+j/+8f3LlLyJLdvv2bWzbtg0vvPACqlWrBgC6GQuIiKhyM6iYFULg5Zdf1vvYPjc3F1OnToWTk5Oubf369WV6vdGjRyMlJQWffvopEhIS0KJFC0RFRaFu3boAtNPpPGvOWTKex4tZfgpE1kAIgYMHD2Lv3r0QQmDv3r16n/QQEVHlZ1AxO378+GJtL730UoUCTJs2DdOmTSvxsWXLlj31uZ988gk++eSTCp2ftFavBsaMKdov42QRRJLJysrChg0bcP36dQBAy5YtMXjwYIlTERGRuRlUsixdutRUOUhC2dn6hSwAPPqklsgi3bx5E5GRkcjMzIRSqcSgQYPQunVrLoJARFQF8foboW9f/f0+fYASLsITWYRr165h9erVEELAy8sL4eHh8Pb2fvYTiYioUmIxW8Wp1cCRI0X7w4YBGzZIFofomfz9/VGjRg34+Phg4MCBxW5KJSKiqoXFbBW3b5/+PkeSkCWKj4+Hn58f5HI5lEplsRtRiYio6pJLHYCkdehQ0XaXLhwrS5ZFo9Fgz549WLJkCfbv369rZyFLRESFeGW2int8Cq4+faTLQfSk9PR0rF+/Hrdu3QIAZGZmlrrcNRERVV3lvjK7YsUKdOnSBX5+frofNvPmzcOmTZuMFo5M7/G5ZQMDpctB9Li///4bCxcuxK1bt2Bra4uwsDAMGTKEhSwRERVTrmJ2wYIFmDFjBgYNGoTU1FSoH62DWq1aNcybN8+Y+cjEPv64aNvRUbocRIB2lcE//vgDK1euRHZ2Nnx8fPDqq69ylT8iIipVuYrZH374Ab/88gs++ugjKBQKXXtQUBDOnz9vtHBkei4uRduPFl4jkszDhw9x7NgxAEC7du0wceJEeHh4SJyKiIgsWbnGzMbGxqJNmzbF2u3s7JCVlVXhUGQ+jy6qAwAaNZIuBxEAeHp6YsiQIbCxsUGzZs2kjkNERFagXFdm/f39cebMmWLt27dv5w8gK3L6tHb1L4DjZUkaarUa0dHRiIuL07W1atWK/48QEVGZlevK7MyZM/H6668jNzcXQggcP34cq1evxty5c7F48WJjZyQTWbRI6gRUlaWmpiIiIgJ37tzBxYsX8Y9//ANKJSdYISIiw5TrJ8eECRNQUFCA9957D9nZ2RgzZgxq1qyJ7777Ds8//7yxM5KJREcXbb/xhnQ5qOq5fPkyNm/ejNzcXNjb22PAgAEsZImIqFzK/dNj8uTJmDx5MpKTk6HRaLg2uhVq3Rq4cUO73b27pFGoiigoKEB0dDSOHz8OAKhVqxbCwsJQjat1EBFROVX4Uoinp6cxcpAE7t4t2n58VgMiU8jJycGKFSuQkJAAAOjcuTN69+6tNyMKERGRocpVzPr7+z918vIbhZf7yGLl5QFHjxbt29tLl4WqBnt7e7i6uiI1NRXDhg1DQECA1JGIiKgSKFcxO336dL19lUqFmJgY7NixAzNnzjRGLjKxJ7uJCyaQKRQUFECj0cDW1hYymQyhoaFQqVRwdXWVOhoREVUS5Spm33rrrRLbf/rpJ5w8ebJCgcj0UlKAH34o2g8JAeTlXtiYqGQpKSlYt24dvLy8MGLECMhkMjg4OMDBwUHqaEREVIkYtYQZOHAgIiMjjfmSZAKPDy8AgH/+U5ocVHmdP38eixYtwr1793Djxg1kZGRIHYmIiCopo86FExERgerVqxvzJcnIDh8Ghgwp2u/bF2jXTro8VLmoVCps374dMTExAIB69ephxIgRcOEdhkREZCLlKmbbtGmjdwOYEAKJiYm4f/8+5s+fb7RwZHwjR+rvv/CCNDmo8rl//z4iIiKQlJQEAOjRowe6d+8OOcewEBGRCZWrmB02bJjevlwuh5eXF3r27IkmTZoYIxeZwIED+tNx1a0LDB8uXR6qPDQaDVavXo2HDx/C2dkZI0aMgL+/v9SxiIioCjC4mC0oKEC9evXQv39/+Pj4mCITmchnnxVtu7kB168DnOKTjEEulyMkJASHDh3CsGHD4OzsLHUkIiKqIgwuZpVKJV577TVcvnzZFHnIhB5fvvbnn1nIUsXcu3cPaWlpuvli/f39Ua9evafOQU1ERGRs5RrM1qFDB90NHmT5MjOBjh3128LCpMlC1k8IgdOnT2Px4sWIjIxESkqK7jEWskREZG7lGjM7bdo0vPPOO4iPj0dgYCCcnJz0Hm/ZsqVRwpFxrF4NHDtWtN+tG2BjI10esl55eXnYtm0bzp8/DwBo2LAh7Ll8HBERScigYvaVV17BvHnzMHr0aADAm2++qXtMJpNBCAGZTAa1Wm3clFQhr7+uv//119LkIOuWmJiIdevW4cGDB5DJZOjTpw86d+7Mq7FERCQpg4rZ3377Df/6178QGxtrqjxkAipV0fapU0DbttJlIet08uRJ7NixA2q1Gq6urggPD0ft2rWljkVERGRYMSuEAADUrVvXJGHI+B51mQ4LWSqPBw8eQK1WIyAgAKGhoXB0dJQ6EhEREYByjJnlR4rWJSFB6gRkrQqHDQFAnz594OPjg+eee47/BxARkUUxuJgNCAh45g+zBw8elDsQGdfx40Xb3t7S5SDrIYTAsWPH8Ndff2Hs2LFQKBRQKBS8sZOIiCySwcXsnDlz4ObmZoosZAKPj5d98kYwoifl5ORg8+bN+OuvvwAAFy5cQKtWrSRORUREVDqDi9nnn38e3rzEZzUeL2arVZMsBlmB+Ph4REREIC0tDQqFAsHBwbwaS0REFs+gYpZj5azPjh1F25xblkoihMCRI0ewe/duaDQauLu7Izw8HH5+flJHIyIieqZyzWZA1qGgAFixomifc9tTSaKjo3HkyBEAQPPmzRESEgI7OzuJUxEREZWNQcWsRqMxVQ4ygbt39fcHDpQmB1m2tm3b4uzZs+jVqxcCAwP5CQwREVmVci1nS9bhzz+Lths3Bnx8JItCFkQIgbi4ONSpUwcA4Onpibfeegu2trYSJyMiIjKcXOoAZDr5+UXb4eHS5SDLkZWVhZUrV2LZsmW4efOmrp2FLBERWStema3EvviiaLtRI+lykGW4efMmIiMjkZmZCaVSiYyMDKkjERERVRiL2UoqPx947MIbF0yowjQaDQ4cOIB9+/ZBCAFPT0+MHDmSU+wREVGlwGK2kvr+e/39fv2kyUHSyszMxPr16xEbGwsAaN26NQYOHMhhBUREVGmwmK0kvvtO+1U4TvbOnaLH+vYFlOzpKunatWuIjY2FjY0NBg8ezNW8iIio0mGJUwmoVMD06aU/vnSp2aKQhWndujUePnyI5557Dl5eXlLHISIiMjoWs5VATo7+fs2a2j8VCmDsWKBWLfNnImlkZGQgOjoaAwcOhIODA2QyGXr37i11LCIiIpNhMVsJ3L5dtO3gAMTHS5eFpPP3339jw4YNyM7OBgCMGDFC4kRERESmx2K2Eli9umi7Z0/JYpBENBoN9uzZg0OHDgEAfHx80KNHD4lTERERmQeLWSv366/Al18W7RcOMaCqIS0tDZGRkYiLiwMABAUFoX///lDyjj8iIqoi+BPPil25AkycqN/20kvSZCHzi4+Px6pVq5CTkwM7OzsMHToUzZo1kzoWERGRWbGYtVKJicDzz+u3ffcd0L27NHnI/Dw8PGBjYwN3d3eEh4fD3d1d6khERERmx2LWCg0ZAmzbpt82bBjw5puSxCEzysrKgqOjI2QyGRwcHDBu3Di4ublxWAEREVVZcqkDkGFu3ixeyDo6Ahs2SBKHzOjy5cv48ccfERMTo2vz8PBgIUtERFUai1kr89tv+vtjxwLR0dJkIfMoKCjA9u3b8fvvvyM3Nxfnz5+HEELqWERERBaBl3SsyIcfAnPnFu1PnQosWCBdHjK9Bw8eICIiAgkJCQCATp06oU+fPpDJZBInIyIisgwsZq3E9ev6hSzw9CVsyfpdvHgRW7ZsQV5eHhwcHDBs2DAEBARIHYuIiMiisJi1Amo10K2bftuXXwKNG0uTh0wvJSUFkZGREEKgdu3aCAsLg5ubm9SxiIiILA6LWStw8SLw6FNmAMBHHwEffCBdHjI9Dw8PdO/eHWq1Gr169YJczuHtREREJWExawXWrtXf//RTaXKQaZ0/fx5+fn7w8PAAAPTk2sRERETPxMs9VuDxm7z+/W+AF+kqF5VKhc2bN2P9+vWIiIhAQUGB1JGIiIisBq/MWoFWrYA//9Rujx4taRQysvv37yMiIgJJSUkAgICAAA4pICIiMgCLWSvj5SV1AjKWM2fOICoqCiqVCk5OThgxYgTq168vdSwiIiKrwmLWChRelaXKQaVSYdu2bTh79iwAwN/fHyNGjICzs7PEyYiIiKwPi1krY2cndQKqKLlcjuTkZMhkMvTs2RNdu3bl0AIiIqJyYjFr4Z5ctVShkCYHVUzh8rMymQwKhQLh4eFITU1FvXr1pA1GRERk5VjMWrj8fKkTUEXl5eVh27ZtcHFxQb9+/QAA1apVQ7Vq1aQNRkREVAmwmLVwOTlF2336SJeDyicxMRHr1q3DgwcPIJfL0a5dOxaxRERERsRi1sKtX1+0LZNJl4MMI4TAyZMnsXPnTqjVari6uiIsLIyFLBERkZGxmLVwmzYVbfPmL+uQm5uLLVu24NKlSwC0c8eGhobC0dFR4mRERESVD4tZC3f1atH2q69Kl4PKRgiBZcuW4d69e5DL5ejbty86duwIGS+rExERmQTnA7JgDx4Af/1VtB8QIF0WKhuZTIbOnTvDzc0NEyZMQKdOnVjIEhERmRCvzFqw8+f197k4lGXKyclBWloafHx8AAAtW7ZE06ZNYWNjI3EyIiKiyo/FrAU7d65oe8gQwNZWuixUsvj4eERERECtVmPq1KlwcnICABayREREZsJi1oK9+WbRdpMm0uWg4oQQOHLkCHbv3g2NRgN3d3dkZWXpilkiIiIyDxazVmLYMKkTUKHs7Gxs2rQJVx/dndesWTOEhITA3t5e4mRERERVj+Q3gM2fPx/+/v6wt7dHYGAgDhw4UOqx69evR79+/eDl5QVXV1d06tQJO3fuNGNa84mN1d/v3FmaHKTv9u3bWLhwIa5evQqFQoFBgwYhPDychSwREZFEJC1m165di+nTp+Ojjz5CTEwMunXrhoEDB+L27dslHr9//37069cPUVFROHXqFHr16oWQkBDExMSYObnpJSYWbfv5ccEES3Hy5Emkp6ejevXqmDRpEtq1a8fZCoiIiCQk6TCDb7/9FhMnTsSkSZMAAPPmzcPOnTuxYMECzJ07t9jx8+bN09v/8ssvsWnTJmzZsgVt2rQxR2SzuXataHvsWOlykL7BgwfDyckJPXv2hB1XsSAiIpKcZMVsfn4+Tp06hVmzZum1BwcH4/Dhw2V6DY1Gg4yMDFSvXr3UY/Ly8pCXl6fbT09PBwCoVCqoVKpyJDdc4XkMOd+iRQoUXjhXKNRQqTSmiEbPcOvWLVy8eBFCCKhUKtjY2KB3794ADOtPklZ53oNkWdiH1o39Z/3M3YeGnEeyYjY5ORlqtRo1atTQa69RowYSH/+M/Sm++eYbZGVlYdSoUaUeM3fuXMyZM6dY+65du8y+vGh0dHSZj83K6gTAGwDg6noIUVEPTZSKSiKEwL1793T/FmvXrm1Q/5FlYh9aP/ahdWP/WT9z9WF2dnaZj5V8NoMnxxsKIco0BnH16tX45JNPsGnTJnh7e5d63AcffIAZM2bo9tPT01G7dm0EBwfD1dW1/MENoFKpEB0djX79+pV5/tF//KOoayZN6gQzRSUAmZmZ2Lx5s66Qbd68OeRyuUH9R5alPO9BsizsQ+vG/rN+5u7Dwk/Sy0KyYtbT0xMKhaLYVdikpKRiV2uftHbtWkycOBHr1q1D3759n3qsnZ1diWMbbWxszP6GMuSc8fFF27a2NuB73zxu3LiB9evXIysrCzY2Nhg0aBCaN2+OqKgoSf7NkHGxD60f+9C6sf+sn7n60JBzSDabga2tLQIDA4tdro6Ojkbnp8xDtXr1arz88stYtWoVBg8ebOqYkpE/1jMuLtLlqEqOHj2KFStWICsrC97e3pg8eTJat24tdSwiIiJ6CkmHGcyYMQNjx45FUFAQOnXqhEWLFuH27duYOnUqAO0QgTt37mD58uUAtIXsuHHj8N1336Fjx466q7oODg5wc3OT7PswtpwcQPPofq+2baXNUpXUrFkTMpkMrVu3xsCBA3n1gIiIyApIWsyOHj0aKSkp+PTTT5GQkIAWLVogKioKdevWBQAkJCTozTm7cOFCFBQU4PXXX8frr7+uax8/fjyWLVtm7vgm889/Fm0rFNLlqAoyMzPh7OwMQHuT17Rp0+Dp6SlxKiIiIioryW8AmzZtGqZNm1biY08WqH/++afpA1mAU6eKtgMDpctRmWk0GuzZswfHjx/HpEmTdDcRspAlIiKyLpIXs1TcX38Vbf/wg3Q5Kqu0tDRERkYiLi4OAHD16tWnzohBRERElovFrIW5cqVoKVsbG0DJHjKqq1evYuPGjcjJyYGdnR1CQkLQvHlzqWMRERFRObFUsiAnTgDt2xftc6EU41Gr1di9ezeOHDkCAPD19UV4ePhTV48jIiIiy8di1oKMG6e//+9/S5OjMoqJidEVsu3bt0e/fv2g5GVvIiIiq8ef5hZi1Sr9sbJt2wKTJkmXp7Jp27Ytrl+/jpYtW6Jp06ZSxyEiIiIjYTFrITZvLtp2d9ef0YAMp1arcfToUXTo0AFKpRJyuRyjR4+WOhYREREZGYtZC5GfX7T9++/S5agMHj58iIiICNy9exdpaWkYNGiQ1JGIiIjIRFjMWojHb/Zq2VK6HNbu0qVL2Lx5M/Ly8uDg4ICGDRtKHYmIiIhMiMWshbhxo2ibq6garqCgADt37sTJkycBaFfzCgsLq1TLHBMREVFxLGYtxKVLRdu2ttLlsEYPHjzAunXrkPhogt4uXbqgV69eUHAtYCIiokqPxawFuH9ff9/JSZoc1komk+Hhw4dwdHTE8OHDObSAiIioCmExawHWrZM6gfXRaDSQy+UAAHd3d4wePRoeHh5wdXWVOBkRERGZk1zqAAT88kvR9osvSpfDWty/fx+LFi3C33//rWvz9/dnIUtERFQF8cqsBThzpmh7yhTJYliFs2fPYtu2bVCpVIiOjkaDBg0gk8mkjkVEREQSYTErsbQ0/f3nnpMmh6XLz8/H9u3bceZR5e/v748RI0awkCUiIqriWMxKLCdHf79aNUliWLSkpCSsW7cOycnJkMlk6NGjB7p166YbM0tERERVF4tZiQlRtD1smGQxLNbDhw/xyy+/oKCgAM7OzggLC0O9evWkjkVEREQWgsWsxB6fXzYzU7oclsrd3R0tWrRARkYGhg8fDifOW0ZERESPYTErsb59i7YfX9K2KktMTISLi4uucB08eDAUCgXHxxIREVExHHQooSfHy06aJE0OSyGEwMmTJ7F48WJs3LgR4tEYDKVSyUKWiIiISsQrsxJ6fLwsAIwaJU0OS5Cbm4utW7fi4sWLALSreqlUKthybV8iIiJ6ChazEnq8mO3dG6iqddvdu3cRERGBhw8fQi6Xo0+fPujUqROvxhIREdEzsZiV0OOLJVy9KlkMyQghcPz4cURHR0OtVsPNzQ3h4eGoVauW1NGIiIjISrCYldCcOUXbDRtKl0MqKpUKx44dg1qtRuPGjREaGgoHBwepYxEREZEVYTErkfR0IDq6aH/yZOmySMXW1hbh4eG4ffs2OnTowGEFREREZDAWsxLZskV/f/RoaXKYkxACR48ehY2NDYKCggAAfn5+8PPzkzgZERERWSsWsxJZvbpou0cPQKGQLos55OTkYOPGjbh69SoUCgXq16+P6tWrSx2LiIiIrByLWYlcv160PWWKdDnMIS4uDhEREUhPT4dCoUD//v3h7u4udSwiIiKqBFjMSuSvv4q227eXLocpCSFw6NAh7NmzB0IIVK9eHSNHjoSPj4/U0YiIiKiSYDErgQ0b9Pcr40xUQgisWbMGVx/NOdaiRQsMGTIEdnZ2EicjIiKiyoTFrAR+/11/vzIuliCTyVCrVi3cuHEDAwcORJs2bThbARERERkdi1kJZGUVba9aBVSWGk+j0SA7OxvOzs4AgK5du6J58+a80YuIiIhMRi51gKrI27tou3lz6XIYU2ZmJlauXInly5dDpVIB0F6dZSFLREREpsQrsxJYsaJouzIseBUbG4vIyEhkZWXBxsYGCQkJqFOnjtSxiIiIqApgMWtmSUlAfn7RvqOjdFkqSqPRYN++fdi/fz8AwMvLCyNHjoSXl5fEyYiIiKiqYDFrZomJ+vvWuvhVRkYG1q9fj5s3bwIA2rRpg4EDB8LGxkbaYERERFSlsJg1M7W6aHvqVOu9+Wv79u24efMmbGxsMGTIELRs2VLqSERERFQFsZg1s8cXSxBCuhwVNWDAAOTm5mLQoEHw9PSUOg4RERFVUZzNwMwerSEAALh3T7ochkpPT8fx48d1+66urhg3bhwLWSIiIpIUr8yaWUpK0Xa7dtLlMMS1a9ewYcMG5OTkwNXVFU2aNJE6EhEREREAFrNm92g9AQBAq1bS5SgLtVqNPXv24PDhwwAAX19feD8+SS4RERGRxFjMmtkffxRtW/K0XKmpqYiMjER8fDwAoH379ujXrx+USv6TISIiIsvBysTMHp+ay8VFuhxPc/XqVWzYsAG5ubmws7NDaGgomjZtKnUsIiIiomJYzJpZXFzRdmCgdDmepqCgALm5uahZsybCwsLg7u4udSQiIiKiErGYNbOaNYE7d7TbljTHrEajgVyundyiWbNmGDVqFAICAqBQKCRORkRERFQ6Ts1lZnl52j/t7aXN8bhLly7hp59+QkZGhq6tadOmLGSJiIjI4rGYNbPkZO2ftWtLmwPQDifYtm0b1q1bhwcPHuDQoUNSRyIiIiIyCIcZmNHDh0Xb165JlwMAUlJSEBERgcRHd6R16dIFvXr1kjYUERERkYFYzJpRbm7RdlCQdDkuXLiALVu2ID8/H46Ojhg2bBgaNWokXSAiIiKicmIxa0YHDhTd8dWggTQZzp49i40bNwIA6tSpg7CwMLi6ukoThoiIiKiCWMya0ebNRUOU1WppMjRt2hSHDh1CkyZN0LNnT90MBkRERETWiMWsGUVHF12ZHTvWfOe9fv066tevD5lMBltbW7z66qtcyYuIiIgqBV6WMxMhgIcPi4rZ3r1Nf878/Hxs2rQJ//vf/3D48GFdOwtZIiIiqixY1ZjJrVv6a9c6Opr2fElJSYiIiMD9+/chk8mg0WhMe0IiIiIiCbCYNZO7d5112z4+gKmGqgohcObMGURFRaGgoADOzs4ICwtDvXr1THNCIiIiIgmxmDUTjaZoiEH79qY5R35+PrZu3Yrz588DABo0aIDhw4fDycnJNCckIiIikhiLWTMpKCi6FNu3r2nOkZKSgosXL0Imk6FXr17o2rUrZDLZs59IREREZKVYzJpJTIy3btvGxjTn8PX1xZAhQ+Dh4YE6deqY5iREREREFoSzGZjJ9evVdNsKhXFeMy8vDxs2bEBCQoKurU2bNixkiYiIqMpgMWsm8fFFsxm0a1fx10tISMDChQtx7tw5rF+/nrMVEBERUZXEYQZmkJenv9+qVflfSwiBEydOYNeuXVCr1XBzc8PQoUO5khcRERFVSSxmzeD8ef2bsMp7T1Zubi42b96My5cvAwAaN26M0NBQODg4VDQiERERkVViMWsGQhRtDxtWvtdIT0/H0qVLkZqaCrlcjn79+qFDhw6crYCIiIiqNBazZvC//xUVnOW9N8vFxQXVq1cHAISHh6NmzZrGiEZERERk1VjMmsHp00XFbI0aZX9eTk4OlEolbGxsIJPJEBYWBrlcDnt7exOkJCIiIrI+LGbN4PFhBq+9VrbnxMXFISIiAg0aNMDQoUMBAI6OjiZIR0REhYQQKCgogFqtljpKpaJSqaBUKpGbm8u/Wytlij60sbGBwgjzlbKYNYPjx7UzDdjaCri7P32MqxAChw8fxu7duyGEwK1bt5Cbm8ursUREJpafn4+EhARkZ2dLHaXSEULAx8cHcXFxvNfDSpmiD2UyGWrVqgVnZ+cKvQ6LWRN7fPrX/Pynd35WVhY2btyIv//+GwDQokULDBkyBHZ2dqaMSERU5Wk0GsTGxkKhUMDPzw+2trYsuoxIo9EgMzMTzs7OnErSShm7D4UQuH//PuLj49GoUaMKXaFlMWtiZb0Sf+vWLURGRiIjIwNKpRIDBgxA27Zt+Z8pEZEZ5OfnQ6PRoHbt2hzSZQIajQb5+fmwt7dnMWulTNGHXl5euHnzJlQqFYtZS5aW9uxjVCoV1q1bh6ysLHh4eGDkyJGoYcidYkREZBQstIjMx1gX7FjMmtjjV2YHDdKgpBWEbWxsEBoaigsXLmDw4MGwtbU1X0AiIiIiK8Zi1sQeL2ZtbIq2Y2NjUVBQgEaNGgEAGjVqpNsmIiIiorLh5ykmFhNTtK1QaMec/Pnnn1i+fDnWr1+PtLKMQyAiIiKTyc/PR8OGDXHo0CGpo1QaSUlJ8PLywp07d0x+LsmL2fnz58Pf3x/29vYIDAzEgQMHnnr8vn37EBgYCHt7e9SvXx8///yzmZKWzzffFG07OWVgxYoV2LdvHwCgSZMmvNGAiIgq5OWXX4ZMJoNMJoNSqUSdOnXw2muv4eHDh8WOPXz4MAYNGgR3d3fY29vjueeewzfffFPivKF79+7FoEGD4OHhAUdHRzRr1gzvvPPOM4uTmJgY3b0f9vb2CAgIwKuvvqqbqccSLVq0CHXr1kWXLl2KPfbqq69CoVBgzZo1xR57+eWXMayEderPnDkDmUyGmzdv6tqEEFi0aBE6dOgAZ2dnVKtWDUFBQZg3b55Jp4N7+PAhxo4dCzc3N7i5uWHs2LFITU196nMK/z09/qVQKPD999/rjrl+/TqGDx8OLy8vuLq6YtSoUbh3757ucW9vb4wdOxazZ8821bemI2kxu3btWkyfPh0fffQRYmJi0K1bNwwcOBC3b98u8fjY2FgMGjQI3bp1Q0xMDD788EO8+eabiIyMNHPyssvP1/7ZoMF1NGmyCDdv3oSNjQ2GDx+O0NBQ2Dw+9oCIiKgcBgwYgISEBNy8eROLFy/Gli1bMG3aNL1jNmzYgB49eqBWrVrYu3cv/vrrL7z11lv44osv8Pzzz0M8tsLPwoUL0bdvX/j4+CAyMhKXLl3Czz//jLS0NHzz+FWaJ2zduhUdO3ZEXl4eVq5cicuXL2PFihVwc3PDl19+We7vT6VSlfu5ZfHDDz9g0qRJxdqzs7Oxdu1azJw5E0uWLKnQOcaOHYvp06cjNDQUe/fuxZkzZ/DPf/4TmzZtwq5duyr02k8zZswYnDlzBjt27MCOHTtw5swZjB079qnPSUhI0Pv69ddfIZPJdIs4ZWVlITg4GDKZDHv27MGhQ4eQn5+PkJAQaB6bk3TChAlYuXJlib9YGZWQUPv27cXUqVP12po0aSJmzZpV4vHvvfeeaNKkiV7blClTRMeOHct8zrS0NAFApKWlGR64HNq314jevf8Qs2d/Ij755BOxYMECcf/+fbOcm4wjPz9fbNy4UeTn50sdhcqJfWj9TN2HOTk54tKlSyInJ8ckr29K48ePF6GhoXptM2bMENWrV9ftZ2ZmCg8PDzFixIhiz9+8ebMAINasWSOEECIuLk7Y2tqK6dOnl3i+hw8fltielZUlPD09xbBhw4o9plarxc2bN4VarRZLly4Vbm5ueo9v2LBBPF6SzJ49W7Rq1UosWbJE+Pv7C5lMJn7++Wfh5+cn1Gq13nNDQkLEuHHj9L6ftm3bCjs7O+Hv7y8++eQToVKpSswshBCnTp0Scrm8xLpg2bJlomPHjiI1NVU4ODiI2NhYvcdL+rsXQoiYmBgBQHf82rVrBQCxcePGYsdqNBqRmppaar6KuHTpkgAgjh49qms7cuSIACD++uuvMr9OaGio6N27t3j48KFQq9Vi586dxf7OHjx4IACI6OhovefWq1dPLFmypMTXfdr7zpB6TbIbwPLz83Hq1CnMmjVLrz04OBiHDx8u8TlHjhxBcHCwXlv//v2xZMkSqFSqEq9y5uXlIS8vT7efnp4OQPtbnql/0wOA/HwlHBxyIZMBrVq1QnBwMGxsbMxybjKOwr5in1kv9qH1M3UfqlQqCCGg0Wj0riy1by9DYqJJTvlUPj7A8ePi2QdC+/F1YXYAuHHjBnbs2AEbGxtd244dO5CSkoIZM2bofX8AMHjwYAQEBGDVqlUYOXIkfv/9d+Tn5+Pdd98tdiwAuLq6lti+fft2JCcnl/g8IQTc3Nz0cj5+zJNtQgj8/fffWLt2LdatWweFQoGaNWvizTffxO7du9GnTx8A2o/Qd+7ciU2bNkGj0WDnzp146aWXMG/ePHTr1g3Xr1/H1KlTIYTAxx9/XOLf3759+xAQEABnZ+diuZcsWYIXX3wRLi4uGDhwIH799Vd88sknpf7dl/T9aDQa/O9//0Pjxo2LXbks5OLiUmI7oP37fpquXbsiKiqqxMcOHToENzc3tGvXTvf67du3h5ubGw4ePFimG8/v3buHbdu2YenSpQC033NOTg5kMpnevzFbW1vI5XIcOHAAvXv31j2/Xbt22L9/P15++eVir63RaCCEKHGeWUPe65IVs8nJyVCr1cXmU61RowYSS/mfIzExscTjCwoKkJycDF9f32LPmTt3LubMmVOsfdeuXWYZr/rgQS9cuNAfN2/WR+vWVxEdHW3yc5JpsO+sH/vQ+pmqD5VKJXx8fJCZmYn8wvFhABISXHH3rvlH5Amh0V18eRaVSoVt27bB1dUVarUaubm5AIAvvvhC9xrnz58HANSqVavE123QoAGuXLmC9PR0XLx4ES4uLnBycipzBgC4cOECAKBmzZqlPi8jIwO5ubkQQugdk5OTA6DoglNeXh7y8/Px008/wdPTU3dcnz59sHz5crRr1w4AsGLFCri7u6Ndu3ZIT0/HZ599hrfeegvDhw8HAHh6emLWrFn45JNPMH369BIzXb16Fd7e3sUyX79+HUePHsXSpUuRnp6OESNG4P3338f06dN18xGrVCoUFBQUe25WVhYAIDMzE+np6bh69Srq169v0N9nof379z/1cXt7+1Jf99atW/D09Cz2uKenJ27dulWmPIsWLYKzszP69u0LQNuHzZs3h6OjI2bMmIF//vOfEELgk08+gUajKfa6Xl5eOHfuXInnys/PR05ODvbv34+CggK9xwwZRyz51FxPTpgrhHjqJLolHV9Se6EPPvgAM2bM0O2np6ejdu3aCA4OfuZvO8bQvj2QmanCn3/GoV+/fhwja4VUKhWio6PZf1aMfWj9TN2Hubm5iIuLg7OzM+zt7XXtvr4yyGRlu0JqTD4+sjL/jLKxsUHPnj0xf/58ZGdnY8mSJbh69SreffddKJXaH/OF35Ozs3OJr6tQKKBQKODq6gobGxvI5XKDf0YWLr3u4uJS7LlCCGRkZMDFxQX29vaQyfS/PwcHBwBFVyHt7OxQt25d1K9fX+91xo0bh6lTp2LRokWws7PDhg0b8Pzzz8Pd3R0AcPbsWcTExODbb7/VPaewwFcqlSVexFKr1XByciqW+ffff0dwcDD8/f0BAGFhYXjzzTdx/Phx3afENjY2UCqVxZ7r5OQEoOjvu/AqZnnqjtatWxv8nEL29va6fn2cTCaDg4NDmfKsXr0aL774Iry8vHR96Orqit9//x2vv/46Fi5cCLlcjueffx5t27Yt9rpubm7Iz88v8Vy5ublwcHBA9+7d9d53AAwq/CUrZj09PaFQKIpdhU1KSip19SsfH58Sj1cqlfDw8CjxOXZ2dro32ONsbGzM8kPN1xdQqQBv7xyznZNMg/1n/diH1s9UfahWqyGTySCXy/VWATt50uinMkDZVkeSyWRwdnZGQEAAAO3NTL169cJnn32Gzz77DADQuHFjAMCVK1fQuXPnYq9x5coVNGvWDHK5HI0bN0ZaWhru3btX4ieepSk8x9WrV9GpUye9xwo/ii6ccUEIoff3XDibQmGbTCaDk5NTsRXZQkND8eqrr2L79u1o164dDhw4gG+//VZ3nEajwZw5czBixIhi+RwdHUtc4c3LywsXLlwolmfFihVITEzUW8hIrVZj6dKlGDBgAABtoXb79u1ir1tYiLm7u0MulyMgIAB//fVXuVaYc3Z2furj3bp1w/bt20t8zNfXF/fu3St23vv378PHx+eZeQ4cOIArV65g7dq1uouGhe+TAQMG4Pr160hOToZSqUS1atXg4+OD+vXr673uw4cP4eXlVeK55HK5rtB/8n1tyPtcstkMbG1tERgYWOwjo+jo6BLfaADQqVOnYsfv2rULQUFB/AFFRET0yOzZs/H111/j7t27ALT3o1SvXr3EmQg2b96Ma9eu4YUXXgAAhIeHw9bWFl999VWJr13atE7BwcHw9PQs9XmF86oXXuEr/Cge0E5lVRYODg4YMWIEVq5cidWrVyMgIACBgYG6x9u2bYsrV66gYcOGxb5KK9zatGmDv/76S282h6ioKGRkZCAmJgZnzpzRfa1btw4bN25ESkoKAO0UmxcuXNAN7Sh04sQJeHl56a4YjxkzBlevXsWmTZuKnV8I8dQ55x8/f0lfixcvLvW5nTp1QlpaGo4fP65rO3bsGNLS0kqttR63ZMkSBAYGolWrVqUe4+npiWrVqmHPnj1ISkrSzXhQ6MKFC2jTps0zz1Uhz7xFzITWrFkjbGxsxJIlS8SlS5fE9OnThZOTk7h586YQQohZs2aJsWPH6o6/ceOGcHR0FG+//ba4dOmSWLJkibCxsRERERFlPqe5ZzMQgndSWzv2n/VjH1o/zmZQutLuqA8MDBSvv/66bn/dunVCoVCIyZMni7Nnz4rY2FixePFi4e7uLsLDw4VGo9Ed+9NPPwmZTCZeeeUV8eeff4qbN2+KgwcPildffVXMmDGj1CwbN24UNjY2IiQkRERHR4vY2Fhx4sQJ8e6774rhw4cLtVotUlJShJOTk3jzzTfFtWvXxMqVK4Wfn1+JsxmUZNeuXcLOzk40btxYfPbZZ3qP7dixQyiVSjF79mxx4cIFcenSJbFmzRrx0UcflZo5OTlZ2NraivPnz+vaQkNDxejRo4sdq9FoRM2aNcW8efOEEEKkpqYKHx8fER4eLk6cOCH+/vtvsWLFCuHu7i6++uorveeNHj1aODg4iC+//FKcOHFC3Lx5U2zZskX07t1bbNiwodR8FTVgwADRsmVLceTIEXHkyBHx3HPPiSFDhugd07hxY7F+/Xq9trS0NOHo6CgWLFgghNDOSFE4m4EQQvz666/iyJEjuu+5evXqxf5tZGVlCQcHB7F///4SsxlrNgNJi1khtG+YunXrCltbW9G2bVuxb98+3WPjx48XPXr00Dv+zz//FG3atBG2traiXr16ur/ksmIxS4Zi/1k/9qH1YzFbutKK2ZUrVwpbW1tx+/ZtXdv+/fvFgAEDhJubm7C1tRXNmjUTX3/9tSgoKCj2/OjoaNG/f3/h7u4u7O3tRZMmTcS7774r7t69+9Q8J06cECNGjBBeXl7Czs5ONGzYUEyePFmcOnVKVwht2LBBNGzYUNjb24shQ4aIRYsWlbmYLSgoEL6+vgKAuH79erHHd+zYITp37iwcHByEq6uraN++vVi0aNFTMz///PO6aUETExOFUqkUv//+e4nHvvHGG+K5557T7V+7dk2EhYWJmjVrCicnJ/Hcc8+JH3/8sdgUYmq1WixYsEC0a9dOODo6CldXVxEYGCi+++47kZ2d/dR8FZGSkiJefPFF4eLiIlxcXMSLL75YbHo1AGLp0qV6bQsXLhQODg66acOeLGbff/99UaNGDWFjYyMaNWokvvnmG71fiIQQYtWqVaJx48alZjNWMSt79E1UGenp6XBzc0NaWppZbgADtDcuREVFYdCgQRwOYYXYf9aPfWj9TN2Hubm5iI2N1a1IScal0WhnZ3B1dS3XuFFTO3/+PPr27Yu///4bLi4uUsexSOXpw/bt22P69OkYM2ZMiY8/7X1nSL1mef+iiIiIiMzoueeew1dffaW3/CxVTFJSEsLDw3VjsU1J8qm5iIiIiKQ2fvx4qSNUKt7e3njvvffMci5emSUiIiIiq8ViloiIiIisFotZIiKiR6rYPdFEkjLW+43FLBERVXmFMyQYsh48EVVMfn4+AO1yyhXBG8CIiKjKUygUqFatGpKSkgBolz4tXL6TKk6j0SA/Px+5ubkWOTUXPZux+1Cj0eD+/ftwdHSEUlmxcpTFLBEREQAfHx8A0BW0ZDxCCOTk5MDBwYG/JFgpU/ShXC5HnTp1Kvx6LGaJiIgAyGQy+Pr6wtvbGyqVSuo4lYpKpcL+/fvRvXt3LlxipUzRh7a2tka5ystiloiI6DEKhaLCY/hIn0KhQEFBAezt7VnMWilL7kMOXCEiIiIiq8ViloiIiIisFotZIiIiIrJaVW7MbOEEvenp6WY7p0qlQnZ2NtLT0y1unAk9G/vP+rEPrR/70Lqx/6yfufuwsE4ry8IKVa6YzcjIAADUrl1b4iRERERE9DQZGRlwc3N76jEyUcXW7tNoNLh79y5cXFzMNtddeno6ateujbi4OLi6uprlnGQ87D/rxz60fuxD68b+s37m7kMhBDIyMuDn5/fM6buq3JVZuVyOWrVqSXJuV1dXvomtGPvP+rEPrR/70Lqx/6yfOfvwWVdkC/EGMCIiIiKyWixmiYiIiMhqsZg1Azs7O8yePRt2dnZSR6FyYP9ZP/ah9WMfWjf2n/Wz5D6scjeAEREREVHlwSuzRERERGS1WMwSERERkdViMUtEREREVovFLBERERFZLRazRjB//nz4+/vD3t4egYGBOHDgwFOP37dvHwIDA2Fvb4/69evj559/NlNSKo0hfbh+/Xr069cPXl5ecHV1RadOnbBz504zpqWSGPo+LHTo0CEolUq0bt3atAHpmQztw7y8PHz00UeoW7cu7Ozs0KBBA/z6669mSktPMrT/Vq5ciVatWsHR0RG+vr6YMGECUlJSzJSWnrR//36EhITAz88PMpkMGzdufOZzLKaeEVQha9asETY2NuKXX34Rly5dEm+99ZZwcnISt27dKvH4GzduCEdHR/HWW2+JS5cuiV9++UXY2NiIiIgIMyenQob24VtvvSX+/e9/i+PHj4urV6+KDz74QNjY2IjTp0+bOTkVMrQPC6Wmpor69euL4OBg0apVK/OEpRKVpw+HDh0qOnToIKKjo0VsbKw4duyYOHTokBlTUyFD++/AgQNCLpeL7777Tty4cUMcOHBANG/eXAwbNszMyalQVFSU+Oijj0RkZKQAIDZs2PDU4y2pnmExW0Ht27cXU6dO1Wtr0qSJmDVrVonHv/fee6JJkyZ6bVOmTBEdO3Y0WUZ6OkP7sCTNmjUTc+bMMXY0KqPy9uHo0aPF//3f/4nZs2ezmJWYoX24fft24ebmJlJSUswRj57B0P77z3/+I+rXr6/X9v3334tatWqZLCOVXVmKWUuqZzjMoALy8/Nx6tQpBAcH67UHBwfj8OHDJT7nyJEjxY7v378/Tp48CZVKZbKsVLLy9OGTNBoNMjIyUL16dVNEpGcobx8uXboU169fx+zZs00dkZ6hPH24efNmBAUF4auvvkLNmjUREBCAd999Fzk5OeaITI8pT/917twZ8fHxiIqKghAC9+7dQ0REBAYPHmyOyGQEllTPKM16tkomOTkZarUaNWrU0GuvUaMGEhMTS3xOYmJiiccXFBQgOTkZvr6+JstLxZWnD5/0zTffICsrC6NGjTJFRHqG8vThtWvXMGvWLBw4cABKJf8blFp5+vDGjRs4ePAg7O3tsWHDBiQnJ2PatGl48OABx82aWXn6r3Pnzli5ciVGjx6N3NxcFBQUYOjQofjhhx/MEZmMwJLqGV6ZNQKZTKa3L4Qo1vas40tqJ/MxtA8LrV69Gp988gnWrl0Lb29vU8WjMihrH6rVaowZMwZz5sxBQECAueJRGRjyPtRoNJDJZFi5ciXat2+PQYMG4dtvv8WyZct4dVYihvTfpUuX8Oabb+Ljjz/GqVOnsGPHDsTGxmLq1KnmiEpGYin1DC9JVICnpycUCkWx3zyTkpKK/bZSyMfHp8TjlUolPDw8TJaVSlaePiy0du1aTJw4EevWrUPfvn1NGZOewtA+zMjIwMmTJxETE4N//OMfALSFkRACSqUSu3btQu/evc2SnbTK8z709fVFzZo14ebmpmtr2rQphBCIj49Ho0aNTJqZipSn/+bOnYsuXbpg5syZAICWLVvCyckJ3bp1w+eff85PKa2AJdUzvDJbAba2tggMDER0dLRee3R0NDp37lziczp16lTs+F27diEoKAg2NjYmy0olK08fAtorsi+//DJWrVrFMV4SM7QPXV1dcf78eZw5c0b3NXXqVDRu3BhnzpxBhw4dzBWdHinP+7BLly64e/cuMjMzdW1Xr16FXC5HrVq1TJqX9JWn/7KzsyGX65cgCoUCQNHVPbJsFlXPmP2Ws0qmcDqSJUuWiEuXLonp06cLJycncfPmTSGEELNmzRJjx47VHV84lcXbb78tLl26JJYsWcKpuSRmaB+uWrVKKJVK8dNPP4mEhATdV2pqqlTfQpVnaB8+ibMZSM/QPszIyBC1atUS4eHh4uLFi2Lfvn2iUaNGYtKkSVJ9C1Waof23dOlSoVQqxfz588X169fFwYMHRVBQkGjfvr1U30KVl5GRIWJiYkRMTIwAIL799lsRExOjm17NkusZFrNG8NNPP4m6desKW1tb0bZtW7Fv3z7dY+PHjxc9evTQO/7PP/8Ubdq0Eba2tqJevXpiwYIFZk5MTzKkD3v06CEAFPsaP368+YOTjqHvw8exmLUMhvbh5cuXRd++fYWDg4OoVauWmDFjhsjOzjZzaipkaP99//33olmzZsLBwUH4+vqKF198UcTHx5s5NRXau3fvU3+2WXI9IxOC1/OJiIiIyDpxzCwRERERWS0Ws0RERERktVjMEhEREZHVYjFLRERERFaLxSwRERERWS0Ws0RERERktVjMEhEREZHVYjFLRERERFbr/9u7/5ioyzgO4O87uJPzC6hR40cgl1wXrqFJpoSrhlHHcNKucf3wpsLCHwnSKMSUrWM13RoTqLbQVuOSYciSc27WLEFQwK0TMjwldiy6NcWVojSBQODTH+6+48v9AJH4sT6v7fvH832e7/N8nns29rnnvs/gZJYxNueYzWYsXLhwpsOYNLVajZKSEq9tCgoK8NRTT01LPLNNbW0toqOjMTIyMq3jXrp0CeHh4ejt7Z3WcRljD4aTWcbYjEhLS4NMJnO5Ojo6Zjo0mM1mSUyhoaF47bXX0NnZOSX9W61WbN26VSzLZDIcP35c0iY3Nxc1NTVTMp4nY+cZHByM9evX4/Lly/fdz1R+ucjLy0N+fj7kcrnbOJ3Xl19+6bbe3Xqp1WqxXqVSITo6GoWFhRj9TzBjYmKwatUqFBcXT9lcGGP/PU5mGWMzJikpCV1dXZLrsccem+mwAACBgYHo6urCtWvXcOTIEVy8eBEpKSkYHh5+4L4feeQRzJ8/32sbf39/BAUFPfBY4xk9z5MnT6K3txfr1q3D4ODgfz62O01NTbDb7TAYDG7jHH0ZjUaXem/r9eGHH6KrqwttbW3Izc3F3r178cUXX0jGSU9PR2lp6ZSsM2NsenAyyxibMfPmzUNISIjk8vHxQVFREWJiYiAIAiIiIrBjxw7cuXPHYz+//PILEhISEBAQgMDAQDz99NO4cOGCWN/U1ITnn38eKpUKERERyM7OHvenZJlMhpCQEISGhiIhIQEmkwk2m03cOS4tLUVUVBSUSiWeeOIJlJeXS54vKCjA4sWLMW/ePISFhSE7O1usG/2agVqtBgDo9XrIZDKxPPo1g1OnTsHPzw+3b9+WjJGdnY0XXnhhyua5cuVK5OTkwOFwoL29XWzjbT3q6uqQnp6Onp4eceezoKAAADA4OIi8vDw8+uijEAQBq1evRl1dndd4Kisr8fLLL8PPz89tnKMvlUrldh7u1gsAAgICEBISArVajYyMDCxbtgw//PCDZBydToebN2+ivr7ea5yMsdmDk1nG2Kwjl8vx6aefwmaz4euvv0ZtbS3y8vI8tjcajQgPD4fVakVzczPef/99KBQKAPfeg9TpdHj11VfR2tqKo0ePoqGhAVlZWfcVkzNxunv3LiwWC9555x289957sNls2LZtG9LT03HmzBkAwLfffovi4mIcOnQIdrsdx48fR0xMjNt+rVYrAKCsrAxdXV1iebTExEQsXLgQx44dE+8NDw+jqqpK3J2cinnevn0bR44cAQDx8wO8r0d8fDxKSkokO6e5ubkA7u1yNjY2orKyEq2trTAYDEhKSoLdbvcYw9mzZ7Fy5coJx+zJ6PUai4hQV1eHtrY2yTwBQKlUYvny5Th37twDx8AYmybEGGMzYPPmzeTj40OCIIhXamqq27ZVVVUUFBQklsvKymjBggViOSAggMxms9tnN27cSFu3bpXcO3fuHMnlcurv73f7zNj+//jjD4qLi6Pw8HAaGBig+Ph42rJli+QZg8FAycnJRER04MAB0mq1NDg46Lb/yMhIKi4uFssAyGKxSNqYTCZavny5WM7Ozqa1a9eK5VOnTpFSqaTu7u4HmicAEgSB5s+fTwAIAKWkpLht7zTeehARdXR0kEwmo6tXr0ruv/jii7Rnzx6PfS9YsIAOHz7sMU7nFRwc7HH8setFdO8zVyqVJAgCKRQKAkB+fn7U2NjoEoNer6e0tDSvnwFjbPbwnclEmjH2/5aQkIDS0lKxLAgCAODMmTPYv38/rly5gr///htDQ0P4559/0NvbK7YZ7d1330VGRgbKy8uRmJgIg8GAqKgoAEBzczM6OjpQUVEhticijIyMoLOzE0uXLnUbW09PD/z9/UFE6OvrQ2xsLKqrq6FUKtHW1iY5wAUAa9aswSeffAIAMBgMKCkpwZIlS5CUlITk5GSsX78evr6T/5NrNBrx7LPP4tq1awgLC0NFRQWSk5OxaNGiB5pnQEAAWlpaMDQ0hPr6ehQWFuLgwYOSNve7HgDQ0tICIoJWq5XcHxgY8PoucH9/v8srBqPjdHIeDnPytl5Ou3btQlpaGv766y/k5+dj7dq1iI+PdxlLpVKhr6/PY4yMsdmFk1nG2IwRBAEajUZyz+FwIDk5Gdu3b8dHH32Ehx56CA0NDXjrrbfc/mQM3Hu/dMOGDTh58iS+//57mEwmVFZWQq/XY2RkBNu2bZO8s+q0ePFij7E5kye5XI7g4GCXpE0mk0nKRCTei4iIQHt7O3788UecPn0aO3bsQGFhIerr611+1p6oVatWISoqCpWVlXj77bdhsVhQVlYm1k92nnK5XFyD6OhoXL9+Ha+//jrOnj0LYHLr4YzHx8cHzc3N8PHxkdT5+/t7fO7hhx/GrVu3vMbpznjr5exbo9FAo9Hg2LFj0Gg0iIuLQ2JioqRdd3e3+GWIMTb7cTLLGJtVLly4gKGhIRw4cEDcfauqqhr3Oa1WC61Wi5ycHLz55psoKyuDXq9HbGwsLl++7DURcsdb8rR06VI0NDRg06ZN4r2mpibJ7qdKpUJKSgpSUlKQmZmJ6OhoXLp0CbGxsS79KRSKCZ2e37BhAyoqKhAeHg65XI5169aJdZOd51g5OTkoKiqCxWKBXq+f0HoolUqX+FesWIHh4WH8+eefeO655yY8/ooVK3DlypX7jnu8ZHesRYsWYefOncjNzcXPP/8s+XJis9mQmpp63zEwxmYGHwBjjM0qUVFRGBoawmeffYbffvsN5eXlLj97j9bf34+srCzU1dXB4XCgsbERVqtVTCx3796N8+fPIzMzExcvXoTdbseJEyewc+fOSce4a9cumM1mHDx4EHa7HUVFRaiurhYPPpnNZnz11Vew2WziHFQqFSIjI932p1arUVNTg+vXr7vdlXQyGo1oaWnBvn37kJqaKvk5fqrmGRgYiIyMDJhMJhDRhNZDrVbjzp07qKmpwY0bN9DX1wetVguj0YhNmzahuroanZ2dsFqt+Pjjj/Hdd995HF+n06GhoeG+Yp6szMxMtLe3Sw7W/f7777h69arLbi1jbBabwfd1GWP/Y5s3b6ZXXnnFbV1RURGFhoaSSqUinU5Hhw8fJgB069YtIpIe+BkYGKA33niDIiIiSKlUUlhYGGVlZUkOPf3000/00ksvkb+/PwmCQMuWLaN9+/Z5jM3dgaaxPv/8c1qyZAkpFArSarWSQ0sWi4VWr15NgYGBJAgCxcXF0enTp8X6sQfATpw4QRqNhnx9fSkyMpKIXA+AOT3zzDMEgGpra13qpmqeDoeDfH196ejRo0Q0/noQEW3fvp2CgoIIAJlMJiIiGhwcpA8++IDUajUpFAoKCQkhvV5Pra2tHmPq7u4mlUpFv/7667hxTrSeyPUzd9qyZQs9+eSTNDw8TERE+/fvJ51O57UvxtjsIiMa9e9PGGOMsRmWl5eHnp4eHDp0aFrHHRgYwOOPP45vvvkGa9asmdaxGWOTx68ZMMYYm1Xy8/MRGRk57f+Fy+FwID8/nxNZxuYY3plljDHGGGNzFu/MMsYYY4yxOYuTWcYYY4wxNmdxMssYY4wxxuYsTmYZY4wxxticxcksY4wxxhibsziZZYwxxhhjcxYns4wxxhhjbM7iZJYxxhhjjM1ZnMwyxhhjjLE561/PpQwbcBGWLwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal Threshold: 0.03\n",
      "Precision: 0.03\n",
      "Recall: 0.76\n",
      "F1-Score: 0.06\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, confusion_matrix, roc_auc_score,roc_curve,auc,classification_report\n",
    "import xgboost as xgb\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import optuna\n",
    "\n",
    "# Split the data into train and test sets\n",
    "X_train = train_df.drop(columns=['bad_flag'])\n",
    "y_train = train_df['bad_flag']\n",
    "X_test = test_df.drop(columns=['bad_flag'])\n",
    "y_test = test_df['bad_flag']\n",
    "\n",
    "# Define the Optuna objective function\n",
    "def objective(trial):\n",
    "    # Suggest hyperparameters\n",
    "    params = {\n",
    "        'objective': 'binary:logistic',\n",
    "        'eval_metric': 'logloss',\n",
    "        'max_depth': trial.suggest_int('max_depth', 3, 15),  # Increased max_depth range\n",
    "        'eta': trial.suggest_float('eta', 0.005, 0.3, log=True),  # More granular eta range\n",
    "        'subsample': trial.suggest_float('subsample', 0.5, 1.0),  # Subsample range\n",
    "        'colsample_bytree': trial.suggest_float('colsample_bytree', 0.5, 1.0),  # Colsample range\n",
    "        'min_child_weight': trial.suggest_int('min_child_weight', 1, 20),  # Increased min_child_weight range\n",
    "        'gamma': trial.suggest_float('gamma', 0, 5),  # Gamma range\n",
    "        'lambda': trial.suggest_float('lambda', 0, 10),  # L2 regularization range\n",
    "        'alpha': trial.suggest_float('alpha', 0, 10),  # L1 regularization range\n",
    "        'scale_pos_weight': trial.suggest_float('scale_pos_weight', 1, 10),  # Class imbalance correction\n",
    "        'n_estimators': trial.suggest_int('n_estimators', 50, 500),  # Number of boosting rounds\n",
    "        'tree_method': 'hist',  #\n",
    "        'device': 'cuda',\n",
    "        'seed': 42\n",
    "    }\n",
    "\n",
    "    # Convert data into DMatrix\n",
    "    dtrain = xgb.DMatrix(X_train, label=y_train)\n",
    "    dtest = xgb.DMatrix(X_test, label=y_test)\n",
    "    \n",
    "    # Train the model\n",
    "    bst = xgb.train(params, dtrain, num_boost_round=params['n_estimators'], verbose_eval=False)\n",
    "    \n",
    "    # Predict probabilities\n",
    "    y_pred_prob = bst.predict(dtest)\n",
    "    \n",
    "    # Calculate AUC\n",
    "    auc_score = roc_auc_score(y_test, y_pred_prob)\n",
    "    return auc_score  # Optuna will maximize this\n",
    "\n",
    "# Run Optuna study\n",
    "study = optuna.create_study(direction='maximize')\n",
    "study.optimize(objective, n_trials=100, timeout=3600)\n",
    "\n",
    "# Best hyperparameters\n",
    "print(\"Best Hyperparameters:\", study.best_params)\n",
    "\n",
    "# Train the model using the best hyperparameters\n",
    "best_params = study.best_params\n",
    "best_params['objective'] = 'binary:logistic'\n",
    "best_params['eval_metric'] = 'logloss'\n",
    "best_params['seed'] = 42\n",
    "best_params['tree_method'] = 'gpu_hist'  # Enable GPU acceleration\n",
    "\n",
    "dtrain = xgb.DMatrix(X_train, label=y_train)\n",
    "dtest = xgb.DMatrix(X_test, label=y_test)\n",
    "\n",
    "bst = xgb.train(best_params, dtrain, num_boost_round=100)\n",
    "\n",
    "# Make predictions\n",
    "y_pred_prob = bst.predict(dtest)\n",
    "\n",
    "# Compute ROC curve and AUC score\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr, tpr, color='blue', lw=2, label=f\"ROC Curve (AUC = {roc_auc:.2f})\")\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n",
    "plt.xlabel('False Positive Rate (FPR)')\n",
    "plt.ylabel('True Positive Rate (TPR)')\n",
    "plt.title('ROC Curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "# Find the optimal threshold (Youden's J statistic)\n",
    "optimal_idx = np.argmax(tpr - fpr)\n",
    "optimal_threshold = thresholds[optimal_idx]\n",
    "\n",
    "print(f\"Optimal Threshold: {optimal_threshold:.2f}\")\n",
    "\n",
    "# Apply the optimal threshold\n",
    "y_pred_optimal = (y_pred_prob > optimal_threshold).astype(int)\n",
    "\n",
    "# Calculate evaluation metrics with optimal threshold\n",
    "precision = precision_score(y_test, y_pred_optimal)\n",
    "recall = recall_score(y_test, y_pred_optimal)\n",
    "f1 = f1_score(y_test, y_pred_optimal)\n",
    "\n",
    "print(f\"Precision: {precision:.2f}\")\n",
    "print(f\"Recall: {recall:.2f}\")\n",
    "print(f\"F1-Score: {f1:.2f}\")\n",
    "\n",
    "print(\"Classification Report:\\n\")\n",
    "report = classification_report(y_test, y_pred_optimal, target_names=[\"Non-Fraud\", \"Fraud\"])\n",
    "print(report)\n",
    "\n",
    "# Save the report to a text file\n",
    "with open(\"./assets/classification_report.txt\", \"w\") as file:\n",
    "    file.write(report)\n",
    "\n",
    "print(\"Classification report saved as 'classification_report.txt'.\")\n",
    "\n",
    "# Confusion matrix with optimal threshold\n",
    "cm = confusion_matrix(y_test, y_pred_optimal)\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['Class 0', 'Class 1'], yticklabels=['Class 0', 'Class 1'])\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n   Non-Fraud       1.00      0.69      0.82     19092\\n       Fraud       0.03      0.76      0.06       270\\n\\n    accuracy                           0.69     19362\\n   macro avg       0.51      0.73      0.44     19362\\nweighted avg       0.98      0.69      0.81     19362\\n'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report saved as 'classification_report.txt'.\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
      "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
      "\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from sklearn.metrics import accuracy_score,precision_score, recall_score, f1_score, roc_auc_score, roc_curve, confusion_matrix, classification_report\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Define custom dataset class\n",
    "class FraudDataset(Dataset):\n",
    "    def __init__(self, data, labels):\n",
    "        self.data = torch.tensor(data, dtype=torch.float32)\n",
    "        self.labels = torch.tensor(labels, dtype=torch.float32)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.data[idx], self.labels[idx]\n",
    "\n",
    "# Define MLP model\n",
    "class FraudMLP(nn.Module):\n",
    "    def __init__(self, input_dim):\n",
    "        super(FraudMLP, self).__init__()\n",
    "        self.model = nn.Sequential(\n",
    "            nn.Linear(input_dim, 512),\n",
    "            nn.BatchNorm1d(512),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.3),\n",
    "\n",
    "            nn.Linear(512, 256),\n",
    "            nn.BatchNorm1d(256),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.3),\n",
    "\n",
    "            nn.Linear(256, 64),\n",
    "            nn.BatchNorm1d(64),\n",
    "            nn.ReLU(),\n",
    "\n",
    "            nn.Linear(64, 1),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.model(x)\n",
    "\n",
    "# Early stopping class\n",
    "class EarlyStopping:\n",
    "    def __init__(self, patience=5, delta=0.001):\n",
    "        self.patience = patience\n",
    "        self.delta = delta\n",
    "        self.counter = 0\n",
    "        self.best_score = None\n",
    "        self.early_stop = False\n",
    "\n",
    "    def __call__(self, val_loss, model):\n",
    "        if self.best_score is None or val_loss < self.best_score - self.delta:\n",
    "            self.best_score = val_loss\n",
    "            self.counter = 0\n",
    "        else:\n",
    "            self.counter += 1\n",
    "            if self.counter >= self.patience:\n",
    "                self.early_stop = True\n",
    "\n",
    "# Prepare data\n",
    "X_train = train_df.drop(\"bad_flag\", axis=1).values\n",
    "y_train = train_df[\"bad_flag\"].values\n",
    "X_val = test_df.drop(\"bad_flag\", axis=1).values\n",
    "y_val = test_df[\"bad_flag\"].values\n",
    "\n",
    "train_dataset = FraudDataset(X_train, y_train)\n",
    "val_dataset = FraudDataset(X_val, y_val)\n",
    "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
    "val_loader = DataLoader(val_dataset, batch_size=64)\n",
    "\n",
    "# Model setup\n",
    "input_dim = X_train.shape[1]\n",
    "model = FraudMLP(input_dim)\n",
    "criterion = nn.BCELoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001, weight_decay=1e-5)\n",
    "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)\n",
    "\n",
    "# Training loop\n",
    "n_epochs = 50\n",
    "early_stopping = EarlyStopping(patience=10)\n",
    "\n",
    "for epoch in range(n_epochs):\n",
    "    model.train()\n",
    "    train_loss = 0\n",
    "\n",
    "    for inputs, labels in train_loader:\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(inputs).squeeze()\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        train_loss += loss.item()\n",
    "\n",
    "    train_loss /= len(train_loader)\n",
    "\n",
    "    # Validation\n",
    "    model.eval()\n",
    "    val_loss = 0\n",
    "    val_preds = []\n",
    "    val_targets = []\n",
    "    val_probs = []\n",
    "    with torch.no_grad():\n",
    "        for inputs, labels in val_loader:\n",
    "            outputs = model(inputs).squeeze()\n",
    "            loss = criterion(outputs, labels)\n",
    "            val_loss += loss.item()\n",
    "            val_probs.extend(outputs.numpy())\n",
    "            val_preds.extend((outputs > 0.01).int().numpy())\n",
    "            val_targets.extend(labels.int().numpy())\n",
    "\n",
    "    val_loss /= len(val_loader)\n",
    "    val_accuracy = accuracy_score(val_targets, val_preds)\n",
    "    val_precision = precision_score(val_targets, val_preds, zero_division=0)\n",
    "    val_recall = recall_score(val_targets, val_preds, zero_division=0)\n",
    "    val_f1 = f1_score(val_targets, val_preds, zero_division=0)\n",
    "    val_auc = roc_auc_score(val_targets, val_probs)\n",
    "    scheduler.step()\n",
    "\n",
    "    print(f\"Epoch {epoch+1}/{n_epochs}, Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}, Val Accuracy: {val_accuracy:.4f}, Precision: {val_precision:.4f}, Recall: {val_recall:.4f}, F1: {val_f1:.4f}, AUC: {val_auc:.4f}\")\n",
    "\n",
    "    early_stopping(val_loss, model)\n",
    "    if early_stopping.early_stop:\n",
    "        print(\"Early stopping triggered.\")\n",
    "        break\n",
    "\n",
    "# Calculate optimal threshold using ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(val_targets, val_probs)\n",
    "optimal_idx = np.argmax(tpr - fpr)\n",
    "optimal_threshold = thresholds[optimal_idx]\n",
    "print(f\"Optimal Threshold: {optimal_threshold:.4f}\")\n",
    "\n",
    "# Final testing\n",
    "X_test = test_df.drop(\"bad_flag\", axis=1).values\n",
    "y_test = test_df[\"bad_flag\"].values\n",
    "test_dataset = FraudDataset(X_test, y_test)\n",
    "test_loader = DataLoader(test_dataset, batch_size=64)\n",
    "\n",
    "model.eval()\n",
    "test_preds = []\n",
    "test_probs = []\n",
    "test_targets = []\n",
    "with torch.no_grad():\n",
    "    for inputs, labels in test_loader:\n",
    "        outputs = model(inputs).squeeze()\n",
    "        test_probs.extend(outputs.numpy())\n",
    "        test_targets.extend(labels.int().numpy())\n",
    "\n",
    "final_preds = (np.array(test_probs) > optimal_threshold).astype(int)\n",
    "\n",
    "# Metrics\n",
    "accuracy = accuracy_score(test_targets, final_preds)\n",
    "precision = precision_score(test_targets, final_preds, zero_division=0)\n",
    "recall = recall_score(test_targets, final_preds, zero_division=0)\n",
    "f1 = f1_score(test_targets, final_preds, zero_division=0)\n",
    "auc = roc_auc_score(test_targets, test_probs)\n",
    "print(f\"Test Accuracy: {accuracy:.4f}, Precision: {precision:.4f}, Recall: {recall:.4f}, F1: {f1:.4f}, AUC: {auc:.4f}\")\n",
    "\n",
    "# Confusion matrix and classification report\n",
    "conf_matrix = confusion_matrix(test_targets, final_preds)\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=[\"Non-Fraud\", \"Fraud\"], yticklabels=[\"Non-Fraud\", \"Fraud\"])\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Classification Report:\\n\")\n",
    "print(classification_report(test_targets, final_preds, target_names=[\"Non-Fraud\", \"Fraud\"]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('data/processed/train.csv')\n",
    "test_df = pd.read_csv('data/processed/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>onus_attribute_2</th>\n",
       "      <th>onus_attribute_26</th>\n",
       "      <th>onus_attribute_17</th>\n",
       "      <th>onus_attribute_23</th>\n",
       "      <th>onus_attribute_20</th>\n",
       "      <th>bureau_439</th>\n",
       "      <th>onus_attribute_29</th>\n",
       "      <th>bureau_450</th>\n",
       "      <th>onus_attribute_32</th>\n",
       "      <th>onus_attribute_38</th>\n",
       "      <th>...</th>\n",
       "      <th>onus_attribute_27</th>\n",
       "      <th>bureau_66</th>\n",
       "      <th>bureau_429</th>\n",
       "      <th>onus_attribute_31</th>\n",
       "      <th>bureau_108</th>\n",
       "      <th>bureau_enquiry_25</th>\n",
       "      <th>bureau_116</th>\n",
       "      <th>bureau_428</th>\n",
       "      <th>account_number</th>\n",
       "      <th>bad_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0.656522</td>\n",
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       "      <td>0.263806</td>\n",
       "      <td>0.263458</td>\n",
       "      <td>0.768328</td>\n",
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       "      <td>0.389047</td>\n",
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       "      <td>2.138123</td>\n",
       "      <td>1.466881</td>\n",
       "      <td>1.258045</td>\n",
       "      <td>1.257115</td>\n",
       "      <td>1.485753</td>\n",
       "      <td>-0.349896</td>\n",
       "      <td>1.345870</td>\n",
       "      <td>1.866639</td>\n",
       "      <td>1.814580</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.343616</td>\n",
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       "      <td>-1.013304</td>\n",
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       "      <td>-0.921248</td>\n",
       "      <td>1.483524</td>\n",
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       "      <td>-0.586137</td>\n",
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       "      <td>0</td>\n",
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       "      <td>-0.815233</td>\n",
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       "      <td>1.866639</td>\n",
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       "      <td>-0.349896</td>\n",
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       "      <td>...</td>\n",
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       "      <td>-0.120885</td>\n",
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       "      <td>1.028563</td>\n",
       "      <td>0.225943</td>\n",
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       "    <tr>\n",
       "      <th>77442</th>\n",
       "      <td>-0.815917</td>\n",
       "      <td>-0.467700</td>\n",
       "      <td>-0.928204</td>\n",
       "      <td>0.267197</td>\n",
       "      <td>0.266847</td>\n",
       "      <td>-0.551765</td>\n",
       "      <td>-0.349896</td>\n",
       "      <td>-0.607144</td>\n",
       "      <td>-0.560162</td>\n",
       "      <td>-0.585105</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.343616</td>\n",
       "      <td>1.396848</td>\n",
       "      <td>-0.711215</td>\n",
       "      <td>-0.113909</td>\n",
       "      <td>0.426667</td>\n",
       "      <td>-0.702699</td>\n",
       "      <td>1.205045</td>\n",
       "      <td>-0.290026</td>\n",
       "      <td>861.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77443</th>\n",
       "      <td>1.714358</td>\n",
       "      <td>2.138123</td>\n",
       "      <td>1.163492</td>\n",
       "      <td>1.295573</td>\n",
       "      <td>1.294618</td>\n",
       "      <td>-0.334555</td>\n",
       "      <td>-0.349896</td>\n",
       "      <td>-0.238679</td>\n",
       "      <td>1.692559</td>\n",
       "      <td>1.557620</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.343616</td>\n",
       "      <td>-0.241395</td>\n",
       "      <td>0.212549</td>\n",
       "      <td>-0.113909</td>\n",
       "      <td>0.426667</td>\n",
       "      <td>1.121273</td>\n",
       "      <td>0.068165</td>\n",
       "      <td>-0.223141</td>\n",
       "      <td>15796.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77444 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       onus_attribute_2  onus_attribute_26  onus_attribute_17  \\\n",
       "0             -0.157467          -0.467700          -0.640470   \n",
       "1              0.656522          -0.467700           0.119309   \n",
       "2             -0.436695           2.138123           1.466881   \n",
       "3             -0.815233          -0.467700          -0.920311   \n",
       "4              1.754213           2.138123           2.083792   \n",
       "...                 ...                ...                ...   \n",
       "77439         -0.527477           2.138123          -0.752289   \n",
       "77440         -0.815985          -0.467700          -0.034543   \n",
       "77441         -0.384968          -0.467700          -0.223922   \n",
       "77442         -0.815917          -0.467700          -0.928204   \n",
       "77443          1.714358           2.138123           1.163492   \n",
       "\n",
       "       onus_attribute_23  onus_attribute_20  bureau_439  onus_attribute_29  \\\n",
       "0              -0.811798          -0.811594    0.069370          -0.349896   \n",
       "1               0.263806           0.263458    0.768328          -0.349896   \n",
       "2               1.258045           1.257115    1.485753          -0.349896   \n",
       "3              -0.945217          -0.944947   -0.982608          -0.349896   \n",
       "4               1.384953           1.383935   -0.806127          -0.349896   \n",
       "...                  ...                ...         ...                ...   \n",
       "77439          -0.816734          -0.816527    0.018441          -0.349896   \n",
       "77440           0.089129           0.088872   -0.973885          -0.349896   \n",
       "77441          -0.350331          -0.350360   -0.938543          -0.349896   \n",
       "77442           0.267197           0.266847   -0.551765          -0.349896   \n",
       "77443           1.295573           1.294618   -0.334555          -0.349896   \n",
       "\n",
       "       bureau_450  onus_attribute_32  onus_attribute_38  ...  \\\n",
       "0       -0.010905          -0.560162          -0.585105  ...   \n",
       "1        0.759409          -0.560162          -0.585105  ...   \n",
       "2        1.345870           1.866639           1.814580  ...   \n",
       "3       -1.059947          -0.560162          -0.585105  ...   \n",
       "4       -0.849073           1.866639           1.814580  ...   \n",
       "...           ...                ...                ...  ...   \n",
       "77439    1.910927           1.692559           1.557620  ...   \n",
       "77440   -1.008071          -0.560162          -0.585105  ...   \n",
       "77441   -0.963230          -0.560162          -0.585105  ...   \n",
       "77442   -0.607144          -0.560162          -0.585105  ...   \n",
       "77443   -0.238679           1.692559           1.557620  ...   \n",
       "\n",
       "       onus_attribute_27  bureau_66  bureau_429  onus_attribute_31  \\\n",
       "0              -0.343616  -0.467739   -0.505972          -0.113909   \n",
       "1              -0.343616   2.507463    0.216754          -0.113909   \n",
       "2              -0.343616   1.635639   -1.013304          -0.113909   \n",
       "3              -0.343616  -0.807359    1.950946          -0.113909   \n",
       "4               2.910229   0.076181    0.652953          -0.113909   \n",
       "...                  ...        ...         ...                ...   \n",
       "77439           2.910229  -0.802353   -0.120885          -0.113909   \n",
       "77440          -0.343616  -0.802353   -1.533982          -0.113909   \n",
       "77441          -0.343616  -0.726846    0.670062          -0.113909   \n",
       "77442          -0.343616   1.396848   -0.711215          -0.113909   \n",
       "77443          -0.343616  -0.241395    0.212549          -0.113909   \n",
       "\n",
       "       bureau_108  bureau_enquiry_25  bureau_116  bureau_428  account_number  \\\n",
       "0       -0.921248          -0.702699   -0.986663   -2.079715         47642.0   \n",
       "1        1.295786           1.121273    1.491365    0.389047         95032.0   \n",
       "2       -0.921248           1.483524    2.155846   -0.586137         40510.0   \n",
       "3       -0.921248          -0.702699   -1.708306    0.896966         51692.0   \n",
       "4       -0.921248          -0.702699    0.256992    0.332454         27459.0   \n",
       "...           ...                ...         ...         ...             ...   \n",
       "77439    0.994812          -0.702699    1.028563    0.225943          6266.0   \n",
       "77440   -0.921248          -0.702699   -0.675623   -1.626601         54887.0   \n",
       "77441   -0.921248          -0.702699   -0.149087    0.726087         76821.0   \n",
       "77442    0.426667          -0.702699    1.205045   -0.290026           861.0   \n",
       "77443    0.426667           1.121273    0.068165   -0.223141         15796.0   \n",
       "\n",
       "       bad_flag  \n",
       "0             0  \n",
       "1             0  \n",
       "2             0  \n",
       "3             0  \n",
       "4             0  \n",
       "...         ...  \n",
       "77439         0  \n",
       "77440         0  \n",
       "77441         0  \n",
       "77442         0  \n",
       "77443         0  \n",
       "\n",
       "[77444 rows x 42 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check for GPU\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print(f\"[INFO] Using device: {device}\")\n",
    "\n",
    "\n",
    "# Define custom dataset class\n",
    "class FraudDataset(Dataset):\n",
    "    def __init__(self, data, labels):\n",
    "        self.data = torch.tensor(data, dtype=torch.float32)\n",
    "        self.labels = torch.tensor(labels, dtype=torch.float32)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.data[idx], self.labels[idx]\n",
    "\n",
    "# Define MLP model\n",
    "class FraudMLP(nn.Module):\n",
    "    def __init__(self, input_dim, num_layers, layer_nodes):\n",
    "        super(FraudMLP, self).__init__()\n",
    "        layers = []\n",
    "        current_dim = input_dim\n",
    "\n",
    "        for i in range(num_layers):\n",
    "            layers.append(nn.Linear(current_dim, layer_nodes[i]))\n",
    "            layers.append(nn.BatchNorm1d(layer_nodes[i]))\n",
    "            layers.append(nn.ReLU())\n",
    "            layers.append(nn.Dropout(DROPOUT_RATE))\n",
    "            current_dim = layer_nodes[i]\n",
    "\n",
    "        layers.append(nn.Linear(current_dim, 1))\n",
    "        layers.append(nn.Sigmoid())\n",
    "\n",
    "        self.model = nn.Sequential(*layers)\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.model(x)\n",
    "\n",
    "# Early stopping class\n",
    "class EarlyStopping:\n",
    "    def __init__(self, patience=PATIENCE, delta=0.001):\n",
    "        self.patience = patience\n",
    "        self.delta = delta\n",
    "        self.counter = 0\n",
    "        self.best_score = None\n",
    "        self.early_stop = False\n",
    "\n",
    "    def __call__(self, val_loss, model):\n",
    "        if self.best_score is None or val_loss < self.best_score - self.delta:\n",
    "            self.best_score = val_loss\n",
    "            self.counter = 0\n",
    "        else:\n",
    "            self.counter += 1\n",
    "            if self.counter >= self.patience:\n",
    "                self.early_stop = True\n",
    "\n",
    "# Prepare data\n",
    "print(f\"PREPARING DATA\")\n",
    "X_train = train_df.drop([\"bad_flag\",\"account_number\"], axis=1).values\n",
    "y_train = train_df[\"bad_flag\"].values\n",
    "X_val = test_df.drop([\"bad_flag\",\"account_number\"], axis=1).values\n",
    "y_val = test_df[\"bad_flag\"].values\n",
    "\n",
    "train_dataset = FraudDataset(X_train, y_train)\n",
    "val_dataset = FraudDataset(X_val, y_val)\n",
    "train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, pin_memory=True)\n",
    "val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False, pin_memory=True)\n",
    "\n",
    "# Model setup\n",
    "print(\"[INFO] Initializing the MLP model...\")\n",
    "INPUT_DIM = X_train.shape[1]\n",
    "model = FraudMLP(INPUT_DIM, NO_LAYERS, HIDDEN_DIMS).to(device)\n",
    "criterion = nn.BCELoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE, weight_decay=WEIGHT_DECAY)\n",
    "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=STEP_SIZE, gamma=GAMMA)\n",
    "\n",
    "summary(model, input_size=X_train.shape)\n",
    "\n",
    "# Training loop\n",
    "early_stopping = EarlyStopping(patience=PATIENCE)\n",
    "print(f\"[INFO] Starting training for {N_EPOCHS} epochs with patience {PATIENCE}...\")\n",
    "for epoch in range(N_EPOCHS):\n",
    "    model.train()\n",
    "    train_loss = 0\n",
    "\n",
    "    for inputs, labels in train_loader:\n",
    "        inputs, labels = inputs.to(device), labels.to(device)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(inputs).squeeze()\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        train_loss += loss.item()\n",
    "\n",
    "    train_loss /= len(train_loader)\n",
    "\n",
    "    # Validation\n",
    "    model.eval()\n",
    "    val_loss = 0\n",
    "    val_preds = []\n",
    "    val_targets = []\n",
    "    val_probs = []\n",
    "    with torch.no_grad():\n",
    "        for inputs, labels in val_loader:\n",
    "            inputs, labels = inputs.to(device), labels.to(device)\n",
    "\n",
    "            outputs = model(inputs).squeeze()\n",
    "            loss = criterion(outputs, labels)\n",
    "            val_loss += loss.item()\n",
    "            val_probs.extend(outputs.cpu().numpy())\n",
    "            val_preds.extend((outputs > VAL_THRESHOLD).int().cpu().numpy())\n",
    "            val_targets.extend(labels.int().cpu().numpy())\n",
    "\n",
    "    val_loss /= len(val_loader)\n",
    "    val_accuracy = accuracy_score(val_targets,val_preds)\n",
    "    val_precision = precision_score(val_targets, val_preds, zero_division=0)\n",
    "    val_recall = recall_score(val_targets, val_preds, zero_division=0)\n",
    "    val_f1 = f1_score(val_targets, val_preds, zero_division=0)\n",
    "    val_auc = roc_auc_score(val_targets, val_probs)\n",
    "    scheduler.step()\n",
    "\n",
    "    print(f\"Epoch {epoch+1}/{N_EPOCHS}, Train Loss: {train_loss:.4f},Val Loss: {val_loss:.4f}, Accuracy: {val_accuracy:.4f}, Precision: {val_precision:.4f}, Recall: {val_recall:.4f}, F1: {val_f1:.4f}, AUC: {val_auc:.4f}\")\n",
    "\n",
    "    early_stopping(val_loss, model)\n",
    "    if early_stopping.early_stop:\n",
    "        print(\"[INFO] Early stopping triggered. Stopping training.\")\n",
    "        break\n",
    "\n",
    "torch.save(model.state_dict(), \"./models/mlp_model.pth\")\n",
    "\n",
    "# Calculate optimal threshold using ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(val_targets, val_probs)\n",
    "optimal_idx = np.argmax(tpr - fpr)\n",
    "optimal_threshold = thresholds[optimal_idx]\n",
    "print(f\"Optimal Threshold: {optimal_threshold:.4f}\")\n",
    "\n",
    "# Save ROC-AUC curve\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label=f'ROC curve (area = {val_auc:.2f}')\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig('./assets/roc_curve.png')\n",
    "print(\"[INFO] ROC-AUC Curve saved at './assets/roc_curve.png'\")\n",
    "\n",
    "# Final testing\n",
    "print(\"[INFO] Evaluating on test set with optimal threshold...\")\n",
    "test_dataset = FraudDataset(X_val, y_val)\n",
    "test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE)\n",
    "\n",
    "model.eval()\n",
    "test_probs = []\n",
    "test_targets = []\n",
    "with torch.no_grad():\n",
    "    for inputs, labels in test_loader:\n",
    "        inputs, labels = inputs.to(device), labels.to(device)\n",
    "\n",
    "        outputs = model(inputs).squeeze()\n",
    "        test_probs.extend(outputs.cpu().numpy())\n",
    "        test_targets.extend(labels.int().cpu().numpy())\n",
    "\n",
    "final_preds = (np.array(test_probs) > optimal_threshold).astype(int)\n",
    "\n",
    "\n",
    "# Metrics\n",
    "accuracy = accuracy_score(test_targets, final_preds)\n",
    "precision = precision_score(test_targets, final_preds)\n",
    "recall = recall_score(test_targets, final_preds)\n",
    "f1 = f1_score(test_targets, final_preds)\n",
    "auc = roc_auc_score(test_targets, test_probs)\n",
    "print(f\"Test Accuracy: {accuracy:.4f}, Precision: {precision:.4f}, Recall: {recall:.4f}, F1: {f1:.4f}, AUC: {auc:.4f}\")\n",
    "\n",
    "# Confusion matrix and classification report\n",
    "conf_matrix = confusion_matrix(test_targets, final_preds)\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=[\"Non-Fraud\", \"Fraud\"], yticklabels=[\"Non-Fraud\", \"Fraud\"])\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.savefig('./assets/confusion_matrix.png')\n",
    "print(\"[INFO] Confusion matrix saved at './assets/confusion_matrix.png'\")\n",
    "\n",
    "print(\"Classification Report:\\n\")\n",
    "report = classification_report(test_targets, final_preds, target_names=[\"Non-Fraud\", \"Fraud\"])\n",
    "print(\"Classification Report:\\n\")\n",
    "print(report)\n",
    "\n",
    "# Save the report to a text file\n",
    "with open(\"./assets/classification_report.txt\", \"w\") as file:\n",
    "    file.write(report)\n",
    "\n",
    "print(\"Classification report saved in assets folder\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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