task-3-project/TeamX.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DUUADTbrc4O_"
      },
      "source": [
        "#**1. Analysis of Data**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LWzbXLYUdAIL",
        "outputId": "55d7b580-3441-4354-cb44-c93fbba35cac"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: xgboost in /usr/local/lib/python3.8/dist-packages (0.90)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (from xgboost) (1.7.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from xgboost) (1.21.6)\n"
          ]
        }
      ],
      "source": [
        "!pip install xgboost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R_5CljEmCEa5",
        "outputId": "556feea5-ccf2-4018-fb23-ee9de5d3a3a0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "THP9rYzuiKGq"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "dataFrame = pd.read_csv(\"/content/drive/MyDrive/AI511-HOMELOAN-2022/train_data.csv\")\n",
        "\n",
        "dataFrame_test = pd.read_csv(\"/content/drive/MyDrive/AI511-HOMELOAN-2022/test_data.csv\")\n",
        "\n",
        "desc=pd.read_csv(\"/content/drive/MyDrive/AI511-HOMELOAN-2022/columns_description.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "MxnPlwi0jR-0",
        "outputId": "0227be97-9e68-47d5-eee8-e93097e8f19c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        SK_ID_CURR NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
              "0           450407         Cash loans           F            N   \n",
              "1           271298         Cash loans           M            Y   \n",
              "2           122238         Cash loans           M            Y   \n",
              "3           305311         Cash loans           M            N   \n",
              "4           414121         Cash loans           F            N   \n",
              "...            ...                ...         ...          ...   \n",
              "184501      439663         Cash loans           F            N   \n",
              "184502      385689    Revolving loans           F            N   \n",
              "184503      153567         Cash loans           F            N   \n",
              "184504      365526         Cash loans           F            N   \n",
              "184505      125605         Cash loans           F            N   \n",
              "\n",
              "       FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  \\\n",
              "0                    Y             1           67500.0    227520.0   \n",
              "1                    Y             1          247500.0   1882372.5   \n",
              "2                    Y             1          180000.0    101880.0   \n",
              "3                    N             0           81000.0    405000.0   \n",
              "4                    Y             0          157500.0    888840.0   \n",
              "...                ...           ...               ...         ...   \n",
              "184501               Y             1          135000.0    143910.0   \n",
              "184502               Y             0          202500.0    360000.0   \n",
              "184503               N             0          135000.0    900000.0   \n",
              "184504               Y             0          123300.0    744988.5   \n",
              "184505               Y             0           81000.0    167895.0   \n",
              "\n",
              "        AMT_ANNUITY  AMT_GOODS_PRICE  ... FLAG_DOCUMENT_19 FLAG_DOCUMENT_20  \\\n",
              "0           11065.5         180000.0  ...                0                0   \n",
              "1           65560.5        1719000.0  ...                0                0   \n",
              "2           10827.0          90000.0  ...                0                0   \n",
              "3           20677.5         405000.0  ...                0                0   \n",
              "4           29506.5         675000.0  ...                0                0   \n",
              "...             ...              ...  ...              ...              ...   \n",
              "184501      15498.0         135000.0  ...                0                0   \n",
              "184502      18000.0         360000.0  ...                0                0   \n",
              "184503      45954.0         900000.0  ...                0                0   \n",
              "184504      20484.0         744988.5  ...                0                0   \n",
              "184505      17757.0         157500.0  ...                0                0   \n",
              "\n",
              "       FLAG_DOCUMENT_21 AMT_REQ_CREDIT_BUREAU_HOUR AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
              "0                     0                        0.0                       0.0   \n",
              "1                     0                        0.0                       0.0   \n",
              "2                     0                        0.0                       0.0   \n",
              "3                     0                        0.0                       0.0   \n",
              "4                     0                        0.0                       0.0   \n",
              "...                 ...                        ...                       ...   \n",
              "184501                0                        NaN                       NaN   \n",
              "184502                0                        0.0                       0.0   \n",
              "184503                0                        NaN                       NaN   \n",
              "184504                0                        0.0                       0.0   \n",
              "184505                0                        0.0                       0.0   \n",
              "\n",
              "        AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n",
              "0                              0.0                        1.0   \n",
              "1                              0.0                        0.0   \n",
              "2                              0.0                        2.0   \n",
              "3                              0.0                        0.0   \n",
              "4                              0.0                        0.0   \n",
              "...                            ...                        ...   \n",
              "184501                         NaN                        NaN   \n",
              "184502                         0.0                        0.0   \n",
              "184503                         NaN                        NaN   \n",
              "184504                         0.0                        4.0   \n",
              "184505                         0.0                        0.0   \n",
              "\n",
              "        AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  TARGET  \n",
              "0                             0.0                         2.0       0  \n",
              "1                             1.0                         3.0       0  \n",
              "2                             0.0                         1.0       0  \n",
              "3                             0.0                         2.0       0  \n",
              "4                             0.0                         2.0       0  \n",
              "...                           ...                         ...     ...  \n",
              "184501                        NaN                         NaN       0  \n",
              "184502                        1.0                         0.0       0  \n",
              "184503                        NaN                         NaN       0  \n",
              "184504                        0.0                         2.0       0  \n",
              "184505                        0.0                         2.0       0  \n",
              "\n",
              "[184506 rows x 122 columns]"
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              "\n",
              "  <div id=\"df-efc413c9-8386-4a29-8212-6faff7eb40ad\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SK_ID_CURR</th>\n",
              "      <th>NAME_CONTRACT_TYPE</th>\n",
              "      <th>CODE_GENDER</th>\n",
              "      <th>FLAG_OWN_CAR</th>\n",
              "      <th>FLAG_OWN_REALTY</th>\n",
              "      <th>CNT_CHILDREN</th>\n",
              "      <th>AMT_INCOME_TOTAL</th>\n",
              "      <th>AMT_CREDIT</th>\n",
              "      <th>AMT_ANNUITY</th>\n",
              "      <th>AMT_GOODS_PRICE</th>\n",
              "      <th>...</th>\n",
              "      <th>FLAG_DOCUMENT_19</th>\n",
              "      <th>FLAG_DOCUMENT_20</th>\n",
              "      <th>FLAG_DOCUMENT_21</th>\n",
              "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
              "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
              "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
              "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
              "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
              "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
              "      <th>TARGET</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>450407</td>\n",
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              "      <td>67500.0</td>\n",
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              "      <td>2.0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>271298</td>\n",
              "      <td>Cash loans</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>1</td>\n",
              "      <td>247500.0</td>\n",
              "      <td>1882372.5</td>\n",
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              "      <td>1719000.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>122238</td>\n",
              "      <td>Cash loans</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>1</td>\n",
              "      <td>180000.0</td>\n",
              "      <td>101880.0</td>\n",
              "      <td>10827.0</td>\n",
              "      <td>90000.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>305311</td>\n",
              "      <td>Cash loans</td>\n",
              "      <td>M</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "      <td>81000.0</td>\n",
              "      <td>405000.0</td>\n",
              "      <td>20677.5</td>\n",
              "      <td>405000.0</td>\n",
              "      <td>...</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>414121</td>\n",
              "      <td>Cash loans</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
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              "      <td>157500.0</td>\n",
              "      <td>888840.0</td>\n",
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              "      <th>184501</th>\n",
              "      <td>439663</td>\n",
              "      <td>Cash loans</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>184502</th>\n",
              "      <td>385689</td>\n",
              "      <td>Revolving loans</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>202500.0</td>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "\n",
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              "  </div>\n",
              "  "
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          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "dataFrame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "XlBQr_n3NepW",
        "outputId": "b22f69cb-6f04-4513-c32d-7f2fcc694fad"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        SK_ID_CURR NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
              "0           367294         Cash loans           F            N   \n",
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              "3           407238         Cash loans           F            N   \n",
              "4           239910         Cash loans           F            N   \n",
              "...            ...                ...         ...          ...   \n",
              "123000      128638         Cash loans           F            Y   \n",
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              "123003      301605         Cash loans           M            N   \n",
              "123004      176833         Cash loans           M            Y   \n",
              "\n",
              "       FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  \\\n",
              "0                    Y             0          180000.0    265306.5   \n",
              "1                    Y             0          202500.0    346500.0   \n",
              "2                    N             0          360000.0    545040.0   \n",
              "3                    Y             0          135000.0    307557.0   \n",
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              "123003               Y             0          180000.0   1089000.0   \n",
              "123004               Y             0          135000.0    495882.0   \n",
              "\n",
              "        AMT_ANNUITY  AMT_GOODS_PRICE  ... FLAG_DOCUMENT_18 FLAG_DOCUMENT_19  \\\n",
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              "123003      31972.5        1089000.0  ...                0                0   \n",
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              "\n",
              "       FLAG_DOCUMENT_20 FLAG_DOCUMENT_21 AMT_REQ_CREDIT_BUREAU_HOUR  \\\n",
              "0                     0                0                        0.0   \n",
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              "        AMT_REQ_CREDIT_BUREAU_DAY  AMT_REQ_CREDIT_BUREAU_WEEK  \\\n",
              "0                             0.0                         0.0   \n",
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              "2                             0.0                         0.0   \n",
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              "4                             NaN                         NaN   \n",
              "...                           ...                         ...   \n",
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              "123002                        NaN                         NaN   \n",
              "123003                        0.0                         0.0   \n",
              "123004                        0.0                         0.0   \n",
              "\n",
              "        AMT_REQ_CREDIT_BUREAU_MON  AMT_REQ_CREDIT_BUREAU_QRT  \\\n",
              "0                             1.0                        2.0   \n",
              "1                             0.0                        0.0   \n",
              "2                             0.0                        1.0   \n",
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              "123003                        0.0                        1.0   \n",
              "123004                        0.0                        0.0   \n",
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              "3                              2.0  \n",
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              "      <th>SK_ID_CURR</th>\n",
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              "      <td>380562</td>\n",
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              "      <td>M</td>\n",
              "      <td>Y</td>\n",
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              "      <td>360000.0</td>\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d82f8d69-85b6-413e-97e4-023bcfd67b8c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d82f8d69-85b6-413e-97e4-023bcfd67b8c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "dataFrame_test"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ah4S_ndEd17d"
      },
      "source": [
        "#**2. Pre-Processing**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l8DzmDabeUiD"
      },
      "source": [
        "##**2.1 Dropping columns with more than 65% Nan values**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I-q0yUKVkpWn",
        "outputId": "04a99f8d-f266-4d55-8818-705e1bbe7e2a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "OWN_CAR_AGE 121633 65.92360140049645\n",
            "YEARS_BUILD_AVG 122757 66.53279568144126\n",
            "COMMONAREA_AVG 128971 69.90070783605952\n",
            "FLOORSMIN_AVG 125244 67.88071932620078\n",
            "LIVINGAPARTMENTS_AVG 126212 68.40536351121372\n",
            "NONLIVINGAPARTMENTS_AVG 128146 69.45356790565076\n",
            "YEARS_BUILD_MODE 122757 66.53279568144126\n",
            "COMMONAREA_MODE 128971 69.90070783605952\n",
            "FLOORSMIN_MODE 125244 67.88071932620078\n",
            "LIVINGAPARTMENTS_MODE 126212 68.40536351121372\n",
            "NONLIVINGAPARTMENTS_MODE 128146 69.45356790565076\n",
            "YEARS_BUILD_MEDI 122757 66.53279568144126\n",
            "COMMONAREA_MEDI 128971 69.90070783605952\n",
            "FLOORSMIN_MEDI 125244 67.88071932620078\n",
            "LIVINGAPARTMENTS_MEDI 126212 68.40536351121372\n",
            "NONLIVINGAPARTMENTS_MEDI 128146 69.45356790565076\n",
            "FONDKAPREMONT_MODE 126254 68.42812699858\n",
            "105\n",
            "OWN_CAR_AGE\n",
            "YEARS_BUILD_AVG\n",
            "COMMONAREA_AVG\n",
            "FLOORSMIN_AVG\n",
            "LIVINGAPARTMENTS_AVG\n",
            "NONLIVINGAPARTMENTS_AVG\n",
            "YEARS_BUILD_MODE\n",
            "COMMONAREA_MODE\n",
            "FLOORSMIN_MODE\n",
            "LIVINGAPARTMENTS_MODE\n",
            "NONLIVINGAPARTMENTS_MODE\n",
            "YEARS_BUILD_MEDI\n",
            "COMMONAREA_MEDI\n",
            "FLOORSMIN_MEDI\n",
            "LIVINGAPARTMENTS_MEDI\n",
            "NONLIVINGAPARTMENTS_MEDI\n",
            "FONDKAPREMONT_MODE\n",
            "104\n"
          ]
        }
      ],
      "source": [
        "drop_columns=[]\n",
        "\n",
        "for i in dataFrame.columns:\n",
        "  if(((dataFrame[i].isna().sum()/dataFrame.shape[0])*100) > 65):\n",
        "    print(i,dataFrame[i].isna().sum(),(dataFrame[i].isna().sum()/dataFrame.shape[0])*100)\n",
        "    drop_columns.append(i)\n",
        "    del dataFrame[i]\n",
        "\n",
        "print(dataFrame.shape[1])\n",
        "\n",
        "for i in dataFrame_test.columns:\n",
        "  if i in drop_columns:\n",
        "    print(i)\n",
        "    del dataFrame_test[i]\n",
        "\n",
        "print(dataFrame_test.shape[1])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5sfRrW33ex2N"
      },
      "source": [
        "##**2.2 Replacing Nan values which has datatype not as object with median values**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ahBNIbWdzgG4",
        "outputId": "2b283270-b8c2-4502-d627-a8e4518ad905"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NAME_TYPE_SUITE\n",
            "OCCUPATION_TYPE\n",
            "HOUSETYPE_MODE\n",
            "WALLSMATERIAL_MODE\n",
            "EMERGENCYSTATE_MODE\n",
            "NAME_TYPE_SUITE\n",
            "OCCUPATION_TYPE\n",
            "HOUSETYPE_MODE\n",
            "WALLSMATERIAL_MODE\n",
            "EMERGENCYSTATE_MODE\n"
          ]
        }
      ],
      "source": [
        "#Replacing Nan values of train data\n",
        "count=0\n",
        "for i in dataFrame.columns[dataFrame.isnull().any()]:\n",
        "  count+=1\n",
        "  if(dataFrame[i].dtypes==object):\n",
        "    print(i)\n",
        "\n",
        "#Replacing Nan values of test data\n",
        "count=0\n",
        "for i in dataFrame_test.columns[dataFrame_test.isnull().any()]:\n",
        "  count+=1\n",
        "  if(dataFrame_test[i].dtypes==object):\n",
        "    print(i)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ioh2DplQikFT",
        "outputId": "eb44c4b2-1b1a-47e0-bf02-c636210121a6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AMT_ANNUITY\n",
            "AMT_GOODS_PRICE\n",
            "NAME_TYPE_SUITE\n",
            "OCCUPATION_TYPE\n",
            "CNT_FAM_MEMBERS\n",
            "EXT_SOURCE_1\n",
            "EXT_SOURCE_2\n",
            "EXT_SOURCE_3\n",
            "APARTMENTS_AVG\n",
            "BASEMENTAREA_AVG\n",
            "YEARS_BEGINEXPLUATATION_AVG\n",
            "ELEVATORS_AVG\n",
            "ENTRANCES_AVG\n",
            "FLOORSMAX_AVG\n",
            "LANDAREA_AVG\n",
            "LIVINGAREA_AVG\n",
            "NONLIVINGAREA_AVG\n",
            "APARTMENTS_MODE\n",
            "BASEMENTAREA_MODE\n",
            "YEARS_BEGINEXPLUATATION_MODE\n",
            "ELEVATORS_MODE\n",
            "ENTRANCES_MODE\n",
            "FLOORSMAX_MODE\n",
            "LANDAREA_MODE\n",
            "LIVINGAREA_MODE\n",
            "NONLIVINGAREA_MODE\n",
            "APARTMENTS_MEDI\n",
            "BASEMENTAREA_MEDI\n",
            "YEARS_BEGINEXPLUATATION_MEDI\n",
            "ELEVATORS_MEDI\n",
            "ENTRANCES_MEDI\n",
            "FLOORSMAX_MEDI\n",
            "LANDAREA_MEDI\n",
            "LIVINGAREA_MEDI\n",
            "NONLIVINGAREA_MEDI\n",
            "HOUSETYPE_MODE\n",
            "TOTALAREA_MODE\n",
            "WALLSMATERIAL_MODE\n",
            "EMERGENCYSTATE_MODE\n",
            "OBS_30_CNT_SOCIAL_CIRCLE\n",
            "DEF_30_CNT_SOCIAL_CIRCLE\n",
            "OBS_60_CNT_SOCIAL_CIRCLE\n",
            "DEF_60_CNT_SOCIAL_CIRCLE\n",
            "DAYS_LAST_PHONE_CHANGE\n",
            "AMT_REQ_CREDIT_BUREAU_HOUR\n",
            "AMT_REQ_CREDIT_BUREAU_DAY\n",
            "AMT_REQ_CREDIT_BUREAU_WEEK\n",
            "AMT_REQ_CREDIT_BUREAU_MON\n",
            "AMT_REQ_CREDIT_BUREAU_QRT\n",
            "AMT_REQ_CREDIT_BUREAU_YEAR\n"
          ]
        }
      ],
      "source": [
        "l=[]\n",
        "for i in dataFrame.columns[dataFrame.isnull().any()]:\n",
        "  print(i)\n",
        "  l.append(i)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e7Y4lCoAbwPA",
        "outputId": "aa38a33f-a3b3-4d54-e611-cde75aff10d8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['SK_ID_CURR', 'NAME_CONTRACT_TYPE', 'CODE_GENDER', 'FLAG_OWN_CAR',\n",
              "       'FLAG_OWN_REALTY', 'CNT_CHILDREN', 'AMT_INCOME_TOTAL', 'AMT_CREDIT',\n",
              "       'AMT_ANNUITY', 'AMT_GOODS_PRICE',\n",
              "       ...\n",
              "       'FLAG_DOCUMENT_19', 'FLAG_DOCUMENT_20', 'FLAG_DOCUMENT_21',\n",
              "       'AMT_REQ_CREDIT_BUREAU_HOUR', 'AMT_REQ_CREDIT_BUREAU_DAY',\n",
              "       'AMT_REQ_CREDIT_BUREAU_WEEK', 'AMT_REQ_CREDIT_BUREAU_MON',\n",
              "       'AMT_REQ_CREDIT_BUREAU_QRT', 'AMT_REQ_CREDIT_BUREAU_YEAR', 'TARGET'],\n",
              "      dtype='object', length=105)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "dataFrame.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dLZCs8Nton-o",
        "outputId": "2396448b-b974-46bd-e19b-a06ba5544c96"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[450407 271298 122238 ... 153567 365526 125605]\n",
            "['Cash loans' 'Revolving loans']\n",
            "['F' 'M' 'XNA']\n",
            "['N' 'Y']\n",
            "['Y' 'N']\n",
            "[ 1  0  2  3  4  5 10  7  6 14 12 19  8  9]\n",
            "[  67500.   247500.   180000.  ...   58063.5   70209.  1147500. ]\n",
            "[ 227520.  1882372.5  101880.  ... 1317807.   284985.   744988.5]\n",
            "[11065.5 65560.5 10827.  ... 74128.5 56749.5  8559. ]\n",
            "[ 180000.  1719000.    90000.   405000.   675000.  1350000.   900000.\n",
            "   45000.   841500.   225000.   495000.   855000.   315000.   990000.\n",
            "  463500.   450000.   810000.    94500.  1318500.  1363500.   270000.\n",
            " 1485000.   382500.   193500.   162000.   166500.   135000.   337500.\n",
            "  657000.   423000.  1575000.   553500.   477000.   148500.   783000.\n",
            "  454500.   229500.   157500.  1354500.  1372500.  1759500.   544500.\n",
            " 2250000.  1125000.   603000.   679500.   472500.   445500.   360000.\n",
            " 1417500.   427500.   693000.   252000.   751500.  1800000.   202500.\n",
            "  297000.   328500.   922500.   112500.   283500.   238500.  1152000.\n",
            "  913500.  1003500.   468000.   171000.    67500.    81000.   189000.\n",
            "  234000.   823500.  1210500.    99000.   220500.   310500.   571500.\n",
            "  594000.   670500.   688500.   247500.   144000.   706500.   292500.\n",
            "  607500.   265500.  1687500.   697500.   436500.   765000.   733500.\n",
            " 1080000.   256500.   598500.  1170000.    58500.  1129500.   414000.\n",
            "  540000.   333000.   522000.   720000.   526500.   846000.   814500.\n",
            "  612000.   441000.   243000.  1147500.   954000.   409500.   630000.\n",
            "  279000.   864000.   945000.   801000.   378000.   904500.   486000.\n",
            "  369000.  1345500.   103500.  1102500.   738000.  1255500.  1507500.\n",
            "  742500.   216000.   661500.  1246500.   702000.   981000.   774000.\n",
            "  211500.   288000.   198000.   715500.   346500.   909000.   342000.\n",
            " 1206000.   351000.   432000.   666000.   121500.   387000.   562500.\n",
            " 1215000.   684000.   373500.  1012500.   108000.  1138500.   517500.\n",
            "  580500.   261000.   306000.   756000.   625500.   585000.   396000.\n",
            " 1161000.   531000.  1305000.    76500.   535500.   184500.   481500.\n",
            "  724500.   391500.   549000.   931500.   639000.   459000.   972000.\n",
            "  643500.   418500.   576000.   364500.   819000.  1053000.  1948500.\n",
            "  355500.   927000.  1588500.   324000.   301500.   886500.   895500.\n",
            "  274500.   787500.   207000.  2160000.  1597500.   567000.  1278000.\n",
            "  153000.  1030500.  2007000.   891000.  1035000.   850500.  1395000.\n",
            "  319500.  1260000.   400500.   126000.   949500.   729000.  1156500.\n",
            "  868500.   117000.  1008000.   918000.  1044000.   621000.  1359000.\n",
            "  747000.  1026000.   711000.  1494000.  1143000.   589500.   769500.\n",
            " 1089000.        nan 1327500.   558000.  1300500.   877500.   648000.\n",
            " 1399500.  1057500.  1273500.  2025000.   130500.   882000.  2700000.\n",
            " 1188000.   805500.   139500.   999000.  1548000.  1192500.  1039500.\n",
            " 1237500.   832500.  1656000.   936000.    49500.  1755000.   508500.\n",
            "  792000.   837000.  1093500.  2205000.   616500.  1449000.  1530000.\n",
            "  967500.   504000.   513000.  1462500.   976500.   828000.  2254500.\n",
            "  652500.   778500.  1737000.  1439248.5  607671.  1107000.  1197000.\n",
            "  175500.  1084500.  1534500.  1062000.  1048500.   634500.   796500.\n",
            "  873000.  1912500.  1219500.   852574.5 1309500.  1066500.  1075500.\n",
            "  490500.  1440000.  1296000.   618903.   760500.  1845000.  1710000.\n",
            " 1561500.  1984500.  1471500.  1123785.  1498500.    72000.   478795.5\n",
            " 1620000.   859500.  1165500.  1021500.  1282500.  1120500.    85500.\n",
            " 1017000.  1579500.   304798.5 1134000.  1773000.    54000.  3150000.\n",
            "  985500.  1386000.  1314000.  1116000.  1179000.   666090.   940500.\n",
            " 1467000.  1287000.  1251000.  1377000.  1332000.  1174500.   411417.\n",
            " 1413000.  1827000.  1606500.   963000.  1570500.   958500.  1444500.\n",
            "   63000.  1935000.  1336500.  1552500.  2070000.  1291500.   499500.\n",
            " 1998000.  1323000.  1098000.  1324962.   332910.  1435500.  1822500.\n",
            " 1431000.  1111500.  1242000.  1076436.  1714500.  1728000.  1908000.\n",
            " 1602000.  1732500.   484155.   991476.   737784.   994500.   482530.5\n",
            "  388660.5 1480500.  1489500.   205533.  1269000.  1512000.  1381500.\n",
            " 1264500.  1071000.  1980000.  1723500.  1453500.  1665000.  1341000.\n",
            " 2182500.  1368000.  1525500.  1516500.  2047500.  1894500.   383035.5\n",
            " 1422000.  3375000.   933174.  1795500.   933300.   887314.5 1233000.\n",
            "  983713.5 1696500.  1183500.  1426500.  1764000.  1503000.  1642500.\n",
            " 1831500.  1390500.  1683000.  1890000.  1476000.  1629000.  1566000.\n",
            " 1840500.  1647000.   199633.5 1224000.   450234.  1305706.5 1521000.\n",
            " 1750500.  2362500.  1638000.   264600.   430447.5 1185925.5 1813500.\n",
            "  349834.5  466083.  1746000.   685336.5 1791000.  1899000.   316444.5\n",
            " 1674000.   492547.5  424039.5 1921500.  1593000.  1078713.  1584000.\n",
            "  906642.  1061307.  1818000.  1858500.   379012.5  912802.5  853821.\n",
            " 1669500.   394038.  1201500.  1768500.  1854000.  1633500.  1804500.\n",
            "  409986.   619150.5  413442.  1611000.  1782000.   599787.   378598.5\n",
            " 2016000.   448546.5  300838.5 1705500.  2151000.   570321.   324819.\n",
            "  965794.5   98028.  1701000.  1867500.  1199308.5 4050000.  2052000.\n",
            " 1777500.   238531.5  242626.5 1624500.   202914.  1786500.   447804.\n",
            "  394101.   400887.   793057.5  415359.  1917000.   405225.  1903500.\n",
            " 1539000.   275553.  1404000.  2736000.   569097.  2137500.  1228500.\n",
            " 1660500.   647644.5 1741500.    40500.  1692000.  2155500.   137142.\n",
            "  630400.5  812983.5  162256.5  762246.   217561.5  502438.5  486085.5\n",
            " 1458000.  1152477.  2002500.   947884.5  280026.   350500.5 1557000.\n",
            "  464463.   323604.  1256103.  1993500.  1939500.   258624.   322560.\n",
            "  370219.5  372712.5  848704.5  425326.5  149719.5 1809000.  2547000.\n",
            "  705361.5 1259563.5  857538.   642915.   250398.   686763.   568908.\n",
            " 1200136.5 2106000.   337221.  1408500.  2115000.   467874.   288787.5\n",
            " 2011500.   638302.5 2092500.  1651500.   381528.   208408.5 1615500.\n",
            "  225243.  1180273.5  660244.5  234342.   159066.   264312.  2475000.\n",
            " 2038500.   855175.5  552384.   780597.  2200500.   851769.   718110.\n",
            "  736888.5  330390.   442062.  1957500.   635008.5  679981.5  420390.\n",
            "  808245.   218232.   446998.5 3555000.  1244515.5  289539.   528183.\n",
            "  872892.   969241.5  559836.  1849500.   389623.5  231043.5  140287.5\n",
            "  241024.5  277947.   435100.5  393534.   584820.   441733.5 1543500.\n",
            " 2119500.  1029838.5  584995.5  810778.5  121878.   418473.  2187000.\n",
            "  295776.  1996456.5  421213.5  303696.   458806.5  377167.5 2448000.\n",
            "  246483.   325944.   178344.   635760.  2223000.   155965.5 2218500.\n",
            " 1805922.  1252863.   955003.5 2925000.  1268743.5 3600000.   552595.5\n",
            "  456480.   852975.  2133000.   682609.5 1966500.  2385000.   244863.\n",
            "  354015.  1930500.   384772.5  894825.  1191595.5  673632.   414981.\n",
            "  211536.   504319.5  655704.   470511.   793296.   938497.5  285097.5\n",
            "  362632.5  874593.   193135.5  532624.5  675382.5  657625.5 1678500.\n",
            " 1039315.5 2065500.   410274.   159093.   379251.   245947.5  857655.\n",
            "  430672.5  766444.5 1723666.5  660937.5  232690.5  828216.   885154.5\n",
            "  459148.5  413910.   537700.5 2114100.   886770.  1177744.5 1481535.\n",
            "  404707.5  900243.   308011.5  738945.  1953000.  2214000.   312790.5\n",
            "  308475.   391009.5  885523.5  354645.   439479.   244435.5 1051632.\n",
            "  518485.5  229126.5  295677.   683023.5  645799.5  517050.   121945.5\n",
            "  378189.   241704.   585616.5 1989000.  2196000.   316372.5 2191500.\n",
            "  357133.5  466713.   461367.  2124000.   674640.   498856.5 1944000.\n",
            "  583569.  3712500.   872739.   574002.   444010.5  381411.  1458859.5\n",
            "  584842.5 2043000.   527823.   607603.5  146578.5  769909.5  653350.5\n",
            "  465925.5 1474663.5  452565.   388746.   635085.   428994.  3060000.\n",
            "  778783.5  453321.  1496893.5  868594.5  546021.   161658.   475686.\n",
            "  535774.5  900090.   505080.   507618.   668506.5 2056500.  2295000.\n",
            "  891418.5   59782.5  412227.   827100.  2745000.  1290766.5  522544.5\n",
            "  822519.   233928.   463279.5  635652.   970006.5 2110500.   782059.5\n",
            "  801270.  1639431.   281853.  2232306.  1199371.5  343840.5  694152.\n",
            "  338598.   472720.5  832878.  2236500.   455548.5  942201.   426042.\n",
            "  323982.   263965.5  734976.  1271875.5  281907.  1247643.   356598.\n",
            "  177444.   751662.   234805.5  455445.  2101500.   784579.5 1265751.\n",
            "  227988.   503559.   592452.   478917.   502893.   982435.5  141354.\n",
            "  323694.   393799.5  387675.  2272500.   353749.5 1220022.   379867.5\n",
            "  284985.   503266.5  744988.5]\n",
            "['Family' 'Unaccompanied' 'Spouse, partner' 'Children' nan 'Other_A'\n",
            " 'Other_B' 'Group of people']\n",
            "['Commercial associate' 'Working' 'Pensioner' 'State servant' 'Student'\n",
            " 'Businessman' 'Unemployed' 'Maternity leave']\n",
            "['Secondary / secondary special' 'Higher education' 'Incomplete higher'\n",
            " 'Lower secondary' 'Academic degree']\n",
            "['Married' 'Civil marriage' 'Single / not married' 'Separated' 'Widow'\n",
            " 'Unknown']\n",
            "['House / apartment' 'With parents' 'Municipal apartment'\n",
            " 'Office apartment' 'Rented apartment' 'Co-op apartment']\n",
            "[0.018634 0.01885  0.022625 0.035792 0.018801 0.018209 0.020713 0.0228\n",
            " 0.006296 0.028663 0.00823  0.032561 0.003069 0.00496  0.006207 0.020246\n",
            " 0.009175 0.003813 0.04622  0.019101 0.007305 0.025164 0.009549 0.010006\n",
            " 0.006671 0.01452  0.015221 0.002042 0.00702  0.018029 0.006008 0.030755\n",
            " 0.003122 0.008866 0.026392 0.007114 0.005084 0.016612 0.011703 0.072508\n",
            " 0.02461  0.008019 0.009657 0.031329 0.006852 0.002134 0.006233 0.006629\n",
            " 0.0105   0.007274 0.004849 0.010556 0.019689 0.014464 0.00733  0.008068\n",
            " 0.010147 0.011657 0.010966 0.008625 0.00963  0.009334 0.002506 0.006305\n",
            " 0.00712  0.005002 0.010032 0.010643 0.003541 0.003818 0.005313 0.008575\n",
            " 0.005144 0.010276 0.001276 0.008474 0.001417 0.000938 0.000533 0.001333\n",
            " 0.00029 ]\n",
            "[-17210 -18640 -14649 ...  -7684 -24330  -7866]\n",
            "[  -774    -96  -6130 ... -11125  -9802  -8924]\n",
            "[ -5580.  -5711.  -1776. ... -15018. -15653. -13987.]\n",
            "[ -755 -2175 -4568 ... -6135 -5778 -5865]\n",
            "[1 0]\n",
            "[1 0]\n",
            "[1 0]\n",
            "[1 0]\n",
            "[0 1]\n",
            "[0 1]\n",
            "['Sales staff' 'Managers' 'Laborers' nan 'Drivers' 'Core staff'\n",
            " 'Low-skill Laborers' 'Private service staff' 'High skill tech staff'\n",
            " 'Accountants' 'Security staff' 'Cleaning staff' 'Medicine staff'\n",
            " 'Secretaries' 'Cooking staff' 'Realty agents' 'Waiters/barmen staff'\n",
            " 'HR staff' 'IT staff']\n",
            "[ 3.  1.  2.  4.  5.  6.  7. 12.  9.  8. 16. 14. 20. 10. 11. nan]\n",
            "[2 3 1]\n",
            "[2 3 1]\n",
            "['WEDNESDAY' 'TUESDAY' 'MONDAY' 'SUNDAY' 'SATURDAY' 'FRIDAY' 'THURSDAY']\n",
            "[12 10 14 18  9 11  8 17 16 13  7 19 15  6  5  4  3 20 21 23  1  2 22  0]\n",
            "[0 1]\n",
            "[0 1]\n",
            "[0 1]\n",
            "[0 1]\n",
            "[0 1]\n",
            "[0 1]\n",
            "['Business Entity Type 3' 'Transport: type 4' 'Other' 'Self-employed'\n",
            " 'XNA' 'Trade: type 7' 'Kindergarten' 'Transport: type 2' 'Restaurant'\n",
            " 'Insurance' 'Government' 'Medicine' 'Bank' 'Industry: type 3' 'Military'\n",
            " 'Security' 'Realtor' 'Industry: type 9' 'Culture'\n",
            " 'Business Entity Type 2' 'Legal Services' 'Emergency' 'Postal'\n",
            " 'Business Entity Type 1' 'Electricity' 'Housing' 'Trade: type 3' 'School'\n",
            " 'Agriculture' 'Hotel' 'University' 'Construction' 'Police'\n",
            " 'Industry: type 7' 'Security Ministries' 'Transport: type 3' 'Services'\n",
            " 'Trade: type 2' 'Trade: type 6' 'Industry: type 11' 'Cleaning'\n",
            " 'Industry: type 2' 'Industry: type 5' 'Religion' 'Trade: type 1' 'Mobile'\n",
            " 'Industry: type 1' 'Industry: type 12' 'Industry: type 4'\n",
            " 'Transport: type 1' 'Advertising' 'Telecom' 'Trade: type 5'\n",
            " 'Industry: type 10' 'Industry: type 6' 'Trade: type 4'\n",
            " 'Industry: type 13' 'Industry: type 8']\n",
            "[       nan 0.58106429 0.47651144 ... 0.40739621 0.32527043 0.56920014]\n",
            "[0.2643538  0.17225106 0.63621804 ... 0.22960586 0.38221931 0.65497724]\n",
            "[           nan 6.86382335e-01 5.40654450e-01 3.37672722e-01\n",
            " 8.19317692e-01 8.06149281e-01 4.92060094e-01 6.61023539e-01\n",
            " 7.03203305e-01 1.00803706e-01 7.92264474e-01 3.63945239e-01\n",
            " 2.60855914e-01 4.34733249e-01 5.91976618e-01 3.96219572e-01\n",
            " 6.04112600e-01 3.17031779e-01 6.21226338e-01 4.50747282e-01\n",
            " 4.32961667e-01 5.81483706e-01 7.62335618e-01 3.53987608e-01\n",
            " 3.00107757e-01 3.75711010e-01 5.01075160e-01 6.51260219e-01\n",
            " 2.44516392e-01 3.84206813e-01 4.79448981e-01 2.62248971e-01\n",
            " 1.96333966e-01 2.04422625e-01 6.22922000e-01 5.29889834e-01\n",
            " 7.38019620e-01 6.59405532e-01 3.39287700e-01 3.57293268e-01\n",
            " 2.91097380e-01 5.62060483e-01 3.79100485e-01 5.79727423e-01\n",
            " 3.65616507e-01 7.53067392e-01 3.45784802e-01 6.47976860e-01\n",
            " 7.13631400e-01 4.25892898e-01 5.42445144e-01 2.52598698e-01\n",
            " 5.76208836e-01 7.16570245e-01 7.12155155e-01 6.24614658e-01\n",
            " 3.97946322e-01 3.32850564e-01 8.01600903e-01 2.75000352e-01\n",
            " 5.13693766e-01 2.58084204e-01 3.06202298e-01 6.78567689e-01\n",
            " 6.62637792e-01 4.68659655e-01 4.29423684e-01 7.46300213e-01\n",
            " 4.11848556e-01 6.33031641e-01 6.41368257e-01 6.34705531e-01\n",
            " 6.84827659e-01 7.75155267e-01 6.49620311e-01 6.89479143e-01\n",
            " 2.13966852e-01 3.09687983e-02 6.02386344e-01 5.84990040e-01\n",
            " 5.46023197e-01 2.39226279e-01 7.82607837e-01 2.34015167e-01\n",
            " 4.84851475e-01 4.03141677e-01 5.54946769e-01 9.69483171e-02\n",
            " 7.68807573e-01 3.91054977e-01 7.78904039e-01 5.15495375e-01\n",
            " 5.93717587e-01 1.06841948e-01 4.93862882e-01 7.95803133e-01\n",
            " 7.43559314e-01 6.05836265e-01 3.58951229e-01 5.63835049e-01\n",
            " 6.52896552e-01 4.04878364e-01 1.34265424e-01 1.47345918e-01\n",
            " 7.19490785e-01 3.04672184e-01 6.07557300e-01 7.01695774e-01\n",
            " 6.65854922e-01 7.06205110e-01 5.24496446e-01 5.95456203e-01\n",
            " 7.23836990e-01 5.28092751e-01 7.35220999e-01 3.21735282e-01\n",
            " 7.18032811e-01 7.85052026e-01 1.50085176e-01 5.56727426e-01\n",
            " 5.20897599e-01 3.15472155e-01 3.60612566e-01 4.75849909e-01\n",
            " 6.19527708e-01 6.94092643e-01 4.74051289e-01 3.07736696e-01\n",
            " 5.08286991e-01 7.58393062e-01 2.77885689e-01 6.14414378e-01\n",
            " 1.15386662e-01 3.44155007e-01 5.53164699e-01 7.36622698e-01\n",
            " 4.57899551e-01 4.99272015e-01 2.41861487e-01 2.40541417e-01\n",
            " 4.43615308e-01 5.26294940e-01 3.99675616e-01 5.11891802e-01\n",
            " 7.15103102e-01 2.73564678e-01 1.71468367e-01 1.33429254e-01\n",
            " 5.72682505e-01 4.54321060e-01 2.94083101e-01 1.85202082e-01\n",
            " 5.22697317e-01 3.29655054e-01 7.33814537e-01 6.57783800e-01\n",
            " 4.65069215e-01 4.90257512e-01 5.97192427e-01 5.16817674e-02\n",
            " 4.38281374e-01 4.15347145e-01 4.83050188e-01 6.75413291e-01\n",
            " 8.05019662e-01 4.77649155e-01 5.67379237e-01 2.11550764e-01\n",
            " 1.81959110e-01 4.63275328e-01 1.66405546e-01 2.21335206e-01\n",
            " 5.83237926e-01 4.08358853e-01 2.97086612e-01 7.89880347e-01\n",
            " 4.10102573e-01 1.09626434e-01 6.72242890e-01 4.86653157e-01\n",
            " 4.95665829e-01 6.16121691e-01 6.73830078e-01 1.62441998e-01\n",
            " 2.48535557e-01 1.64414179e-01 4.72253343e-01 6.26304277e-01\n",
            " 1.16867266e-01 1.13196353e-01 7.73895694e-01 2.88129600e-01\n",
            " 7.28141299e-01 7.61026370e-01 2.66456808e-01 6.43025564e-01\n",
            " 5.19097338e-01 7.25276435e-01 6.98667555e-01 7.20944150e-01\n",
            " 6.81705878e-01 5.86740009e-01 7.07699345e-01 4.48962273e-01\n",
            " 5.51381262e-01 6.00657537e-01 8.32785025e-01 1.20641030e-01\n",
            " 6.83268831e-01 8.73042707e-02 2.82248434e-01 3.18595524e-01\n",
            " 3.68968738e-01 3.80799683e-01 6.54529280e-01 1.32597495e-01\n",
            " 5.90233339e-01 3.20163367e-01 4.24130311e-01 3.55638717e-01\n",
            " 6.17826147e-01 7.54406173e-01 1.26100559e-01 8.59053129e-01\n",
            " 8.63363382e-01 4.31191798e-01 3.10818254e-01 5.10089528e-01\n",
            " 8.12822607e-01 4.01407414e-01 7.57069015e-01 3.48677564e-02\n",
            " 6.12704244e-01 4.45396242e-01 2.98594990e-01 7.22392890e-01\n",
            " 6.27990819e-01 4.20610964e-01 5.37069958e-01 3.23311245e-01\n",
            " 1.75605979e-01 7.40799088e-01 2.35310517e-01 7.50375150e-01\n",
            " 3.92773861e-01 6.42370375e-02 2.47190938e-01 3.50695843e-01\n",
            " 5.74446617e-01 5.77969119e-01 6.69056695e-01 6.46329898e-01\n",
            " 2.25086862e-01 3.94495405e-01 7.59712182e-01 1.91821602e-01\n",
            " 2.23831317e-01 3.13916677e-01 3.87625344e-01 6.70651753e-01\n",
            " 6.97146908e-01 1.49167461e-01 3.89338792e-01 5.70916542e-01\n",
            " 3.28063161e-01 3.85914672e-01 9.82185953e-02 7.83832434e-01\n",
            " 4.41835823e-01 4.40057830e-01 6.64248263e-01 6.56158373e-01\n",
            " 5.60284328e-01 4.27657370e-01 2.07964174e-01 7.09189110e-01\n",
            " 2.67868936e-01 7.04706423e-01 1.40243184e-01 7.94628583e-01\n",
            " 8.52828467e-01 5.65607981e-01 4.97468889e-01 6.91021227e-01\n",
            " 3.49055251e-01 1.02782014e-01 2.69285800e-01 2.05598139e-01\n",
            " 2.72133684e-01 6.31354537e-01 5.58506628e-01 3.72333666e-01\n",
            " 7.99296783e-01 7.00183851e-01 3.77404249e-01 7.42181612e-01\n",
            " 3.70649632e-01 6.10991328e-01 4.61482391e-01 7.49592809e-02\n",
            " 4.81249341e-01 6.38043528e-01 7.67523105e-01 7.93449030e-01\n",
            " 1.51007535e-01 8.10618022e-01 8.21443313e-01 5.31686143e-01\n",
            " 5.33481630e-01 5.47810466e-01 1.19119065e-01 5.04681319e-01\n",
            " 7.71361592e-01 2.36610824e-01 6.67457742e-01 5.38862707e-01\n",
            " 1.77704072e-01 2.12756305e-01 8.24594971e-01 3.40906426e-01\n",
            " 1.43758053e-01 8.03885061e-01 1.86292950e-01 4.36506499e-01\n",
            " 1.26897526e-01 4.18854289e-01 5.35276250e-01 2.28883417e-01\n",
            " 2.09154699e-01 1.45542813e-01 2.49885063e-01 3.01624892e-01\n",
            " 5.17296581e-01 2.70707387e-01 2.32724776e-01 6.29674251e-01\n",
            " 3.09275356e-01 3.42528872e-01 8.37746853e-01 7.51723715e-01\n",
            " 4.70456116e-01 7.39411754e-01 3.03146374e-01 8.07273923e-01\n",
            " 1.59519540e-01 6.09275667e-01 1.57594999e-01 2.86652483e-01\n",
            " 7.80143638e-01 1.16124914e-01 8.25635745e-01 1.90705948e-01\n",
            " 9.56927242e-02 1.69428727e-01 7.29566691e-01 1.13922396e-01\n",
            " 1.24519471e-01 4.56109739e-01 7.70087070e-01 7.10674386e-01\n",
            " 5.69148771e-01 6.12681138e-02 1.48254379e-01 6.80138822e-01\n",
            " 8.27702668e-01 4.17099668e-01 4.85913395e-02 8.16092360e-01\n",
            " 1.76652579e-01 5.44234741e-01 2.50944406e-02 2.51239446e-01\n",
            " 7.63639921e-01 8.45437922e-01 2.89611238e-01 5.53331995e-02\n",
            " 5.98926218e-01 7.49021705e-01 1.72495467e-01 8.34784159e-01\n",
            " 9.88592804e-02 1.95198406e-01 4.66864006e-01 2.59467655e-01\n",
            " 5.06484240e-01 8.33786999e-01 7.66233670e-01 1.78760465e-01\n",
            " 6.46719559e-02 5.23933458e-02 8.96306691e-02 1.37654462e-01\n",
            " 1.31770133e-01 2.18859082e-01 2.79335321e-01 4.59690450e-01\n",
            " 3.26475210e-01 2.53962809e-01 8.13917470e-01 1.56639827e-01\n",
            " 7.26711209e-01 7.44606879e-02 6.76992503e-01 8.43543539e-01\n",
            " 6.77939106e-02 4.22369652e-01 4.13596760e-01 1.02118788e-01\n",
            " 7.86266615e-01 9.38365970e-02 8.33641977e-02 7.88680775e-01\n",
            " 1.07532176e-01 2.85179905e-01 8.22498762e-01 3.34454126e-01\n",
            " 4.06617437e-01 5.27265239e-04 7.72631135e-01 2.31439351e-01\n",
            " 5.38443535e-02 8.65895915e-01 4.52533559e-01 2.63646813e-01\n",
            " 1.52866229e-01 8.00451340e-01 5.49596502e-01 6.92559067e-01\n",
            " 3.74020803e-01 1.39375780e-01 1.94067828e-01 2.16402961e-01\n",
            " 7.90596960e-02 4.47178578e-01 3.31250865e-01 2.22580765e-01\n",
            " 8.35776516e-01 2.76440695e-01 5.88487788e-01 5.02878277e-01\n",
            " 6.55500263e-02 2.15182404e-01 1.99770570e-01 6.95621930e-01\n",
            " 7.55740050e-01 2.95582642e-01 6.39707568e-01 3.82501804e-01\n",
            " 4.88455184e-01 2.20094645e-01 7.98137231e-01 7.76409851e-01\n",
            " 2.80789574e-01 1.23735322e-01 1.36800522e-01 3.62277247e-01\n",
            " 2.83711883e-01 6.44679455e-01 1.25307878e-01 1.35951044e-01\n",
            " 5.64751797e-02 9.50703958e-02 1.42872523e-01 3.67291018e-01\n",
            " 1.38512877e-01 7.44932185e-01 2.56705572e-01 8.53731432e-01\n",
            " 2.02086602e-01 3.12365369e-01 6.04436913e-02 7.77659443e-01\n",
            " 1.04119916e-01 1.19877961e-01 2.00926088e-01 2.10350229e-01\n",
            " 8.17172399e-01 2.26347396e-01 1.67408447e-01 1.60488931e-01\n",
            " 4.89258668e-02 1.46442067e-01 7.30987370e-01 1.84116156e-01\n",
            " 6.87932838e-01 1.97474512e-01 3.27480398e-02 7.91074913e-01\n",
            " 1.73527430e-01 7.05805188e-02 2.06778654e-01 2.30158897e-01\n",
            " 1.68416171e-01 8.02745476e-01 8.23549312e-01 8.55523587e-01\n",
            " 1.05473186e-01 6.36376171e-01 1.98620045e-01 8.42589277e-01\n",
            " 3.24891229e-01 2.45851214e-01 7.96972682e-01 6.51096140e-02\n",
            " 6.64391717e-02 8.11722793e-01 2.37916080e-01 8.62510303e-01\n",
            " 2.55331771e-01 7.47663390e-01 1.21408286e-01 7.87476198e-01\n",
            " 7.80157665e-02 8.31778230e-01 1.53802586e-01 1.51934549e-01\n",
            " 8.01163805e-02 1.41115100e-01 7.32403323e-01 1.01459357e-01\n",
            " 8.46378063e-01 1.28504377e-01 8.61616624e-02 8.84605647e-02\n",
            " 8.08393591e-01 4.41303159e-02 1.08924404e-01 3.36061521e-01\n",
            " 8.40666560e-01 3.86975129e-02 3.52339973e-01 1.03449052e-01\n",
            " 2.00259511e-02 1.55689375e-01 1.87388765e-01 9.08147090e-02\n",
            " 1.74564266e-01 3.47418227e-01 5.88259005e-02 1.08226323e-01\n",
            " 8.66731234e-01 1.22179744e-01 5.96296575e-02 2.65049430e-01\n",
            " 7.39651461e-02 1.89595260e-01 9.20128094e-02 2.03252114e-01\n",
            " 8.50326149e-02 2.43186480e-01 1.12474350e-01 9.63186928e-02\n",
            " 5.42129844e-02 7.15326763e-02 8.48244300e-01 2.92588007e-01\n",
            " 5.20364071e-02 2.17628520e-01 9.14119938e-02 3.16247736e-02\n",
            " 8.29750142e-01 1.00151820e-01 1.79821765e-01 9.32250954e-02\n",
            " 1.30947153e-01 1.88489534e-01 1.61463080e-01 6.59932074e-02\n",
            " 1.22955418e-01 1.04794623e-01 1.63425695e-01 2.27612915e-01\n",
            " 3.92377151e-02 1.30128543e-01 1.11042402e-01 1.92942228e-01\n",
            " 8.80268480e-01 5.34780874e-02 8.11859432e-02 7.59656813e-02\n",
            " 8.39170037e-02 7.01088438e-02 7.64735192e-02 8.20382959e-01\n",
            " 8.59924176e-01 3.25203451e-02 1.65407460e-01 8.26671642e-01\n",
            " 8.18247508e-01 1.70446123e-01 6.68879340e-02 4.92625778e-02\n",
            " 1.80887978e-01 6.33753623e-02 1.41991543e-01 4.63097465e-02\n",
            " 6.82511545e-02 1.58554900e-01 2.40571964e-02 4.96014853e-02\n",
            " 7.95864362e-02 1.54743631e-01 9.75816127e-02 8.90438827e-02\n",
            " 3.68624823e-02 7.29831557e-02 5.27525967e-02 8.39698085e-01\n",
            " 5.09793334e-02 8.55954513e-02 1.10332428e-01 1.14652494e-01\n",
            " 5.06315131e-02 8.09508289e-01 1.18364326e-01 1.35106016e-01\n",
            " 7.81378238e-01 3.56032843e-02 9.44516920e-02 4.76007352e-02\n",
            " 7.64939274e-01 7.20131886e-02 6.91742267e-02 6.73395087e-02\n",
            " 1.06155623e-01 2.80779003e-02 8.17255925e-02 6.08545970e-02\n",
            " 8.57297454e-01 1.44648146e-01 5.76392903e-02 4.66293360e-02\n",
            " 7.69844691e-02 8.30766606e-01 8.28147042e-02 1.83035167e-01\n",
            " 8.67312636e-02 1.11756371e-01 7.85361446e-02 8.78806992e-02\n",
            " 9.95036835e-02 5.49573941e-02 3.89667159e-02 4.69510224e-02\n",
            " 1.29314289e-01 6.16842561e-02 4.38270669e-02 4.00616000e-02\n",
            " 3.66072628e-02 3.73780642e-02 4.29292066e-02 6.29485767e-02\n",
            " 3.71194126e-02 7.34726404e-02 7.74985465e-02 7.24966768e-02\n",
            " 8.22685076e-02 8.41630287e-01 5.45839935e-02 3.03260035e-02\n",
            " 8.36764077e-01 7.10551247e-02 6.00353829e-02 5.31141728e-02\n",
            " 8.56412800e-01 5.13294139e-02 4.72748180e-02 1.27698794e-01\n",
            " 8.74844253e-01 8.28728832e-01 4.06200396e-02 3.34404021e-02\n",
            " 3.53564629e-02 9.26171714e-02 5.72487718e-02 5.80323088e-02\n",
            " 8.77194258e-01 9.02209393e-02 2.32248707e-02 5.57114233e-02\n",
            " 6.21030378e-02 6.25244733e-02 8.64212002e-01 8.85488394e-01\n",
            " 5.84278409e-02 1.17613732e-01 5.60920788e-02 8.50091881e-01\n",
            " 4.14716966e-02 8.38724852e-01 4.11859296e-02 8.65056176e-01\n",
            " 4.50521147e-02 4.26338431e-02 3.18464044e-02 7.54609403e-02\n",
            " 5.68607395e-02 3.32080587e-02 3.22941806e-02 2.88760241e-02\n",
            " 4.17593601e-02 8.61652750e-01 8.15007386e-01 1.94675368e-02\n",
            " 4.56768096e-02 6.38048445e-02 2.37208308e-02 2.58100107e-02\n",
            " 2.84742479e-02 2.61752069e-02 3.29772745e-02 1.60781171e-02\n",
            " 6.96400847e-02 4.99426019e-02 2.19512925e-02 8.47313519e-01\n",
            " 3.46258510e-02 4.23404218e-02 5.92265014e-02 4.03398941e-02\n",
            " 8.69210801e-01 3.01146246e-02 8.44731375e-02 4.32265239e-02\n",
            " 4.53634379e-02 8.58177559e-01 8.06495443e-02 8.82530313e-01\n",
            " 8.78739767e-01 4.59922418e-02 3.95105215e-02 2.52715002e-02\n",
            " 2.43982125e-02 4.82589835e-02 4.79287862e-02 4.20489313e-02\n",
            " 3.05388193e-02 6.87112549e-02 3.14046353e-02 2.63596859e-02\n",
            " 1.90587694e-02 3.97851461e-02 3.81644526e-02 5.02859402e-02\n",
            " 8.87664202e-01 3.84300954e-02 2.94890196e-02 3.76384476e-02\n",
            " 1.75062455e-02 1.41482655e-02 4.47428279e-02 2.54497765e-02\n",
            " 2.99046737e-02 8.51008703e-01 3.51112903e-02 3.07530812e-02\n",
            " 1.45563649e-02 8.70841988e-01 1.82663270e-02 1.67772514e-02\n",
            " 8.71651065e-01 4.44355656e-02 2.65454297e-02 3.11859799e-02\n",
            " 3.43855641e-02 2.67324463e-02 2.35543731e-02 3.79005735e-02\n",
            " 3.41468857e-02 8.72455816e-01 4.35258068e-02 3.63537437e-02\n",
            " 2.04550299e-02 4.09020477e-02 8.81026575e-01 8.54629801e-01\n",
            " 2.96961417e-02 8.93976075e-01 3.61019144e-02 1.81374345e-02\n",
            " 2.27390386e-02 3.39098058e-02 8.73256255e-01 2.03110127e-02\n",
            " 2.86744531e-02 2.73012161e-02 2.01679886e-02 3.58517647e-02\n",
            " 3.20695369e-02 2.82753998e-02 2.30618111e-02]\n",
            "[0.0124    nan 0.1979 ... 0.7206 0.1146 0.0882]\n",
            "[   nan 0.1169 0.1886 ... 0.5403 0.709  0.4426]\n",
            "[0.9444    nan 0.9841 0.9826 0.9762 0.9851 0.9801 0.9836 0.9891 0.9846\n",
            " 0.9985 0.9781 0.997  0.9856 0.9767 0.9816 0.9791 0.9831 0.9796 0.9722\n",
            " 0.9657 0.9935 0.998  0.9687 0.9786 0.9821 0.9866 0.9776 0.994  0.9911\n",
            " 0.9692 0.9811 0.9737 0.9965 0.9707 0.995  0.9771 0.9871 0.9747 0.9742\n",
            " 0.9921 0.9876 0.9995 0.9732 0.9757 0.9752 0.9697 0.9906 0.9886 0.9677\n",
            " 0.9861 0.999  0.9881 0.9806 0.9896 0.9901 0.9617 0.9702 0.9613 0.9429\n",
            " 0.9945 0.9404 0.9717 0.993  0.9662 0.     0.9603 0.9727 0.9712 0.9518\n",
            " 0.9916 0.9667 0.996  0.9955 0.9459 0.6513 0.4923 0.9925 0.9622 0.9588\n",
            " 0.9598 0.9642 1.     0.9975 0.545  0.6533 0.9652 0.9608 0.9215 0.9578\n",
            " 0.9473 0.9583 0.9627 0.9493 0.9672 0.9468 0.9488 0.9513 0.9275 0.9394\n",
            " 0.4983 0.9593 0.9573 0.9647 0.9334 0.9563 0.9508 0.9424 0.9682 0.9632\n",
            " 0.9434 0.9568 0.9374 0.9523 0.9339 0.918  0.916  0.4903 0.9409 0.4878\n",
            " 0.9414 0.9379 0.9344 0.4848 0.9558 0.9483 0.6538 0.9305 0.9463 0.9091\n",
            " 0.9533 0.9389 0.9637 0.4898 0.9101 0.9449 0.9498 0.9478 0.9399 0.9503\n",
            " 0.9235 0.7442 0.9285 0.9324 0.928  0.9528 0.8927 0.923  0.9185 0.9384\n",
            " 0.4993 0.8276 0.9071 0.5941 0.9349 0.9121 0.9454 0.3164 0.6567 0.4873\n",
            " 0.9419 0.4978 0.9548 0.929  0.3169 0.6841 0.9329 0.9553 0.4918 0.926\n",
            " 0.9295 0.9225 0.4913 0.7829 0.4759 0.4943 0.93   0.9314 0.9364 0.4883\n",
            " 0.9543 0.9354 0.9439 0.7432 0.4779 0.0969 0.8922 0.3259 0.841  0.9001\n",
            " 0.9096 0.9066 0.924  0.9245 0.9538 0.9319 0.0447 0.9136 0.4953 0.919\n",
            " 0.4973 0.6344 0.4908 0.9195 0.3304 0.7437 0.9309 0.92   0.9151 0.4863\n",
            " 0.4853 0.4948 0.8833 0.7914 0.1987 0.4893 0.9359 0.9116 0.9175 0.6508\n",
            " 0.9086 0.4784 0.4933 0.1942 0.9205 0.229  0.8982 0.921  0.9369 0.3294\n",
            " 0.922  0.4789 0.6572 0.0179 0.8992 0.8977 0.917  0.9056 0.2156 0.8823\n",
            " 0.5852 0.9255 0.7809 0.8579 0.8967 0.3224 0.925  0.4938 0.8674 0.2434\n",
            " 0.4774 0.6547 0.8987 0.0979 0.9006 0.4888 0.8718 0.8629 0.4958 0.9265]\n",
            "[0.        nan 0.16   0.28   0.24   0.32   0.08   0.04   0.12   0.2\n",
            " 0.4    0.44   0.48   0.36   0.032  0.1    0.14   0.2512 1.     0.2264\n",
            " 0.1332 0.22   0.6    0.06   0.72   0.68   0.304  0.096  0.0968 0.02\n",
            " 0.56   0.2664 0.8    0.1064 0.2532 0.52   0.1648 0.0864 0.09   0.64\n",
            " 0.3    0.18   0.96   0.38   0.016  0.088  0.0664 0.0532 0.1864 0.1732\n",
            " 0.336  0.0932 0.5332 0.5    0.26   0.1464 0.0132 0.42   0.2064 0.3464\n",
            " 0.272  0.106  0.072  0.0264 0.03   0.07   0.01   0.11   0.224  0.056\n",
            " 0.15   0.112  0.1212 0.2932 0.3732 0.092  0.05   0.2132 0.064  0.0916\n",
            " 0.1884 0.4264 0.0088 0.1856 0.132  0.34   0.19   0.104  0.4532 0.1264\n",
            " 0.84   0.0308 0.074  0.88   0.74   0.1368 0.17   0.024  0.1376 0.0844\n",
            " 0.0464 0.0248 0.1532 0.108  0.0148 0.384  0.128  0.164  0.144  0.58\n",
            " 0.136  0.46   0.76   0.39   0.1736 0.0752 0.33   0.152  0.176  0.1644\n",
            " 0.23   0.192  0.036  0.92   0.0048 0.13   0.0288 0.5864 0.45   0.432\n",
            " 0.0512 0.0216 0.018  0.21   0.0188 0.168  0.208  0.0856 0.0456 0.54\n",
            " 0.1132 0.0828 0.3564 0.0352 0.1148 0.66   0.25   0.248  0.78   0.0652\n",
            " 0.0472 0.288  0.2548 0.62   0.29   0.0376 0.2168 0.3332 0.808  0.0168\n",
            " 0.0468 0.1428 0.0112 0.252  0.0912 0.352  0.1828 0.0684 0.2056 0.3864\n",
            " 0.296  0.048  0.008  0.0284 0.3064 0.1524 0.034  0.9    0.1048 0.1028\n",
            " 0.2144 0.2248 0.124  0.0848 0.232  0.1484 0.1432 0.1776 0.0152 0.086\n",
            " 0.2068 0.0736 0.0576 0.1244 0.0336 0.256  0.59   0.35   0.216  0.194\n",
            " 0.1416 0.1384 0.0144 0.392  0.2284 0.0708 0.0232 0.2304 0.172  0.3108\n",
            " 0.184  0.0332 0.154  0.0172 0.1748 0.0528 0.0064 0.448  0.1012 0.046\n",
            " 0.9332 0.062  0.344  0.012  0.0176 0.0892 0.1016 0.2088 0.312  0.0732\n",
            " 0.0252 0.0872 0.0888 0.0568 0.052  0.264  0.002 ]\n",
            "[0.0345    nan 0.069  0.2414 0.2069 0.2759 0.1379 0.1034 0.1724 0.1207\n",
            " 0.     0.4828 0.3448 0.3793 0.4138 0.3103 0.1241 0.1262 0.1552 0.1083\n",
            " 0.9655 0.1148 0.0862 0.1897 0.0517 0.8276 0.5172 0.5862 0.1666 0.5517\n",
            " 0.1293 0.6897 0.131  0.0414 0.0834 0.2241 0.1638 0.2183 0.1983 0.1952\n",
            " 0.0917 0.4483 0.6207 0.0728 0.1321 0.1493 0.2586 0.0572 0.0552 0.1517\n",
            " 0.0621 0.1838 0.2986 0.1793 0.0459 0.0803 0.1172 0.1448 0.109  0.7241\n",
            " 0.1931 0.1586 0.1466 0.4021 1.     0.1121 0.3621 0.3276 0.0966 0.2872\n",
            " 0.7931 0.2345 0.1103 0.1655 0.2528 0.2207 0.0776 0.0603 0.0914 0.0948\n",
            " 0.431  0.1862 0.1045 0.2297 0.1607 0.8621 0.1348 0.201  0.0828 0.0897\n",
            " 0.7586 0.6552 0.0714 0.1576 0.25   0.1338 0.1328 0.16   0.0762 0.1069\n",
            " 0.2155 0.2931 0.1772 0.1762 0.0372 0.1179 0.0886 0.2845 0.0745 0.1593\n",
            " 0.2452 0.0483 0.1417 0.1483 0.1359 0.3217 0.331  0.1131 0.181  0.0976\n",
            " 0.1414 0.931  0.0628 0.1434 0.0431 0.2276 0.2328 0.0638 0.2552 0.0659\n",
            " 0.1821 0.061  0.0786 0.0759 0.0383 0.8966 0.2483 0.0407 0.111  0.2621\n",
            " 0.0934 0.1455 0.1128 0.0852 0.1352 0.0766 0.1559 0.2641 0.0993 0.1507\n",
            " 0.2    0.1624 0.4655 0.0369 0.0172 0.0793 0.0997 0.0676 0.0559 0.569\n",
            " 0.1786 0.2145 0.0469 0.1869 0.1072 0.0448 0.1938 0.1231 0.1921 0.2672\n",
            " 0.0817 0.2166 0.1345 0.2897 0.1569 0.27   0.1248 0.2828 0.1086 0.0893\n",
            " 0.1476 0.0738 0.1969 0.3331 0.1217 0.3017 0.1283 0.0631 0.2138 0.1314\n",
            " 0.1162 0.1052 0.1597 0.3676 0.0645 0.3707 0.099  0.1279 0.091  0.3966\n",
            " 0.1076 0.1234 0.0983 0.0979 0.0783 0.2017 0.1276 0.1014 0.0952 0.0497\n",
            " 0.1524 0.0534 0.0579 0.0541 0.11   0.089  0.0969 0.0869 0.1355 0.0814\n",
            " 0.0938 0.0276 0.0752 0.5    0.049  0.141  0.3034 0.4741 0.1341 0.2455\n",
            " 0.4597 0.0055 0.15   0.1334 0.059  0.09   0.1428 0.0841 0.0755 0.2966\n",
            " 0.1672 0.0931 0.1272 0.1228 0.1421 0.1059 0.1386 0.1255 0.0879 0.0114\n",
            " 0.0393 0.1679 0.04   0.0848 0.0903 0.1166 0.05   0.1886 0.1186 0.0321\n",
            " 0.0641 0.13   0.1452 0.1224]\n",
            "[0.0417    nan 0.4583 0.3333 0.1667 0.625  0.375  0.     0.6667 0.2708\n",
            " 0.125  0.0208 0.0833 0.2917 0.175  0.1108 0.25   0.7917 0.8192 0.5175\n",
            " 0.2083 0.4858 0.3608 0.3262 0.5    0.3542 0.5833 0.75   0.1042 0.0625\n",
            " 0.34   0.1458 0.5417 0.4167 0.5521 0.2221 0.2292 0.4792 0.3383 0.6042\n",
            " 0.5275 0.0938 0.1596 0.6108 0.4721 0.1875 0.4083 0.7083 0.6458 0.2333\n",
            " 0.4917 0.3471 0.2358 0.9583 0.1333 0.3054 0.1917 0.6625 0.3888 0.1733\n",
            " 0.1488 1.     0.4442 0.7775 0.4304 0.4375 0.6146 0.5208 0.2775 0.1525\n",
            " 0.7708 0.5692 0.3958 0.3667 0.3192 0.4333 0.3438 0.875  0.1804 0.4929\n",
            " 0.3083 0.2417 0.3417 0.1388 0.5138 0.5104 0.3646 0.3583 0.7812 0.3404\n",
            " 0.0483 0.2638 0.6875 0.15   0.2242 0.7637 0.1942 0.5625 0.3167 0.1562\n",
            " 0.3696 0.4104 0.8125 0.2033 0.4642 0.225  0.3125 0.2583 0.2667 0.06\n",
            " 0.1583 0.6771 0.4479 0.2604 0.0667 0.1188 0.2188 0.4292 0.3    0.4025\n",
            " 0.0275 0.0521 0.1967 0.9167 0.3687 0.5971 0.1625 0.4271 0.3229 0.0583\n",
            " 0.4233 0.0779 0.095  0.5917 0.2    0.1354 0.3425 0.2683 0.0692 0.1083\n",
            " 0.3571 0.3529 0.1617 0.6804 0.2396 0.0971 0.1146 0.1133 0.2429 0.35\n",
            " 0.8021 0.0138 0.2812 0.6525 0.4062 0.0875 0.09   0.6725 0.5196 0.2142\n",
            " 0.37   0.2833 0.215  0.1    0.8992 0.3917 0.3029 0.2738 0.1687 0.3833\n",
            " 0.6417 0.4833 0.8958 0.5792 0.105  0.2212 0.1308 0.2575 0.3483 0.2308\n",
            " 0.3854 0.2446 0.84   0.6354 0.1979 0.0729 0.4896 0.5863 0.325  0.4667\n",
            " 0.0083 0.0554 0.075  0.1125 0.3817 0.2542 0.1908 0.8333 0.65   0.4688\n",
            " 0.1475 0.3371 0.1154 0.6387 0.5554 0.1167 0.2854 0.05   0.0571 0.245\n",
            " 0.3213 0.8608 0.1208 0.2379 0.1708 0.0312 0.1013 0.2158 0.1075 0.1225\n",
            " 0.45   0.6375 0.0479 0.3925 0.0104 0.2617 0.2558 0.4417 0.7833 0.1542\n",
            " 0.5333 0.3033 0.3808 0.6    0.1833 0.3867 0.4    0.425  0.9304 0.7358\n",
            " 0.3154 0.1771 0.8542 0.0917 0.3021 0.1521 0.7292 0.0979 0.515  0.0167\n",
            " 0.9375 0.1417 0.1179 0.475  0.5729 0.4096 0.275  0.4008 0.3271 0.6083\n",
            " 0.0858 0.14   0.3629 0.6167 0.2304 0.5067 0.1904 0.4458 0.465  0.5488\n",
            " 0.3108 0.6342 0.2208 0.725  0.1546 0.5475 0.01   0.1725 0.6842 0.3517\n",
            " 0.5483 0.2546 0.5654 0.3388 0.4958 0.1817 0.2883 0.28   0.0771 0.3092\n",
            " 0.2421 0.2967 0.2021 0.6979 0.1608 0.595  0.5508 0.2742 0.0983 0.5167\n",
            " 0.1612 0.3279 0.0783 0.5317 0.2675 0.2375 0.6071 0.5583 0.185  0.3238\n",
            " 0.4758 0.5083 0.3392 0.2983 0.0333 0.1379 0.5667 0.0067 0.345  0.8471\n",
            " 0.1792 0.1679 0.0613 0.525  0.8054 0.1425 0.9792 0.3679 0.1296 0.2312\n",
            " 0.12   0.6317 0.3012 0.1567 0.2238 0.0883 0.0712 0.2404 0.1717 0.2258\n",
            " 0.9479 0.3508 0.0467 0.1071 0.3625 0.2267 0.575  0.7    0.115  0.6783\n",
            " 0.1954 0.16   0.2821]\n",
            "[   nan 0.1587 0.1552 ... 0.5531 0.2547 0.782 ]\n",
            "[0.0095    nan 0.1033 ... 0.4448 0.6423 0.6957]\n",
            "[0.        nan 0.188  ... 0.686  0.2544 0.7361]\n",
            "[0.0126    nan 0.2017 0.2605 0.125  0.2279 0.0945 0.1197 0.2899 0.1261\n",
            " 0.0735 0.1134 0.063  0.0746 0.0168 0.1218 0.1891 0.2153 0.0084 0.0021\n",
            " 0.1544 0.2962 0.2132 0.2258 0.0504 0.0903 0.0231 0.166  0.0441 0.0074\n",
            " 0.0063 0.2248 0.0725 0.0536 0.2668 0.2941 0.1292 0.0914 0.0032 0.0756\n",
            " 0.042  0.1796 0.1124 0.3393 0.0221 0.375  0.0882 0.188  0.3361 0.0189\n",
            " 0.084  0.1439 0.187  0.4307 0.4527 0.2542 0.1901 0.3015 0.1513 0.2721\n",
            " 0.1071 0.1817 0.1492 0.1239 0.1502 0.0788 0.4265 0.3876 0.0798 0.0284\n",
            " 0.3099 0.1807 0.2269 0.0704 0.1103 0.0525 0.1838 0.0714 0.0105 0.0935\n",
            " 0.1113 0.2447 0.3424 0.0924 0.0767 0.1828 0.0662 0.0977 0.0347 0.0336\n",
            " 0.02   0.0851 1.     0.0861 0.104  0.3761 0.021  0.1397 0.3046 0.2174\n",
            " 0.     0.0578 0.1271 0.2836 0.0431 0.1334 0.0368 0.1954 0.0252 0.0053\n",
            " 0.0294 0.083  0.1523 0.2374 0.1471 0.0378 0.5252 0.0819 0.0095 0.0777\n",
            " 0.0462 0.1061 0.062  0.1775 0.105  0.2784 0.2038 0.0893 0.2332 0.0966\n",
            " 0.1008 0.2647 0.6387 0.4023 0.0315 0.0599 0.1229 0.1366 0.4034 0.1555\n",
            " 0.3025 0.1786 0.1176 0.1019 0.1565 0.2006 0.2689 0.2553 0.3004 0.2164\n",
            " 0.2731 0.0987 0.0588 0.1702 0.0137 0.0672 0.0158 0.1166 0.3918 0.0147\n",
            " 0.2973 0.3288 0.1082 0.1303 0.145  0.0242 0.5546 0.1639 0.1975 0.1155\n",
            " 0.1387 0.1618 0.2479 0.0357 0.0809 0.5588 0.1355 0.0305 0.2416 0.1345\n",
            " 0.2805 0.1597 0.3782 0.5231 0.6786 0.1744 0.0693 0.1029 0.1607 0.2847\n",
            " 0.2511 0.1324 0.4874 0.3676 0.0473 0.5273 0.208  0.0872 0.1681 0.4202\n",
            " 0.5126 0.1534 0.2143 0.0956 0.48   0.0273 0.271  0.4118 0.0609 0.0998\n",
            " 0.1092 0.2532 0.4359 0.1187 0.0042 0.0494 0.1849 0.1313 0.1429 0.0116\n",
            " 0.146  0.1408 0.0179 0.3477 0.2069 0.2206 0.2059 0.23   0.3624 0.0483\n",
            " 0.1376 0.0399 0.3372 0.0683 0.0641 0.2111 0.1576 0.0011 0.2983 0.1649\n",
            " 0.7983 0.2458 0.1996 0.2626 0.1723 0.4212 0.292  0.688  0.1418 0.416\n",
            " 0.3403 0.3078 0.1586 0.3498 0.0567 0.3792 0.3162 0.1282 0.5294 0.2426\n",
            " 0.1208 0.167  0.1733 0.2405 0.3803 0.334  0.2101 0.4569 0.2216 0.0389\n",
            " 0.4412 0.3603 0.2027 0.4065 0.2742 0.2353 0.1712 0.2679 0.0557 0.2195\n",
            " 0.3866 0.2521 0.2237 0.3855 0.2384 0.2563 0.2395 0.0651 0.4706 0.4244\n",
            " 0.1691 0.3319 0.2773 0.8718 0.3109 0.2637 0.041  0.4422 0.6355 0.2468\n",
            " 0.3214 0.4191 0.2868 0.1145 0.1964 0.0515 0.3204 0.1481 0.4538 0.4737\n",
            " 0.4139 0.291  0.8981 0.229  0.2763 0.1765 0.2616 0.3235 0.5977 0.0263\n",
            " 0.2048 0.5042 0.3666 0.7048 0.0452 0.3582 0.1912 0.3298 0.2931 0.3309\n",
            " 0.4611 0.2584 0.2122 0.4181 0.8109 0.1754 0.3445 0.625  0.5662 0.2815\n",
            " 0.5021 0.3739 0.3036 0.3908 0.3519 0.604  0.2363 0.3057 0.9149 0.4905\n",
            " 0.1985 0.209  0.417  0.3939 0.1628 0.2185 0.5515 0.3592 0.2227 0.3813\n",
            " 0.313  0.4727 0.3414 0.4275 0.0546 0.5179 0.354  0.2994 0.4968 0.2342\n",
            " 0.2311 0.3151 0.3718 0.3456 0.0326 0.3435 0.4107 0.3971 0.2857 0.9055\n",
            " 0.2752 0.5914 0.6176 0.5011 0.5263 0.3992 0.4254 0.4128 0.3193 0.8855\n",
            " 0.3508 0.6712 0.4076 0.2794 0.1943 0.3771 0.3687 0.9622 0.3256 0.395\n",
            " 0.4464 0.2595 0.3225 0.4853 0.3729 0.3141 0.4653 0.1859 0.3645 0.4664\n",
            " 0.3067 0.5882 0.2437 0.3897 0.437  0.6218 0.2952 0.459  0.4758 0.25\n",
            " 0.3571 0.3561 0.1933 0.312  0.5809 0.3487 0.6744 0.5609 0.4391 0.438\n",
            " 0.5347 0.2489 0.3929 0.6261 0.4916 0.4622 0.3088 0.4223 0.542  0.4233\n",
            " 0.2321 0.6303 0.4895 0.6313 0.3267 0.2658 0.479  0.4989 0.2574 0.4937\n",
            " 0.4496 0.4002 0.3382 0.5924 0.5242 0.4086 0.2878 0.1922 0.666  0.3613\n",
            " 0.7489 0.3708 0.27   0.52   0.4517 0.6534 0.3529 0.6239 0.3246 0.5326\n",
            " 0.5777 0.5714 0.4401 0.4716 0.8361 0.3887 0.5672 0.9737 0.4349 0.5\n",
            " 0.5599 0.3183 0.7237 0.7826 0.7899 0.6366 0.5168 0.4044 0.4863 0.2889\n",
            " 0.3351 0.4779 0.4559 0.3277 0.4643 0.7017 0.7038 0.3172 0.6134 0.3981\n",
            " 0.4685 0.4286 0.4958 0.5221 0.5074 0.5704 0.7521 0.5315 0.3466 0.5641\n",
            " 0.6565 0.2826 0.5462 0.8792 0.333  0.6155 0.6334 0.3655 0.4632 0.4296\n",
            " 0.5494 0.3845 0.5084 0.6082 0.4433 0.4485 0.687  0.5525 0.4338 0.7941\n",
            " 0.4506 0.4884 0.4055 0.5746 0.4475 0.7248 0.4328 0.4821 0.6492 0.6817\n",
            " 0.5147 0.8529 0.5767 0.3824 0.5137 0.6607 0.4149 0.541  0.563  0.4443\n",
            " 0.6008 0.8372 0.708  0.4013 0.7742 0.584  0.8561 0.4601 0.7889 0.3634\n",
            " 0.4454 0.6124 0.6397 0.5441 0.6092 0.5389 0.7027 0.5053 0.5651 0.6229\n",
            " 0.5725 0.562  0.355  0.5032 0.7279 0.3834 0.5431 0.8929 0.792  0.5158\n",
            " 0.4097 0.5567 0.7815 0.4769 0.645  0.5693 0.5819 0.7658 0.5483 0.4811\n",
            " 0.6513 0.5284 0.7553 0.5357 0.4979 0.9065 0.5504 0.605  0.8162 0.5095\n",
            " 0.7059 0.521  0.75   0.458  0.6471 0.5578 0.5935 0.5378 0.3697 0.6502\n",
            " 0.8992 0.6586 0.7269 0.6345 0.6628 0.7878 0.5683 0.5336 0.9223 0.771\n",
            " 0.6029 0.5966 0.4548 0.5305 0.4926 0.854  0.6681 0.7836 0.4674 0.6019\n",
            " 0.5735 0.9422 0.9191 0.4317 0.6765 0.6113 0.6576 0.5189 0.7069 0.5557\n",
            " 0.6901 0.4748 0.5956 0.6103 0.834  0.7153 0.4947 0.8382 0.8824 0.7794\n",
            " 0.6807 0.5861 0.6271 0.5452 0.7006 0.9107 0.7111 0.7437 0.5368 0.583\n",
            " 0.6197 0.6187 0.4695 0.7458 0.6429 0.396  0.4832 0.6796 0.7605 0.5903\n",
            " 0.8351 0.6849 0.646  0.5893 0.667  0.8288 0.5998 0.8498 0.8403 0.6922\n",
            " 0.6723 0.8088 0.7595 0.9947 0.8508 0.7395 0.8456 0.5756 0.6975 0.875\n",
            " 0.7731 0.6292 0.7962 0.6775 0.6071 0.709  0.6061 0.8918 0.9664 0.9884\n",
            " 0.5987 0.6439 0.9002 0.6702 0.6376 0.855  0.7857 0.7952 0.7752 0.8487\n",
            " 0.6555 0.8099 0.958  0.6649 0.5063 0.6324 0.7227 0.7847 0.6828 0.8204\n",
            " 0.5536 0.8393 0.7143 0.9842 0.8676 0.5116 0.7511 0.6166 0.4842 0.7637\n",
            " 0.5945 0.6282 0.9244 0.6597]\n",
            "[   nan 0.1213 0.1958 ... 0.5607 0.7357 0.4593]\n",
            "[9.444e-01       nan 9.841e-01 9.826e-01 9.762e-01 9.851e-01 9.801e-01\n",
            " 9.836e-01 9.891e-01 9.846e-01 9.985e-01 9.782e-01 9.970e-01 9.856e-01\n",
            " 9.767e-01 9.816e-01 9.791e-01 9.831e-01 9.796e-01 9.722e-01 9.657e-01\n",
            " 9.935e-01 9.980e-01 9.687e-01 9.786e-01 9.821e-01 9.866e-01 9.777e-01\n",
            " 9.940e-01 9.911e-01 9.692e-01 9.811e-01 9.737e-01 9.965e-01 9.707e-01\n",
            " 9.950e-01 9.772e-01 9.871e-01 9.747e-01 9.742e-01 9.921e-01 9.876e-01\n",
            " 9.995e-01 9.732e-01 9.757e-01 9.752e-01 9.697e-01 9.906e-01 9.886e-01\n",
            " 9.677e-01 9.861e-01 9.990e-01 9.881e-01 9.806e-01 9.896e-01 9.901e-01\n",
            " 9.618e-01 9.702e-01 9.613e-01 9.429e-01 9.945e-01 9.960e-01 9.404e-01\n",
            " 9.717e-01 9.930e-01 9.662e-01 5.000e-04 9.494e-01 9.727e-01 9.712e-01\n",
            " 9.518e-01 9.916e-01 9.667e-01 9.955e-01 9.926e-01 9.588e-01 9.598e-01\n",
            " 9.643e-01 1.000e+00 9.975e-01 5.452e-01 0.000e+00 9.647e-01 9.608e-01\n",
            " 9.215e-01 9.623e-01 9.652e-01 9.603e-01 9.578e-01 9.474e-01 9.583e-01\n",
            " 9.628e-01 9.469e-01 9.489e-01 9.513e-01 9.275e-01 9.449e-01 9.593e-01\n",
            " 9.672e-01 9.573e-01 9.335e-01 9.563e-01 9.508e-01 9.350e-01 9.682e-01\n",
            " 9.633e-01 9.434e-01 9.568e-01 9.374e-01 9.523e-01 9.340e-01 9.181e-01\n",
            " 8.421e-01 9.409e-01 9.414e-01 9.459e-01 9.384e-01 9.345e-01 9.558e-01\n",
            " 9.280e-01 9.484e-01 9.305e-01 9.464e-01 9.091e-01 9.533e-01 9.389e-01\n",
            " 9.638e-01 9.424e-01 9.364e-01 9.101e-01 9.499e-01 9.503e-01 9.235e-01\n",
            " 9.325e-01 9.285e-01 9.479e-01 9.439e-01 9.528e-01 8.928e-01 9.230e-01\n",
            " 9.553e-01 9.186e-01 9.121e-01 8.277e-01 9.071e-01 5.943e-01 9.359e-01\n",
            " 9.543e-01 9.454e-01 9.394e-01 9.419e-01 9.399e-01 9.146e-01 9.548e-01\n",
            " 9.290e-01 4.359e-01 9.141e-01 9.076e-01 9.295e-01 9.225e-01 9.161e-01\n",
            " 9.330e-01 9.300e-01 9.136e-01 9.315e-01 9.355e-01 9.151e-01 9.379e-01\n",
            " 9.730e-02 8.923e-01 8.411e-01 9.002e-01 9.096e-01 9.067e-01 9.240e-01\n",
            " 9.245e-01 9.538e-01 9.320e-01 4.520e-02 9.369e-01 9.191e-01 9.196e-01\n",
            " 9.310e-01 8.401e-01 8.833e-01 9.116e-01 9.086e-01 8.977e-01 9.206e-01\n",
            " 9.106e-01 8.982e-01 9.211e-01 9.220e-01 1.840e-02 8.992e-01 9.171e-01\n",
            " 9.057e-01 8.823e-01 9.260e-01 8.972e-01 4.518e-01 8.580e-01 8.967e-01\n",
            " 9.250e-01 8.987e-01 9.830e-02 8.222e-01 8.719e-01 7.433e-01 9.265e-01]\n",
            "[0.        nan 0.1611 0.282  0.2417 0.3222 0.0806 0.0403 0.1208 0.2014\n",
            " 0.3625 0.4431 0.4834 1.     0.4028 0.6042 0.6848 0.5639 0.8056 0.5236\n",
            " 0.725  0.6445 0.9667 0.8459 0.8862 0.7653 0.9264]\n",
            "[0.0345    nan 0.069  0.2414 0.2069 0.2759 0.1379 0.1034 0.1724 0.\n",
            " 0.4828 0.3103 0.3793 0.4138 0.3448 0.9655 0.8276 0.5172 0.5862 0.5517\n",
            " 0.6897 0.6207 0.4483 0.7241 1.     0.7931 0.8621 0.7586 0.6552 0.931\n",
            " 0.8966]\n",
            "[0.0417    nan 0.4583 0.3333 0.1667 0.625  0.375  0.     0.6667 0.125\n",
            " 0.0833 0.2917 0.7083 0.875  0.2083 0.25   0.5    0.5417 0.75   0.5833\n",
            " 0.7917 0.4167 0.9583 1.     0.9167 0.8333]\n",
            "[   nan 0.1623 0.1587 ... 0.5658 0.2605 0.7999]\n",
            "[0.0099    nan 0.1076 ... 0.6692 0.7248 0.2257]\n",
            "[0.        nan 0.199  ... 0.7262 0.2693 0.7793]\n",
            "[0.0125    nan 0.1999 ... 0.6537 0.7276 0.3003]\n",
            "[   nan 0.1169 0.1886 ... 0.5403 0.709  0.4426]\n",
            "[0.9444    nan 0.9841 0.9826 0.9762 0.9851 0.9801 0.9836 0.9891 0.9846\n",
            " 0.9985 0.9781 0.997  0.9856 0.9767 0.9816 0.9791 0.9831 0.9796 0.9722\n",
            " 0.9657 0.9935 0.998  0.9687 0.9786 0.9821 0.9866 0.9776 0.994  0.9911\n",
            " 0.9692 0.9811 0.9737 0.9965 0.9707 0.995  0.9771 0.9871 0.9747 0.9742\n",
            " 0.9921 0.9876 0.9995 0.9732 0.9757 0.9752 0.9697 0.9906 0.9886 0.9677\n",
            " 0.9861 0.999  0.9881 0.9806 0.9896 0.9901 0.9617 0.9702 0.9613 0.9429\n",
            " 0.9945 0.996  0.9404 0.9717 0.993  0.9662 0.     0.9603 0.9955 0.9727\n",
            " 0.9712 0.9518 0.9916 0.9667 0.9459 0.4923 0.9925 0.9622 0.9588 0.9598\n",
            " 0.9642 1.     0.9975 0.545  0.9652 0.9608 0.9215 0.9578 0.9473 0.9583\n",
            " 0.9627 0.9493 0.9672 0.9468 0.9488 0.9513 0.9275 0.9449 0.4983 0.9593\n",
            " 0.9573 0.9647 0.9334 0.9563 0.9508 0.9424 0.9682 0.9632 0.9434 0.9568\n",
            " 0.9374 0.9523 0.9339 0.918  0.916  0.4903 0.9409 0.4878 0.9414 0.9384\n",
            " 0.9344 0.4848 0.9558 0.9483 0.9305 0.9463 0.9091 0.9533 0.9389 0.9637\n",
            " 0.4898 0.9101 0.9498 0.9503 0.9235 0.9285 0.9324 0.9478 0.928  0.9528\n",
            " 0.8927 0.923  0.9185 0.4993 0.8276 0.9071 0.5941 0.9349 0.9121 0.9399\n",
            " 0.9454 0.9394 0.4873 0.9419 0.9329 0.4978 0.9548 0.929  0.6841 0.9553\n",
            " 0.4918 0.926  0.9295 0.9225 0.4913 0.4759 0.4943 0.93   0.9314 0.9364\n",
            " 0.4883 0.9543 0.9354 0.9379 0.4779 0.0969 0.9369 0.9439 0.8922 0.841\n",
            " 0.9001 0.9096 0.9066 0.924  0.9245 0.9538 0.9319 0.0447 0.9136 0.4953\n",
            " 0.919  0.4973 0.4908 0.9195 0.9309 0.92   0.9151 0.4863 0.4853 0.4948\n",
            " 0.8833 0.4893 0.9359 0.9116 0.9086 0.4784 0.4933 0.9205 0.8982 0.921\n",
            " 0.922  0.4789 0.0179 0.8992 0.8977 0.917  0.9056 0.8823 0.8579 0.8967\n",
            " 0.925  0.4938 0.4774 0.8987 0.0979 0.4888 0.8718 0.8629 0.4958 0.9265]\n",
            "[0.    nan 0.16 0.28 0.24 0.32 0.08 0.04 0.12 0.2  0.4  0.44 0.48 0.36\n",
            " 0.1  0.14 1.   0.22 0.6  0.06 0.72 0.68 0.56 0.8  0.52 0.02 0.64 0.18\n",
            " 0.96 0.38 0.3  0.5  0.42 0.26 0.34 0.84 0.88 0.74 0.58 0.76 0.46 0.92\n",
            " 0.54 0.66 0.78 0.62 0.9 ]\n",
            "[0.0345    nan 0.069  0.2414 0.2069 0.2759 0.1379 0.1034 0.1724 0.1207\n",
            " 0.     0.4828 0.3448 0.3793 0.4138 0.3103 0.1552 0.9655 0.0862 0.1897\n",
            " 0.0517 0.8276 0.5172 0.5862 0.5517 0.6897 0.4483 0.6207 0.2586 0.2241\n",
            " 0.7241 1.     0.3621 0.3276 0.7931 0.431  0.8621 0.7586 0.6552 0.2931\n",
            " 0.931  0.8966 0.3966 0.4655 0.0172 0.569  0.5   ]\n",
            "[0.0417    nan 0.4583 0.3333 0.1667 0.625  0.375  0.     0.6667 0.2708\n",
            " 0.125  0.0208 0.0833 0.2917 0.25   0.7917 0.875  0.2083 0.3542 0.5\n",
            " 0.5833 0.75   0.1042 0.0625 0.1458 0.5417 0.4167 0.2292 0.4792 0.6042\n",
            " 0.1875 0.7083 0.6458 0.9583 1.     0.4375 0.7708 0.3958 0.6875 0.5208\n",
            " 0.5625 0.8125 0.3125 0.9167 0.8333 0.8958 0.8542 0.7292 0.9375 0.9792]\n",
            "[   nan 0.1615 0.1579 ... 0.5628 0.2591 0.7956]\n",
            "[0.0096    nan 0.1051 ... 0.4367 0.6538 0.7082]\n",
            "[0.        nan 0.1919 ... 0.7004 0.2597 0.7516]\n",
            "['block of flats' nan 'specific housing' 'terraced house']\n",
            "[0.0074    nan 0.1789 ... 0.3286 0.7499 0.4634]\n",
            "['Mixed' nan 'Panel' 'Stone, brick' 'Block' 'Monolithic' 'Wooden' 'Others']\n",
            "['No' nan 'Yes']\n",
            "[  0.   1.   7.   2.   5.   3.   4.   8.   6.   9.  12.  13.  15.  10.\n",
            "  14.  11.  16.  nan  21.  19.  20.  22.  17.  24.  25.  18.  23.  29.\n",
            " 348.  30.  26.  27.]\n",
            "[ 0.  1.  2.  3. nan  5.  4.  8.  6. 34.  7.]\n",
            "[  0.   1.   7.   2.   5.   3.   4.   8.   6.   9.  11.  12.  13.  15.\n",
            "  10.  14.  16.  nan  20.  18.  22.  24.  19.  25.  21.  17.  23.  29.\n",
            " 344.  30.  27.]\n",
            "[ 0.  1.  2.  3. nan  4.  6.  5. 24.  7.]\n",
            "[ -150.  -444.     0. ... -3730. -3731. -3480.]\n",
            "[0 1]\n",
            "[1 0]\n",
            "[0 1]\n",
            "[0 1]\n",
            "[0 1]\n",
            "[0 1]\n",
            "[0 1]\n",
            "[0 1]\n",
            "[0 1]\n",
            "[0 1]\n",
            "[0]\n",
            "[0 1]\n",
            "[0 1]\n",
            "[0 1]\n",
            "[0 1]\n",
            "[0 1]\n",
            "[0 1]\n",
            "[0 1]\n",
            "[0 1]\n",
            "[0 1]\n",
            "[ 0. nan  1.  3.  2.  4.]\n",
            "[ 0. nan  1.  2.  3.  6.  5.  4.  8.  9.]\n",
            "[ 0. nan  1.  2.  6.  4.  3.  5.  7.]\n",
            "[ 1.  0.  2. nan  6.  3. 11.  4. 10.  5.  8.  7. 12.  9. 22. 15. 14. 13.\n",
            " 17. 18. 16. 19. 24.]\n",
            "[  0.   1.  nan   2.   3.   4.   5.   8.   7.   6. 261.  19.]\n",
            "[ 2.  3.  1. nan  6.  0.  8.  5.  7.  4.  9. 11. 10. 17. 13. 12. 19. 25.\n",
            " 14. 15. 16. 23. 20.]\n",
            "[0 1]\n"
          ]
        }
      ],
      "source": [
        "for i in dataFrame.columns:\n",
        "  print(dataFrame[i].unique())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "76W7x5mPffwb"
      },
      "outputs": [],
      "source": [
        "#Filling na with median for train data\n",
        "\n",
        "for i in dataFrame.columns:\n",
        "  if(dataFrame[i].isna().any() and dataFrame[i].dtypes!=object):\n",
        "    dataFrame[i]=dataFrame[i].fillna(dataFrame[i].median())\n",
        "\n",
        "\n",
        "#Filling na with median for test data\n",
        "\n",
        "for i in dataFrame_test.columns:\n",
        "  if(dataFrame_test[i].isna().any() and dataFrame_test[i].dtypes!=object):\n",
        "    dataFrame_test[i]=dataFrame_test[i].fillna(dataFrame_test[i].median())\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-x4Dz-QRfrIJ"
      },
      "source": [
        "##**2.3 Replacing Nan values which has datatype as object with mode values**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "pce3STI-Eksz"
      },
      "outputs": [],
      "source": [
        "#Filling object Nan with mode for train data\n",
        "for i in dataFrame.columns:\n",
        "  if(dataFrame[i].isna().any() and dataFrame[i].dtypes==object):\n",
        "    dataFrame[i]=dataFrame[i].fillna(dataFrame[i].mode())\n",
        "\n",
        "\n",
        "#Filling object Nan with mode for test data\n",
        "for i in dataFrame_test.columns:\n",
        "  if(dataFrame_test[i].isna().any() and dataFrame_test[i].dtypes==object):\n",
        "    dataFrame_test[i]=dataFrame_test[i].fillna(dataFrame_test[i].mode())\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S-9lVkfwgKkz"
      },
      "source": [
        "##**2.4 Performing one hot encoding for columns with datatype as object**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DGMWVJm7Ei6w",
        "outputId": "acbd7abf-7edf-457e-aab4-ea04224246ae"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['NAME_CONTRACT_TYPE', 'CODE_GENDER', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY', 'NAME_TYPE_SUITE', 'NAME_INCOME_TYPE', 'NAME_EDUCATION_TYPE', 'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE', 'OCCUPATION_TYPE', 'WEEKDAY_APPR_PROCESS_START', 'ORGANIZATION_TYPE', 'HOUSETYPE_MODE', 'WALLSMATERIAL_MODE', 'EMERGENCYSTATE_MODE']\n",
            "['NAME_CONTRACT_TYPE', 'CODE_GENDER', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY', 'NAME_TYPE_SUITE', 'NAME_INCOME_TYPE', 'NAME_EDUCATION_TYPE', 'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE', 'OCCUPATION_TYPE', 'WEEKDAY_APPR_PROCESS_START', 'ORGANIZATION_TYPE', 'HOUSETYPE_MODE', 'WALLSMATERIAL_MODE', 'EMERGENCYSTATE_MODE']\n"
          ]
        }
      ],
      "source": [
        "#Performing one hot encoding for train data\n",
        "columns_for_one_hot_encoding=[]\n",
        "for i in dataFrame.columns:\n",
        "  if(dataFrame[i].dtypes==object):\n",
        "    columns_for_one_hot_encoding.append(i)\n",
        "print(columns_for_one_hot_encoding)\n",
        "\n",
        "\n",
        "#Performing one hot encoding for test data\n",
        "columns_for_one_hot_encoding=[]\n",
        "for i in dataFrame_test.columns:\n",
        "  if(dataFrame_test[i].dtypes==object):\n",
        "    columns_for_one_hot_encoding.append(i)\n",
        "print(columns_for_one_hot_encoding)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "R2h75JLxHOF3"
      },
      "outputs": [],
      "source": [
        "dataFrame=pd.get_dummies(dataFrame,columns=columns_for_one_hot_encoding)\n",
        "\n",
        "dataFrame_test=pd.get_dummies(dataFrame_test,columns=columns_for_one_hot_encoding)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "qeYPDC75BAah",
        "outputId": "bbe22834-e8bd-4aa1-eedc-1474c5748b05"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        SK_ID_CURR  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
              "0           450407             1           67500.0    227520.0      11065.5   \n",
              "1           271298             1          247500.0   1882372.5      65560.5   \n",
              "2           122238             1          180000.0    101880.0      10827.0   \n",
              "3           305311             0           81000.0    405000.0      20677.5   \n",
              "4           414121             0          157500.0    888840.0      29506.5   \n",
              "...            ...           ...               ...         ...          ...   \n",
              "184501      439663             1          135000.0    143910.0      15498.0   \n",
              "184502      385689             0          202500.0    360000.0      18000.0   \n",
              "184503      153567             0          135000.0    900000.0      45954.0   \n",
              "184504      365526             0          123300.0    744988.5      20484.0   \n",
              "184505      125605             0           81000.0    167895.0      17757.0   \n",
              "\n",
              "        AMT_GOODS_PRICE  REGION_POPULATION_RELATIVE  DAYS_BIRTH  \\\n",
              "0              180000.0                    0.018634      -17210   \n",
              "1             1719000.0                    0.018850      -18640   \n",
              "2               90000.0                    0.022625      -14649   \n",
              "3              405000.0                    0.035792      -12762   \n",
              "4              675000.0                    0.018801      -16329   \n",
              "...                 ...                         ...         ...   \n",
              "184501         135000.0                    0.016612      -12139   \n",
              "184502         360000.0                    0.010032      -22472   \n",
              "184503         900000.0                    0.014464      -15037   \n",
              "184504         744988.5                    0.026392      -20586   \n",
              "184505         157500.0                    0.018850      -16397   \n",
              "\n",
              "        DAYS_EMPLOYED  DAYS_REGISTRATION  ...  HOUSETYPE_MODE_terraced house  \\\n",
              "0                -774            -5580.0  ...                              0   \n",
              "1                 -96            -5711.0  ...                              0   \n",
              "2               -6130            -1776.0  ...                              0   \n",
              "3               -5891            -6430.0  ...                              0   \n",
              "4                -345            -1413.0  ...                              0   \n",
              "...               ...                ...  ...                            ...   \n",
              "184501          -1442            -3888.0  ...                              0   \n",
              "184502         365243           -12171.0  ...                              0   \n",
              "184503            -88            -5025.0  ...                              0   \n",
              "184504         365243            -6310.0  ...                              0   \n",
              "184505          -3009             -708.0  ...                              0   \n",
              "\n",
              "        WALLSMATERIAL_MODE_Block  WALLSMATERIAL_MODE_Mixed  \\\n",
              "0                              0                         1   \n",
              "1                              0                         0   \n",
              "2                              0                         0   \n",
              "3                              0                         0   \n",
              "4                              0                         0   \n",
              "...                          ...                       ...   \n",
              "184501                         0                         0   \n",
              "184502                         0                         0   \n",
              "184503                         0                         0   \n",
              "184504                         0                         0   \n",
              "184505                         0                         0   \n",
              "\n",
              "        WALLSMATERIAL_MODE_Monolithic  WALLSMATERIAL_MODE_Others  \\\n",
              "0                                   0                          0   \n",
              "1                                   0                          0   \n",
              "2                                   0                          0   \n",
              "3                                   0                          0   \n",
              "4                                   0                          0   \n",
              "...                               ...                        ...   \n",
              "184501                              0                          0   \n",
              "184502                              0                          0   \n",
              "184503                              0                          0   \n",
              "184504                              0                          0   \n",
              "184505                              0                          0   \n",
              "\n",
              "        WALLSMATERIAL_MODE_Panel  WALLSMATERIAL_MODE_Stone, brick  \\\n",
              "0                              0                                0   \n",
              "1                              0                                0   \n",
              "2                              1                                0   \n",
              "3                              0                                0   \n",
              "4                              0                                0   \n",
              "...                          ...                              ...   \n",
              "184501                         0                                1   \n",
              "184502                         1                                0   \n",
              "184503                         1                                0   \n",
              "184504                         0                                0   \n",
              "184505                         0                                0   \n",
              "\n",
              "        WALLSMATERIAL_MODE_Wooden  EMERGENCYSTATE_MODE_No  \\\n",
              "0                               0                       1   \n",
              "1                               0                       0   \n",
              "2                               0                       1   \n",
              "3                               0                       0   \n",
              "4                               0                       0   \n",
              "...                           ...                     ...   \n",
              "184501                          0                       1   \n",
              "184502                          0                       1   \n",
              "184503                          0                       1   \n",
              "184504                          0                       0   \n",
              "184505                          0                       0   \n",
              "\n",
              "        EMERGENCYSTATE_MODE_Yes  \n",
              "0                             0  \n",
              "1                             0  \n",
              "2                             0  \n",
              "3                             0  \n",
              "4                             0  \n",
              "...                         ...  \n",
              "184501                        0  \n",
              "184502                        0  \n",
              "184503                        0  \n",
              "184504                        0  \n",
              "184505                        0  \n",
              "\n",
              "[184506 rows x 226 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5f0a5648-d8cd-43b9-ac11-16e3cd87d461\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\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>SK_ID_CURR</th>\n",
              "      <th>CNT_CHILDREN</th>\n",
              "      <th>AMT_INCOME_TOTAL</th>\n",
              "      <th>AMT_CREDIT</th>\n",
              "      <th>AMT_ANNUITY</th>\n",
              "      <th>AMT_GOODS_PRICE</th>\n",
              "      <th>REGION_POPULATION_RELATIVE</th>\n",
              "      <th>DAYS_BIRTH</th>\n",
              "      <th>DAYS_EMPLOYED</th>\n",
              "      <th>DAYS_REGISTRATION</th>\n",
              "      <th>...</th>\n",
              "      <th>HOUSETYPE_MODE_terraced house</th>\n",
              "      <th>WALLSMATERIAL_MODE_Block</th>\n",
              "      <th>WALLSMATERIAL_MODE_Mixed</th>\n",
              "      <th>WALLSMATERIAL_MODE_Monolithic</th>\n",
              "      <th>WALLSMATERIAL_MODE_Others</th>\n",
              "      <th>WALLSMATERIAL_MODE_Panel</th>\n",
              "      <th>WALLSMATERIAL_MODE_Stone, brick</th>\n",
              "      <th>WALLSMATERIAL_MODE_Wooden</th>\n",
              "      <th>EMERGENCYSTATE_MODE_No</th>\n",
              "      <th>EMERGENCYSTATE_MODE_Yes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
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              "      <td>450407</td>\n",
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              "      <td>67500.0</td>\n",
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              "      <td>11065.5</td>\n",
              "      <td>180000.0</td>\n",
              "      <td>0.018634</td>\n",
              "      <td>-17210</td>\n",
              "      <td>-774</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>271298</td>\n",
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              "      <td>247500.0</td>\n",
              "      <td>1882372.5</td>\n",
              "      <td>65560.5</td>\n",
              "      <td>1719000.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>122238</td>\n",
              "      <td>1</td>\n",
              "      <td>180000.0</td>\n",
              "      <td>101880.0</td>\n",
              "      <td>10827.0</td>\n",
              "      <td>90000.0</td>\n",
              "      <td>0.022625</td>\n",
              "      <td>-14649</td>\n",
              "      <td>-6130</td>\n",
              "      <td>-1776.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
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              "      <th>3</th>\n",
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              "      <td>405000.0</td>\n",
              "      <td>20677.5</td>\n",
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              "      <td>-12762</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "    </tr>\n",
              "    <tr>\n",
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              "        SK_ID_CURR  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
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            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "dataFrame_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BzmVk1nzLnMj",
        "outputId": "2349953a-0a98-4ee1-bf07-4a972acb2833"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "226\n"
          ]
        }
      ],
      "source": [
        "count=0\n",
        "for i in dataFrame.columns:\n",
        "  if(dataFrame[i].dtypes!=object):\n",
        "    count+=1\n",
        "print(count)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5N2L4JSAgl3v"
      },
      "source": [
        "##**2.5 Performing min-max normalizing on the dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "-3R7i4UTxpIh"
      },
      "outputs": [],
      "source": [
        "#Performing min-max normalizing for train data\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "scaler.fit(dataFrame)\n",
        "scaled_features = scaler.transform(dataFrame)\n",
        "dataFrame = pd.DataFrame(data=scaled_features,index=dataFrame.index, columns=dataFrame.columns)\n",
        "\n",
        "\n",
        "# #Performing min-max normalizing for test data\n",
        "scaler = MinMaxScaler()\n",
        "scaler.fit(dataFrame_test)\n",
        "scaled_features = scaler.transform(dataFrame_test)\n",
        "dataFrame_test = pd.DataFrame(data=scaled_features,index=dataFrame_test.index, columns=dataFrame_test.columns)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "mxhq_iWbVYyd",
        "outputId": "af9bfb8e-399f-4c75-b708-0656688fe439"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        SK_ID_CURR  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
              "0         0.983587      0.052632          0.000354    0.045573     0.036855   \n",
              "1         0.480827      0.052632          0.001893    0.458770     0.249386   \n",
              "2         0.062414      0.052632          0.001316    0.014202     0.035925   \n",
              "3         0.576302      0.000000          0.000469    0.089888     0.074342   \n",
              "4         0.881732      0.000000          0.001123    0.210697     0.108775   \n",
              "...            ...           ...               ...         ...          ...   \n",
              "184501    0.953429      0.052632          0.000931    0.024697     0.054142   \n",
              "184502    0.801923      0.000000          0.001508    0.078652     0.063900   \n",
              "184503    0.150355      0.000000          0.000931    0.213483     0.172920   \n",
              "184504    0.745326      0.000000          0.000831    0.174779     0.073587   \n",
              "184505    0.071865      0.000000          0.000469    0.030685     0.062952   \n",
              "\n",
              "        AMT_GOODS_PRICE  REGION_POPULATION_RELATIVE  DAYS_BIRTH  \\\n",
              "0              0.034792                    0.254009    0.451163   \n",
              "1              0.418631                    0.257000    0.370427   \n",
              "2              0.012346                    0.309272    0.595754   \n",
              "3              0.090909                    0.491595    0.702292   \n",
              "4              0.158249                    0.256321    0.500903   \n",
              "...                 ...                         ...         ...   \n",
              "184501         0.023569                    0.226010    0.737466   \n",
              "184502         0.079686                    0.134897    0.154076   \n",
              "184503         0.214366                    0.196267    0.573848   \n",
              "184504         0.175705                    0.361433    0.260558   \n",
              "184505         0.029181                    0.257000    0.497064   \n",
              "\n",
              "        DAYS_EMPLOYED  DAYS_REGISTRATION  ...  HOUSETYPE_MODE_terraced house  \\\n",
              "0            0.044729           0.761701  ...                            0.0   \n",
              "1            0.046498           0.756107  ...                            0.0   \n",
              "2            0.030750           0.924154  ...                            0.0   \n",
              "3            0.031374           0.725401  ...                            0.0   \n",
              "4            0.045848           0.939657  ...                            0.0   \n",
              "...               ...                ...  ...                            ...   \n",
              "184501       0.042985           0.833960  ...                            0.0   \n",
              "184502       1.000000           0.480227  ...                            0.0   \n",
              "184503       0.046519           0.785403  ...                            0.0   \n",
              "184504       1.000000           0.730526  ...                            0.0   \n",
              "184505       0.038895           0.969764  ...                            0.0   \n",
              "\n",
              "        WALLSMATERIAL_MODE_Block  WALLSMATERIAL_MODE_Mixed  \\\n",
              "0                            0.0                       1.0   \n",
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              "184504                       0.0                       0.0   \n",
              "184505                       0.0                       0.0   \n",
              "\n",
              "        WALLSMATERIAL_MODE_Monolithic  WALLSMATERIAL_MODE_Others  \\\n",
              "0                                 0.0                        0.0   \n",
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              "\n",
              "        WALLSMATERIAL_MODE_Panel  WALLSMATERIAL_MODE_Stone, brick  \\\n",
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              "184504                       0.0                              0.0   \n",
              "184505                       0.0                              0.0   \n",
              "\n",
              "        WALLSMATERIAL_MODE_Wooden  EMERGENCYSTATE_MODE_No  \\\n",
              "0                             0.0                     1.0   \n",
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              "184504                        0.0                     0.0   \n",
              "184505                        0.0                     0.0   \n",
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              "      <th></th>\n",
              "      <th>SK_ID_CURR</th>\n",
              "      <th>CNT_CHILDREN</th>\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>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>184502</th>\n",
              "      <td>0.801923</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.001508</td>\n",
              "      <td>0.078652</td>\n",
              "      <td>0.063900</td>\n",
              "      <td>0.079686</td>\n",
              "      <td>0.134897</td>\n",
              "      <td>0.154076</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.480227</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>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>184503</th>\n",
              "      <td>0.150355</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000931</td>\n",
              "      <td>0.213483</td>\n",
              "      <td>0.172920</td>\n",
              "      <td>0.214366</td>\n",
              "      <td>0.196267</td>\n",
              "      <td>0.573848</td>\n",
              "      <td>0.046519</td>\n",
              "      <td>0.785403</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>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>184504</th>\n",
              "      <td>0.745326</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000831</td>\n",
              "      <td>0.174779</td>\n",
              "      <td>0.073587</td>\n",
              "      <td>0.175705</td>\n",
              "      <td>0.361433</td>\n",
              "      <td>0.260558</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.730526</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>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>184505</th>\n",
              "      <td>0.071865</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000469</td>\n",
              "      <td>0.030685</td>\n",
              "      <td>0.062952</td>\n",
              "      <td>0.029181</td>\n",
              "      <td>0.257000</td>\n",
              "      <td>0.497064</td>\n",
              "      <td>0.038895</td>\n",
              "      <td>0.969764</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>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>184506 rows × 226 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7382ebc4-d49f-4f9d-b95d-33a6b8d3c510')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7382ebc4-d49f-4f9d-b95d-33a6b8d3c510 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7382ebc4-d49f-4f9d-b95d-33a6b8d3c510');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "dataFrame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "ZBwNRIV0d88D",
        "outputId": "fd222738-052a-4ecf-a8b7-d0f0cd198927"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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1Lpv89sDoPGqc30QHmK26Hj05UWiorutuv8vSydMOfM1k9j93djf/aXQeNUrtLsaxKKQK35nZa8+9JpY/9b1Tr7lkqrtgZXQeNYJFMSIePWmkVkwf9synTJz9mHPXPOfCbpfV0XlUaxbFiDhRaOSmWLjoXWvesPRpE5+YuWT6KRd1u/6BRXNSu//fWBTSLN3Bo7Z6xdS7lzx38tSbrp/53Uuj86h2ajeR1rUoatfIap5ruzvssnTytANfO/nO/76ru/kV0XlUG3dEB5ituhaFE4XGxkUze++158TyPVx4ayPdHh1gtupaFE4UGjtrF97/6MJb1ZwoRuTO6ADSw5li4aK/cuGtahbFiNwcHUCq4sJbFTx6GpHa3QKvdnLhrYfhRDEiThSqlXUW3t+d6i64MTqPQlkUI+JEoVpaMX3YQXtMrNjOhXerWRQjcgs1vA1eAphk08X3L7x3v9CFd6usZtnqe6NDzFYti6LM0xl6ZSHVVm/hfeJSF96t8svoAHNRy6Lo85xXjbB24X3k5AkuvJuvjA4wF3UuijI6gDRI357ZZ689Jz71lJOmXn2JC+/GcqIYseujA0iD1+ksn/7DZ+4xsWK7z695tgvv5rEoRqyMDiANyySbLs7WHL1034mPT186s7t3eDdHGR1gLupcFE4Uarzb2WLrl0+euOS5k6fedIML7yZwohixMjqANCrXdnfYZYkL7yYoowPMhUUh1YgL71q7lWWr744OMRe1LYoyT++ipmOcND8uvGvquugAc1Xboui7PDqAFOVBC2/v8B5/P4kOMFcWhVRz/YX30udPnvKrG2a2+150Hq3Xj6IDzFXdi+Ky6ADSuPhFd8dkyeTpz3DhPbYsiiA/jA4gjZu1C++Tp17lI83HxzQ1PnrqdLvd6AzzkmTFdcCu0TmkcbSIqYn3Ljzn0pctuGCfTocto/O02JUsW71HdIi5qvtEAR4/Seu17sL7ezO7eYd3nNoeO0EzisKFtrQBt7PF1i+b/Osl/YW3d3iPXq2PyS0KqUX6C+8Dj5r8i5/c3d3syug8LeJEEcyikGbpgpmn7f3UieW7v2/qVd9d093Ehffw/Tg6wHzUfpkNkGTFtcDjo3NIdbSIqYmTFp596RELvu3CeziuZtnq3aJDzEcTJgpwqpDmbJJNF79zzRvXLry9w3vwLowOMF9NKQqvfJLmqb/wXnro5AdvXOkd3oNkUYyJ/4oOIDXFz7s77fqsydOf4cJ7YL4dHWC+mlIUlwK3RYeQmsSF90Bcw7LVN83nAzqdzopOp/PbTqfz00GFmq1GFEWZpzPA16NzSM3T6XxyOj3oKRNnb/dPaw7xkeaz958D+IxzgBcM4HPmrBFF0ffV6ABSU61deO83ceaa78882Tu8N9435/sB3W73ImDVALLMWZOK4hvAmugQUpOtYsttjph8z5LewntbF97VpoFvRYcYhMYURZmndwAXR+eQ2qC38P7wM143+Q4X3uv3A5atbsRRXWOKos/jJ2mEzp/Zd++nTizf/f1Tr3Th/VD/Hh1gUCwKSfPU6Zw1/cL+wnvpt114/3//Eh1gUBrxCI91JVlxDfCE6BxSW23N6ts+vui0K57eufqgToeF0XmCDOyxHZ1O53PAIcC2wM3Ae7rd7vJBfPbGatpEAVBEB5DabJ2F98oWL7z/eVAf1O12X9Htdh/X7XY37Xa7O466JKCZReHxkzQGWr7w/mJ0gEFqYlFcCNwVHUJSzzoL74tbsvC+lmWra/3+iQdrXFGUeTrJAG5ykTRInc5Z0y88eI+JFduet2Zp0+/wHtix07hoXFH0Ne5/KKkJJli02Qlrjlm638SZa37Qu8O7iTfJNurYCRp41RNAkhWbATcBW0VnkbR+T+qs/OWKRafesmPn1gOiswzI9SxbnUSHGLRGThRlnt4H/EN0DknVft7dadffn/jwAa+ffPuPG7Lw/kJ0gGFoZFH0nRUdQNLG+dbMfvs8dWL57h+YesV313Q3+VV0nnkY+aWro9DIo6e1kqz4LvDM6BySNt5iJu87aeGKS1+64KJ9Ox22iM4zCxexbPXS6BDD0OSJApwqpNrpLbyPPWS/iTOnarbw/kR0gGFpelGcB42+DE9qrFVsuc1LJ9+z5AWT+cobu9t+PzrPBtxGg6+2bHRRlHl6D3BudA5Jc3d1d+c6LLw/w7LVE9EhhqXRRdHn8ZPUAOssvC8ew4V3o7/PNHqZvVaSFd8Hnh6dQ9JgLGbyvpMXrvjeSxZc9LQxWHg3dom9VhsmCmh420ttM8Gizf5izbFL9584c3IMFt6NXWKv1Zai+Bw+KFBqnNvYctvghfctNHiJvVYriqLM0/+lVxaSGmjdhff/dje7aoS/9OlNXmKv1Yqi6DsdaP5CRmqxb83st88eE8t3y6dePoo7vO8EPjLkX2MstKYoyjy9kga9w1bS+nQ6H58+/KA9JlZs88XpJd/udrlzSL/Qx1i2uhX3abWmKPpOjg4gaTQmWLTZO6aOPWT/iTMnL5t50qAX3vcCfz/Azxtrrbg8dl1JVvwb8MLoHJJG68mdG365YtGpt+zQuW0QjzT/KMtWHzeAz6mFtk0UACdFB5A0eld3d9714IkzDjh68s/nu/CeAk4ZVK46aN1EAZBkxTeA50fnkBSl2z12wb9d8o6F5+2ysDOzwyz/4U+zbPWRw0g1rto4UQAsiw4gKdL9C+9/nn7WhbNYeM8A+TCTjaNWThQASVZ8FUijc0iKtw2rb/3Eor+/cr/Ozw/qdFhY8R/9J5atfvnIgo2Jtk4UAO/C+yok0bvD+yWTy5YcNpnf8KvuNuu7w3uK3veN1mntRAGQZMXngNb96UBStedtctmPT9v0Y4sf0blv93X+9hksW/1/w0IFavNEAfDXUJu3Z0kakW/O7L/PHhPLd/vg1MsuXtPd5CZ6z4pr7RWTrZ4oAJKsOAs4OjqHpPG0mMn7/nzhF0845uRzz4jOEqXtEwXA3wB3R4eQNJ4mWHTzB9a88pPROSK1vijKPP0VcGJ0Dklj64QyT++LDhGp9UXRdwZweXQISWPnO2WenhcdIppFAZR5Og0cA0xHZ5E0NmaA46NDjAOLoq/M08tpybPlJW2Us8s8/VF0iHFgUTzQu4Ebo0NICreKlt5c93AsinWUeXo38JboHJLCHVfm6c3RIcZF6++jeDhJVnwZ+KPoHJJCfKHM0yOiQ4wTJ4qHdxy9OzEltcvNwJuiQ4wbi+JhlHl6I95bIbXR0WWe3hodYtxYFOt3BnBZdAhJI/PpMk//LTrEOLIo1qPM0xm8t0Jqi5V4z8R6WRQVyjz9IfDe6ByShqoLvK7M09XRQcaVRbFhJwP/ER1C0tCcWebpf0aHGGcWxQb0j6BeDfwqOoukgbsGOCE6xLizKDZCmae3AC/DlxxJTTIDHFnm6f9GBxl3FsVGKvP0YuCvonNIGpgP9X9fawMsitn5W+Ar0SEkzdsF+Ae/jeYjPGYpyYqtgB8CSXAUSXNzHXBAmae3RQepCyeKWSrz9HbgCGAyOoukWbsLONySmB2LYg7KPP0B8I7oHJJmZQZ4VZmnV0QHqRuLYo7KPD0DaP0rEqUaOdFHdMyNRTE/bwB+ER1C0gZ9rszT90eHqCuLYh7KPL0LSIFborNIWq/LgNdHh6gzr3oagCQr9qN3ud2jorNIeoDfAPuXeeqTFebBiWIAyjy9HHgRXgkljZMJ4EWWxPxZFANS5um36D0TaiY6iyQA3ljm6aXRIZrAohigMk+/ALwlOock3lfm6WeiQzSFRTFgZZ5+DN9hIUX62zJP3x0doklcZg9JkhVnAsdG55Ba5rQyT98WHaJpnCiG583AF6NDSC1yhiUxHE4UQ5RkxWLga8BzorNIDfexMk/fHB2iqZwohqjM0wngj+k9bVbScJwFHBcdosksiiHr3719KJaFNAzLgWPLPPVoZIgsihEo8/RW4NnAf0VnkRrkHHr3SlgSQ2ZRjEiZp3fSmyy+EZ1FaoDPAq8v89QbXEfAohihMk/vAQ4H/iU6i1RjnwOOtCRGx6IYsTJPJ+m9Ie/T0VmkGloOvKbM0+noIG3i5bFBkqzoAB8A3hmdRaqJd5d5+r7oEG1kUQRLsuI44HSc7qT1mQReV+bpudFB2sqiGANJVrwYOBfYLDqLNGbuoPeo8G9HB2kzi2JMJFlxMPAVYOvoLNKYuB74wzJPr4wO0nYed4yJMk8vBg4GfhadRRoDFwFPtyTGg0UxRso8/RnwdODz0VmkQGcBzy3z1HfRjwmPnsZUkhVvAv4OWBydRRqRNcBbyzz9aHQQPZBFMcaSrNgP+AKwa3QWachuA15a5ukF0UH0UB49jbEyTy8H9qW35Jaa6iJgP0tifFkUY67M0zvoPar8BHqjudQUk0AGPLvM0+ujw2j9PHqqkSQrnkVv0b19dBZpnq4EXl3m6Y+ig2jDnChqpMzT7wBPA74VnUWaoy5wBr2jJkuiJpwoaijJik2AvwbeBSwMjiNtrF8DR5V56qP2a8aiqLEkK/amd835AdFZpA34F3ovGbotOohmz6Kouf508SbgfcAWwXGkB7sLOL7M07Ojg2juLIqGSLJiB+DDwIujs0h936X37ojrooNofiyKhkmy4nDgI8BO0VnUWr+ltz9b4VvomsGiaKAkKx4JnAy8Ba9s0+hM0ptqT+q/I14NYVE0WJIV+9Nbdj8tOosa7yvA28s8vSY6iAbPomi4JCsWAG8F/gZ4RHAcNc8VwNvKPP1mdBANj0XREklWPBb4S+AYfCKt5m8VvXt5Pl7m6XR0GA2XRdEy/auj3gW8HlgUHEf1swY4E1hW5umq6DAaDYuipZKs2AV4N3Ak3t2tjfPv9PYQvnWuZSyKlkuy4vHAicBrgAXBcTR+poF/Bk7pP/ZeLWRRCIAkK54IvAd4BV5SK7gXOBv4kDfMyaLQAyRZsTuwDHgp0IlNowC3AR8Fzijz9NboMBoPFoUeVpIVewDH0TuS8rLa5ivpvaN9eZmn9wRn0ZixKFQpyYotgD+l9+DB3YLjaPB+BJwKnOdlrlofi0IbLcmKPwDeDPwfvFKqztYAX6d3vOSNctogi0Kz1r9577XAUThl1MlPgE8D55Z5+tvoMKoPi0LzkmTFQcDrgCOARwXH0UPdApwLfLrM0x9Hh1E9WRQaiCQrHgH8MXA4cCiwZWyiVrsd+BLwT8D5ZZ6uCc6jmrMoNHBJVmwKPAt4Ib19xu/FJmqF1cC/0iuHb5Z5OhWcRw1iUWjokqx4MveXxsG4CB+EaeAy4ALgfOCiMk8nYiOpqSwKjVSSFVsBL6BXHIcBW8Umqo0uvWX0+fTK4SJfDqRRsSgUpv+ujAP7X/v3v56Ad4SvdRW9YjgfuLDM09uC86ilLAqNlSQrtgT24/7i2B/YNTTUaNwKXEnvRUDfAS4o8/Q3sZGkHotCYy/Jiq15aHnsHBpq7m6iVwhX0psYrgSu9LlKGmcWhWopyYrNgR0f5mundX68LaM/xrqH3tvfVgE38sBCuKrM09UjziPNm0WhxkqyYjGwA/eXx/b0bgrcvP+12YN+vAm9Yln717VfM/TuTVi1ztdtD/r5KmBVmaf3jebfThodi0KSVMkX1EiSKlkUkqRKFoUkqZJFIUmqZFFIkipZFJKkShaFJKmSRSFJqmRRSJIqWRSSpEoWhSSpkkUhSapkUUiSKlkUkqRKFoUkqZJFIUmqZFFIkipZFJKkShaFJKmSRSFJqmRRSJIqWRSSpEoWhSSpkkUhSapkUUiSKlkUkqRKFoUkqZJFIUmqZFFIkipZFJKkShaFJKmSRSFJqmRRSJIqWRSSpEoWhSSpkkUhSapkUUiSKlkUkqRKFoUkqZJFIUmqZFFIkipZFJKkShaFJKmSRSFJqvT/AMj9a2f6mIxNAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        " \n",
        " \n",
        "# Creating dataset\n",
        "target = ['0', '1']\n",
        " \n",
        "data = [169611,14895]\n",
        " \n",
        "# Creating plot\n",
        "fig = plt.figure(figsize =(10, 7))\n",
        "plt.pie(data, labels = target)\n",
        " \n",
        "# show plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xe9lsRk7XhnJ",
        "outputId": "1c6f8d19-8249-4ad8-ad0f-ebfc8b7317ea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "184506 123005 226 225\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "print(dataFrame.shape[0],dataFrame_test.shape[0],dataFrame.shape[1],dataFrame_test.shape[1])\n",
        "\n",
        "\n",
        "x_train=dataFrame.loc[:, dataFrame.columns != 'TARGET']\n",
        "\n",
        "y_train=dataFrame['TARGET']\n",
        "\n",
        "x_test=dataFrame_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e9x7jyC9eX-b",
        "outputId": "c61c7f11-23d8-412f-e4fa-cd98369499ed"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: imblearn in /usr/local/lib/python3.8/dist-packages (0.0)\n",
            "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.8/dist-packages (from imblearn) (0.8.1)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.8/dist-packages (from imbalanced-learn->imblearn) (1.21.6)\n",
            "Requirement already satisfied: scikit-learn>=0.24 in /usr/local/lib/python3.8/dist-packages (from imbalanced-learn->imblearn) (1.0.2)\n",
            "Requirement already satisfied: scipy>=0.19.1 in /usr/local/lib/python3.8/dist-packages (from imbalanced-learn->imblearn) (1.7.3)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.8/dist-packages (from imbalanced-learn->imblearn) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.24->imbalanced-learn->imblearn) (3.1.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install imblearn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TarSXPNTR1DC",
        "outputId": "b6408caf-f930-4a59-92a7-f6511cf7c9c2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "After OverSampling, the shape of train_X: (339222, 225)\n",
            "After OverSampling, the shape of train_y: (339222,) \n",
            "\n",
            "After OverSampling, counts of label '1': 169611\n",
            "After OverSampling, counts of label '0': 169611\n"
          ]
        }
      ],
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "sm = SMOTE(random_state = 2)\n",
        "\n",
        "x_train_res, y_train_res = sm.fit_resample(x_train, y_train.ravel())\n",
        "print('After OverSampling, the shape of train_X: {}'.format(x_train_res.shape))\n",
        "print('After OverSampling, the shape of train_y: {} \\n'.format(y_train_res.shape))\n",
        "  \n",
        "print(\"After OverSampling, counts of label '1': {}\".format(sum(y_train_res == 1)))\n",
        "print(\"After OverSampling, counts of label '0': {}\".format(sum(y_train_res == 0)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "w3qfgJWVjjWO"
      },
      "outputs": [],
      "source": [
        "x_train, x_val, y_train, y_val = train_test_split(x_train_res, y_train_res, test_size = 0.20, random_state = 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V-gKudG8j1dT",
        "outputId": "08958730-b328-462a-f5a1-9b4a2521d837"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 100 rounds.\n",
            "[200]\tvalid_0's binary_logloss: 0.339604\tvalid_0's auc: 0.96114\n",
            "[400]\tvalid_0's binary_logloss: 0.229249\tvalid_0's auc: 0.972459\n",
            "[600]\tvalid_0's binary_logloss: 0.182281\tvalid_0's auc: 0.975361\n",
            "[800]\tvalid_0's binary_logloss: 0.162228\tvalid_0's auc: 0.976565\n",
            "[1000]\tvalid_0's binary_logloss: 0.153036\tvalid_0's auc: 0.977208\n",
            "[1200]\tvalid_0's binary_logloss: 0.147623\tvalid_0's auc: 0.977681\n",
            "[1400]\tvalid_0's binary_logloss: 0.14414\tvalid_0's auc: 0.978072\n",
            "[1600]\tvalid_0's binary_logloss: 0.141891\tvalid_0's auc: 0.97835\n",
            "[1800]\tvalid_0's binary_logloss: 0.140401\tvalid_0's auc: 0.978537\n",
            "[2000]\tvalid_0's binary_logloss: 0.1394\tvalid_0's auc: 0.978661\n",
            "Did not meet early stopping. Best iteration is:\n",
            "[1999]\tvalid_0's binary_logloss: 0.139399\tvalid_0's auc: 0.978662\n",
            "The no. of features selected: 125\n"
          ]
        }
      ],
      "source": [
        "from lightgbm import LGBMClassifier\n",
        "from sklearn.feature_selection import SelectFromModel\n",
        "lgbc=LGBMClassifier(n_estimators=500, learning_rate=0.05,\n",
        "                    num_leaves=32, colsample_bytree=0.2,                                           \n",
        "                    reg_alpha=3, reg_lambda=1, min_split_gain=0.01,    \n",
        "                    min_child_weight=40)\n",
        "embeded_lgb_selector = SelectFromModel(lgbc, max_features=10)\n",
        "embeded_lgb_selector.fit(x_train, y_train)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "model_sk = LGBMClassifier(boosting_type='gbdt', max_depth=7, learning_rate=0.01, n_estimators= 2000, \n",
        "                 class_weight='balanced', subsample=0.9, colsample_bytree= 0.8, n_jobs=-1)\n",
        "train_features, valid_features, train_y, valid_y = train_test_split(x_train_res, y_train_res, test_size = 0.20, random_state = 42)\n",
        "model_sk.fit(train_features, train_y, early_stopping_rounds=100, eval_set = [(valid_features, valid_y)], eval_metric = 'auc', verbose = 200)\n",
        "\n",
        "\n",
        "\n",
        "feature_imp = pd.DataFrame(sorted(zip(model_sk.feature_importances_, x_train.columns)), columns=['Value','Feature'])\n",
        "features_df = feature_imp.sort_values(by=\"Value\", ascending=False)\n",
        "selected_features = list(features_df[features_df['Value']>=50]['Feature'])\n",
        "# Saving the selected features into pickle file\n",
        "# with open('select_features.txt','wb') as fp:\n",
        "#     pickle.dump(selected_features, fp)\n",
        "print('The no. of features selected:',len(selected_features))\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "2XKt724dyoHl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e945bd9a-fc8c-451b-8a07-41492c5e854b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Features selected: ['EXT_SOURCE_3', 'AMT_REQ_CREDIT_BUREAU_YEAR', 'EXT_SOURCE_2', 'HOUR_APPR_PROCESS_START', 'EXT_SOURCE_1', 'AMT_GOODS_PRICE', 'DAYS_BIRTH', 'AMT_INCOME_TOTAL', 'OBS_30_CNT_SOCIAL_CIRCLE', 'OBS_60_CNT_SOCIAL_CIRCLE', 'AMT_ANNUITY', 'AMT_CREDIT', 'CNT_CHILDREN', 'REGION_POPULATION_RELATIVE', 'DAYS_LAST_PHONE_CHANGE', 'DAYS_ID_PUBLISH', 'AMT_REQ_CREDIT_BUREAU_MON', 'DAYS_EMPLOYED', 'AMT_REQ_CREDIT_BUREAU_QRT', 'DEF_30_CNT_SOCIAL_CIRCLE', 'DAYS_REGISTRATION', 'SK_ID_CURR', 'REGION_RATING_CLIENT_W_CITY', 'DEF_60_CNT_SOCIAL_CIRCLE', 'FLAG_WORK_PHONE', 'CODE_GENDER_F', 'FLAG_OWN_CAR_N', 'ENTRANCES_MODE', 'NAME_EDUCATION_TYPE_Secondary / secondary special', 'FLOORSMAX_MODE', 'REGION_RATING_CLIENT', 'NAME_FAMILY_STATUS_Married', 'FLAG_DOCUMENT_3', 'ORGANIZATION_TYPE_Self-employed', 'TOTALAREA_MODE', 'ORGANIZATION_TYPE_Business Entity Type 3', 'LANDAREA_AVG', 'NAME_INCOME_TYPE_Working', 'OCCUPATION_TYPE_Laborers', 'BASEMENTAREA_MODE', 'FLAG_PHONE', 'NAME_CONTRACT_TYPE_Cash loans', 'LANDAREA_MODE', 'CODE_GENDER_M', 'YEARS_BEGINEXPLUATATION_MODE', 'CNT_FAM_MEMBERS', 'YEARS_BEGINEXPLUATATION_MEDI', 'NONLIVINGAREA_MEDI', 'NONLIVINGAREA_MODE', 'OCCUPATION_TYPE_Drivers', 'NAME_EDUCATION_TYPE_Lower secondary', 'LIVINGAREA_MODE', 'YEARS_BEGINEXPLUATATION_AVG', 'ORGANIZATION_TYPE_Construction', 'WEEKDAY_APPR_PROCESS_START_TUESDAY', 'FLOORSMAX_MEDI', 'REG_CITY_NOT_LIVE_CITY', 'APARTMENTS_AVG', 'APARTMENTS_MODE', 'LIVINGAREA_AVG', 'BASEMENTAREA_AVG', 'WEEKDAY_APPR_PROCESS_START_WEDNESDAY', 'WEEKDAY_APPR_PROCESS_START_SATURDAY', 'OCCUPATION_TYPE_Core staff', 'NAME_EDUCATION_TYPE_Higher education', 'WEEKDAY_APPR_PROCESS_START_MONDAY', 'ENTRANCES_MEDI', 'ENTRANCES_AVG', 'NONLIVINGAREA_AVG', 'FLOORSMAX_AVG', 'OCCUPATION_TYPE_Low-skill Laborers', 'AMT_REQ_CREDIT_BUREAU_WEEK', 'APARTMENTS_MEDI', 'LANDAREA_MEDI', 'BASEMENTAREA_MEDI', 'ORGANIZATION_TYPE_Transport: type 3', 'ORGANIZATION_TYPE_Military', 'NAME_FAMILY_STATUS_Separated', 'ELEVATORS_MODE', 'NAME_TYPE_SUITE_Unaccompanied', 'FLAG_OWN_CAR_Y', 'WEEKDAY_APPR_PROCESS_START_SUNDAY', 'FLAG_EMAIL', 'WEEKDAY_APPR_PROCESS_START_THURSDAY', 'NAME_FAMILY_STATUS_Single / not married', 'FLAG_OWN_REALTY_N', 'NAME_FAMILY_STATUS_Widow', 'FLAG_DOCUMENT_18', 'FLAG_DOCUMENT_16', 'LIVINGAREA_MEDI', 'WEEKDAY_APPR_PROCESS_START_FRIDAY', 'NAME_EDUCATION_TYPE_Incomplete higher', 'EMERGENCYSTATE_MODE_No', 'NAME_INCOME_TYPE_Unemployed', 'ORGANIZATION_TYPE_Realtor', 'LIVE_CITY_NOT_WORK_CITY', 'NAME_HOUSING_TYPE_Rented apartment', 'FLAG_DOCUMENT_13', 'ORGANIZATION_TYPE_Trade: type 3', 'NAME_INCOME_TYPE_State servant', 'NAME_HOUSING_TYPE_House / apartment', 'NAME_TYPE_SUITE_Other_B', 'NAME_HOUSING_TYPE_With parents', 'ORGANIZATION_TYPE_Industry: type 11', 'WALLSMATERIAL_MODE_Stone, brick', 'OCCUPATION_TYPE_High skill tech staff', 'NAME_CONTRACT_TYPE_Revolving loans', 'NAME_FAMILY_STATUS_Civil marriage', 'FLAG_DOCUMENT_6', 'ORGANIZATION_TYPE_Restaurant', 'OCCUPATION_TYPE_Managers', 'ORGANIZATION_TYPE_Postal', 'FLAG_DOCUMENT_11', 'ORGANIZATION_TYPE_Industry: type 9', 'REG_CITY_NOT_WORK_CITY', 'ORGANIZATION_TYPE_University', 'OCCUPATION_TYPE_Medicine staff', 'ORGANIZATION_TYPE_Transport: type 4', 'ELEVATORS_AVG', 'FLAG_OWN_REALTY_Y', 'FLAG_DOCUMENT_14', 'NAME_INCOME_TYPE_Commercial associate', 'NAME_TYPE_SUITE_Children', 'ORGANIZATION_TYPE_Business Entity Type 2', 'NAME_TYPE_SUITE_Spouse, partner']\n",
            "(67845, 125) (67845,)\n",
            "(123005, 125)\n",
            "(271377, 125) (271377,)\n"
          ]
        }
      ],
      "source": [
        "print('Features selected:',selected_features)\n",
        "\n",
        "x_train = pd.DataFrame(x_train, columns=selected_features)\n",
        "\n",
        "x_val = pd.DataFrame(x_val, columns=selected_features)\n",
        "\n",
        "\n",
        "x_test = pd.DataFrame(x_test, columns=selected_features)\n",
        "\n",
        "x_test_first=x_test['SK_ID_CURR'].to_numpy()\n",
        "\n",
        "\n",
        "print(x_val.shape,y_val.shape)\n",
        "print(x_test.shape)\n",
        "print(x_train.shape,y_train.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "JKBf3je7hH_M",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "outputId": "5bac1478-f418-4e57-8f76-09ce62668a63"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        EXT_SOURCE_3  AMT_REQ_CREDIT_BUREAU_YEAR  EXT_SOURCE_2  \\\n",
              "203447      0.458509                    0.012998      0.498112   \n",
              "208993      0.489113                    0.051311      0.340699   \n",
              "295619      0.232504                    0.049927      0.184592   \n",
              "263231      0.481253                    0.075998      0.235129   \n",
              "61594       0.717267                    0.080000      0.676299   \n",
              "...              ...                         ...           ...   \n",
              "122579      0.776279                    0.280000      0.159526   \n",
              "304137      0.598522                    0.024347      0.510040   \n",
              "152315      0.310436                    0.000000      0.471992   \n",
              "117952      0.220435                    0.120000      0.805534   \n",
              "305711      0.302922                    0.046155      0.434316   \n",
              "\n",
              "        HOUR_APPR_PROCESS_START  EXT_SOURCE_1  AMT_GOODS_PRICE  DAYS_BIRTH  \\\n",
              "203447                 0.441847      0.511566         0.134955    0.690310   \n",
              "208993                 0.385604      0.411882         0.053835    0.950335   \n",
              "295619                 0.587115      0.519049         0.242628    0.677968   \n",
              "263231                 0.404354      0.519049         0.147020    0.631556   \n",
              "61594                  0.434783      0.519049         0.068462    0.154302   \n",
              "...                         ...           ...              ...         ...   \n",
              "122579                 0.391304      0.519049         0.221100    0.120596   \n",
              "304137                 0.548203      0.321397         0.102132    0.714934   \n",
              "152315                 0.695652      0.519049         0.102132    0.920732   \n",
              "117952                 0.521739      0.224705         0.046016    0.725045   \n",
              "305711                 0.518403      0.181142         0.051197    0.700524   \n",
              "\n",
              "        AMT_INCOME_TOTAL  OBS_30_CNT_SOCIAL_CIRCLE  OBS_60_CNT_SOCIAL_CIRCLE  \\\n",
              "203447          0.001378                  0.000000                  0.000000   \n",
              "208993          0.000545                  0.000813                  0.000822   \n",
              "295619          0.001557                  0.003566                  0.003607   \n",
              "263231          0.001604                  0.000000                  0.000000   \n",
              "61594           0.000739                  0.002874                  0.002907   \n",
              "...                  ...                       ...                       ...   \n",
              "122579          0.001162                  0.000000                  0.000000   \n",
              "304137          0.001222                  0.014743                  0.014915   \n",
              "152315          0.001123                  0.017241                  0.017442   \n",
              "117952          0.001700                  0.005747                  0.002907   \n",
              "305711          0.000961                  0.000442                  0.000447   \n",
              "\n",
              "        ...  ORGANIZATION_TYPE_University  OCCUPATION_TYPE_Medicine staff  \\\n",
              "203447  ...                           0.0                             0.0   \n",
              "208993  ...                           0.0                             0.0   \n",
              "295619  ...                           0.0                             0.0   \n",
              "263231  ...                           0.0                             0.0   \n",
              "61594   ...                           0.0                             0.0   \n",
              "...     ...                           ...                             ...   \n",
              "122579  ...                           0.0                             0.0   \n",
              "304137  ...                           0.0                             0.0   \n",
              "152315  ...                           0.0                             0.0   \n",
              "117952  ...                           0.0                             0.0   \n",
              "305711  ...                           0.0                             0.0   \n",
              "\n",
              "        ORGANIZATION_TYPE_Transport: type 4  ELEVATORS_AVG  FLAG_OWN_REALTY_Y  \\\n",
              "203447                                  0.0       0.000000           1.000000   \n",
              "208993                                  0.0       0.000000           0.282779   \n",
              "295619                                  0.0       0.000000           0.000000   \n",
              "263231                                  0.0       0.004002           0.000000   \n",
              "61594                                   0.0       0.000000           1.000000   \n",
              "...                                     ...            ...                ...   \n",
              "122579                                  0.0       0.000000           1.000000   \n",
              "304137                                  0.0       0.125227           1.000000   \n",
              "152315                                  0.0       0.000000           1.000000   \n",
              "117952                                  0.0       0.000000           1.000000   \n",
              "305711                                  0.0       0.000000           0.153878   \n",
              "\n",
              "        FLAG_DOCUMENT_14  NAME_INCOME_TYPE_Commercial associate  \\\n",
              "203447               0.0                               0.000000   \n",
              "208993               0.0                               0.000000   \n",
              "295619               0.0                               0.248181   \n",
              "263231               0.0                               0.000000   \n",
              "61594                0.0                               0.000000   \n",
              "...                  ...                                    ...   \n",
              "122579               0.0                               0.000000   \n",
              "304137               0.0                               0.000000   \n",
              "152315               0.0                               0.000000   \n",
              "117952               0.0                               1.000000   \n",
              "305711               0.0                               0.000000   \n",
              "\n",
              "        NAME_TYPE_SUITE_Children  ORGANIZATION_TYPE_Business Entity Type 2  \\\n",
              "203447                       0.0                                       0.0   \n",
              "208993                       0.0                                       0.0   \n",
              "295619                       0.0                                       0.0   \n",
              "263231                       0.0                                       0.0   \n",
              "61594                        0.0                                       0.0   \n",
              "...                          ...                                       ...   \n",
              "122579                       0.0                                       0.0   \n",
              "304137                       0.0                                       0.0   \n",
              "152315                       0.0                                       0.0   \n",
              "117952                       0.0                                       0.0   \n",
              "305711                       0.0                                       0.0   \n",
              "\n",
              "        NAME_TYPE_SUITE_Spouse, partner  \n",
              "203447                         0.000000  \n",
              "208993                         0.000000  \n",
              "295619                         0.000000  \n",
              "263231                         0.000000  \n",
              "61594                          0.000000  \n",
              "...                                 ...  \n",
              "122579                         0.000000  \n",
              "304137                         0.608666  \n",
              "152315                         0.000000  \n",
              "117952                         0.000000  \n",
              "305711                         0.000000  \n",
              "\n",
              "[271377 rows x 125 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8eee0685-3954-4857-8246-61ba7cd36cda\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\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>EXT_SOURCE_3</th>\n",
              "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
              "      <th>EXT_SOURCE_2</th>\n",
              "      <th>HOUR_APPR_PROCESS_START</th>\n",
              "      <th>EXT_SOURCE_1</th>\n",
              "      <th>AMT_GOODS_PRICE</th>\n",
              "      <th>DAYS_BIRTH</th>\n",
              "      <th>AMT_INCOME_TOTAL</th>\n",
              "      <th>OBS_30_CNT_SOCIAL_CIRCLE</th>\n",
              "      <th>OBS_60_CNT_SOCIAL_CIRCLE</th>\n",
              "      <th>...</th>\n",
              "      <th>ORGANIZATION_TYPE_University</th>\n",
              "      <th>OCCUPATION_TYPE_Medicine staff</th>\n",
              "      <th>ORGANIZATION_TYPE_Transport: type 4</th>\n",
              "      <th>ELEVATORS_AVG</th>\n",
              "      <th>FLAG_OWN_REALTY_Y</th>\n",
              "      <th>FLAG_DOCUMENT_14</th>\n",
              "      <th>NAME_INCOME_TYPE_Commercial associate</th>\n",
              "      <th>NAME_TYPE_SUITE_Children</th>\n",
              "      <th>ORGANIZATION_TYPE_Business Entity Type 2</th>\n",
              "      <th>NAME_TYPE_SUITE_Spouse, partner</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>203447</th>\n",
              "      <td>0.458509</td>\n",
              "      <td>0.012998</td>\n",
              "      <td>0.498112</td>\n",
              "      <td>0.441847</td>\n",
              "      <td>0.511566</td>\n",
              "      <td>0.134955</td>\n",
              "      <td>0.690310</td>\n",
              "      <td>0.001378</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.000000</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",
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              "    <tr>\n",
              "      <th>122579</th>\n",
              "      <td>0.776279</td>\n",
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              "    <tr>\n",
              "      <th>304137</th>\n",
              "      <td>0.598522</td>\n",
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              "    <tr>\n",
              "      <th>152315</th>\n",
              "      <td>0.310436</td>\n",
              "      <td>0.000000</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>117952</th>\n",
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              "    <tr>\n",
              "      <th>305711</th>\n",
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              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>271377 rows × 125 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8eee0685-3954-4857-8246-61ba7cd36cda')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8eee0685-3954-4857-8246-61ba7cd36cda button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8eee0685-3954-4857-8246-61ba7cd36cda');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "x_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "OIYIYndSY2E5"
      },
      "outputs": [],
      "source": [
        "#Function to compute accuracy\n",
        "def accuracy(pred,test_data):\n",
        "  correct_pred=0\n",
        "  for i in range(len(pred)):\n",
        "    if pred[i]==test_data[i]:\n",
        "      correct_pred+=1\n",
        "  acc=(correct_pred/len(pred))*100\n",
        "  return acc"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Wm-gqAbrx9P"
      },
      "source": [
        "##**Logistic Regression**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "7S4rOQgjhhoF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eaef2981-7f13-4763-ce3b-fb9a6905c813"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy:  70.55788930650748\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "model = LogisticRegression()\n",
        "model=model.fit(x_train, y_train)\n",
        "predicted_val_value = model.predict(x_val)\n",
        "print(\"Training Accuracy: \",accuracy(predicted_val_value,y_val))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "n9DkmVyrgZpl"
      },
      "outputs": [],
      "source": [
        "predicted_test_value = model.predict(x_test)\n",
        "df1 = pd.DataFrame(data=np.column_stack((x_test_first,predicted_test_value)),columns=['SK_ID_CURR','TARGET'])\n",
        "df1.to_csv('/content/drive/MyDrive/AI511-HOMELOAN-2022/submission_logistic.csv',index=False) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "Xt4IpKCaiwnJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b1ebe5bc-aac4-41de-fd14-469d4130a9b2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    77355\n",
              "1.0    45650\n",
              "Name: TARGET, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "df1['TARGET'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SCqz03Llr_uu"
      },
      "source": [
        "##**Decision Trees**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "VWAj8lpQoGMy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7dd40cae-68e2-4fc0-939e-8fac2582e71b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy:  90.3132139435478\n"
          ]
        }
      ],
      "source": [
        "from sklearn.datasets import make_classification\n",
        "from sklearn import tree\n",
        " \n",
        "X, t = make_classification(1000, 5, n_classes=2, shuffle=True, random_state=10)\n",
        " \n",
        "model = tree.DecisionTreeClassifier()\n",
        "model = model.fit(x_train, y_train)\n",
        " \n",
        "predicted_val_value = model.predict(x_val)\n",
        "print(\"Training Accuracy: \",accuracy(predicted_val_value,y_val))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "qb_fI04RmPCy"
      },
      "outputs": [],
      "source": [
        "predicted_test_value = model.predict(x_test)\n",
        "df2 = pd.DataFrame(data=np.column_stack((x_test_first,predicted_test_value)),columns=['SK_ID_CURR','TARGET'])\n",
        "df2.to_csv('/content/drive/MyDrive/AI511-HOMELOAN-2022/submission_tree.csv',index=False) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "JzDuxs0Viqa_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dfa92353-22ca-45cb-af4f-5718d6f290a2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0    63609\n",
              "0.0    59396\n",
              "Name: TARGET, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "df2['TARGET'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E8XWOFdEIoWH"
      },
      "source": [
        "##**Random Forest**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "iQCVURj8IlpW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e7fe2327-3b23-4be3-e30c-b91c8ee5142a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy:  95.51772422433487\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier  \n",
        "model= RandomForestClassifier(n_estimators= 50, criterion=\"gini\",max_features='sqrt',bootstrap=True,)  \n",
        "model.fit(x_train, y_train)  \n",
        "RandomForestClassifier(...)\n",
        "predicted_val_value= model.predict(x_val)  \n",
        "\n",
        "print(\"Training Accuracy: \",accuracy(predicted_val_value,y_val))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "YZnk7MsqKczW"
      },
      "outputs": [],
      "source": [
        "predicted_test_value = model.predict(x_test)\n",
        "df3 = pd.DataFrame(data=np.column_stack((x_test_first,predicted_test_value)),columns=['SK_ID_CURR','TARGET'])\n",
        "df3.to_csv('/content/drive/MyDrive/AI511-HOMELOAN-2022/submission_forest.csv',index=False) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "LRfN3AOwia24",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a77b4f06-6361-4515-e971-afd09f8f4179"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    117433\n",
              "1.0      5572\n",
              "Name: TARGET, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "df3['TARGET'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P9jW82VfF5UO"
      },
      "source": [
        "##**LGBM Classifier**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "-mwI3yYpxkEJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d2b5b34-0a8c-4625-c6d8-27f78ec70487"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy:  91.5837570933746\n"
          ]
        }
      ],
      "source": [
        "import lightgbm as lgb\n",
        "model = lgb.LGBMClassifier(boosting_type= 'dart',\n",
        "          max_depth= 10,\n",
        "          \n",
        "          nthread= 5,\n",
        "          num_leaves= 32,\n",
        "          learning_rate= 0.05,\n",
        "          max_bin= 512,\n",
        "          subsample_for_bin= 200,\n",
        "          subsample= 0.7,\n",
        "          subsample_freq= 1,\n",
        "          colsample_bytree= 0.8,\n",
        "          reg_alpha= 20,\n",
        "          reg_lambda= 20,\n",
        "          min_split_gain= 0.5,\n",
        "          min_child_weight= 1,\n",
        "          min_child_samples= 10,\n",
        "          scale_pos_weigh= 1,\n",
        "          num_class = 1,\n",
        "          metric = 'auc')\n",
        "model.fit(x_train,y_train,verbose=20,eval_metric='logloss')\n",
        "\n",
        "\n",
        "predicted_val_value= model.predict(x_val)  \n",
        "print(\"Training Accuracy: \",accuracy(predicted_val_value,y_val))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "wxu-DANp9N8b"
      },
      "outputs": [],
      "source": [
        "predicted_test_value = model.predict(x_test)\n",
        "df5 = pd.DataFrame(data=np.column_stack((x_test_first,predicted_test_value)),columns=['SK_ID_CURR','TARGET'])\n",
        "df5.to_csv('/content/drive/MyDrive/AI511-HOMELOAN-2022/submission_lgbm.csv',index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "mvhb25LPqwaq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "01217f40-56d3-4a28-fdec-fa2526681ec5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    71750\n",
              "1.0    51255\n",
              "Name: TARGET, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "df5['TARGET'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vRDlQutSq3u2"
      },
      "source": [
        "LGBM 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "wzl-2Nmuq3Ag",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03e662c1-0f81-4e8a-adb9-75cedd05d27b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 100 rounds.\n",
            "[10]\tvalid_0's auc: 0.917786\n",
            "[20]\tvalid_0's auc: 0.930398\n",
            "[30]\tvalid_0's auc: 0.944136\n",
            "[40]\tvalid_0's auc: 0.955024\n",
            "[50]\tvalid_0's auc: 0.960595\n",
            "[60]\tvalid_0's auc: 0.964821\n",
            "[70]\tvalid_0's auc: 0.967456\n",
            "[80]\tvalid_0's auc: 0.969059\n",
            "[90]\tvalid_0's auc: 0.970448\n",
            "[100]\tvalid_0's auc: 0.971496\n",
            "[110]\tvalid_0's auc: 0.972351\n",
            "[120]\tvalid_0's auc: 0.972943\n",
            "[130]\tvalid_0's auc: 0.973486\n",
            "[140]\tvalid_0's auc: 0.973919\n",
            "[150]\tvalid_0's auc: 0.974291\n",
            "[160]\tvalid_0's auc: 0.97459\n",
            "[170]\tvalid_0's auc: 0.974881\n",
            "[180]\tvalid_0's auc: 0.975132\n",
            "[190]\tvalid_0's auc: 0.9754\n",
            "[200]\tvalid_0's auc: 0.975658\n",
            "[210]\tvalid_0's auc: 0.975878\n",
            "[220]\tvalid_0's auc: 0.976076\n",
            "[230]\tvalid_0's auc: 0.976255\n",
            "[240]\tvalid_0's auc: 0.976411\n",
            "[250]\tvalid_0's auc: 0.976554\n",
            "[260]\tvalid_0's auc: 0.976706\n",
            "[270]\tvalid_0's auc: 0.976836\n",
            "[280]\tvalid_0's auc: 0.976973\n",
            "[290]\tvalid_0's auc: 0.977088\n",
            "[300]\tvalid_0's auc: 0.977181\n",
            "[310]\tvalid_0's auc: 0.977273\n",
            "[320]\tvalid_0's auc: 0.977385\n",
            "[330]\tvalid_0's auc: 0.977457\n",
            "[340]\tvalid_0's auc: 0.977523\n",
            "[350]\tvalid_0's auc: 0.977578\n",
            "[360]\tvalid_0's auc: 0.977654\n",
            "[370]\tvalid_0's auc: 0.977719\n",
            "[380]\tvalid_0's auc: 0.977797\n",
            "[390]\tvalid_0's auc: 0.977831\n",
            "[400]\tvalid_0's auc: 0.977885\n",
            "[410]\tvalid_0's auc: 0.977928\n",
            "[420]\tvalid_0's auc: 0.977976\n",
            "[430]\tvalid_0's auc: 0.978017\n",
            "[440]\tvalid_0's auc: 0.978069\n",
            "[450]\tvalid_0's auc: 0.978101\n",
            "[460]\tvalid_0's auc: 0.97816\n",
            "[470]\tvalid_0's auc: 0.9782\n",
            "[480]\tvalid_0's auc: 0.978244\n",
            "[490]\tvalid_0's auc: 0.978266\n",
            "[500]\tvalid_0's auc: 0.978292\n",
            "[510]\tvalid_0's auc: 0.978332\n",
            "[520]\tvalid_0's auc: 0.978365\n",
            "[530]\tvalid_0's auc: 0.9784\n",
            "[540]\tvalid_0's auc: 0.97841\n",
            "[550]\tvalid_0's auc: 0.978447\n",
            "[560]\tvalid_0's auc: 0.978453\n",
            "[570]\tvalid_0's auc: 0.978483\n",
            "[580]\tvalid_0's auc: 0.978498\n",
            "[590]\tvalid_0's auc: 0.978502\n",
            "[600]\tvalid_0's auc: 0.978517\n",
            "[610]\tvalid_0's auc: 0.978532\n",
            "[620]\tvalid_0's auc: 0.978579\n",
            "[630]\tvalid_0's auc: 0.978588\n",
            "[640]\tvalid_0's auc: 0.978607\n",
            "[650]\tvalid_0's auc: 0.978624\n",
            "[660]\tvalid_0's auc: 0.978631\n",
            "[670]\tvalid_0's auc: 0.978649\n",
            "[680]\tvalid_0's auc: 0.978656\n",
            "[690]\tvalid_0's auc: 0.978656\n",
            "[700]\tvalid_0's auc: 0.97867\n",
            "[710]\tvalid_0's auc: 0.97868\n",
            "[720]\tvalid_0's auc: 0.978699\n",
            "[730]\tvalid_0's auc: 0.9787\n",
            "[740]\tvalid_0's auc: 0.978707\n",
            "[750]\tvalid_0's auc: 0.978716\n",
            "[760]\tvalid_0's auc: 0.97872\n",
            "[770]\tvalid_0's auc: 0.978725\n",
            "[780]\tvalid_0's auc: 0.978734\n",
            "[790]\tvalid_0's auc: 0.978739\n",
            "[800]\tvalid_0's auc: 0.978753\n",
            "[810]\tvalid_0's auc: 0.978759\n",
            "[820]\tvalid_0's auc: 0.97876\n",
            "[830]\tvalid_0's auc: 0.978762\n",
            "[840]\tvalid_0's auc: 0.978776\n",
            "[850]\tvalid_0's auc: 0.978769\n",
            "[860]\tvalid_0's auc: 0.978783\n",
            "[870]\tvalid_0's auc: 0.978793\n",
            "[880]\tvalid_0's auc: 0.978801\n",
            "[890]\tvalid_0's auc: 0.978802\n",
            "[900]\tvalid_0's auc: 0.978797\n",
            "[910]\tvalid_0's auc: 0.978796\n",
            "[920]\tvalid_0's auc: 0.978812\n",
            "[930]\tvalid_0's auc: 0.978809\n",
            "[940]\tvalid_0's auc: 0.978808\n",
            "[950]\tvalid_0's auc: 0.978806\n",
            "[960]\tvalid_0's auc: 0.978813\n",
            "[970]\tvalid_0's auc: 0.978825\n",
            "[980]\tvalid_0's auc: 0.978833\n",
            "[990]\tvalid_0's auc: 0.978833\n",
            "[1000]\tvalid_0's auc: 0.978834\n",
            "[1010]\tvalid_0's auc: 0.978841\n",
            "[1020]\tvalid_0's auc: 0.978829\n",
            "[1030]\tvalid_0's auc: 0.978842\n",
            "[1040]\tvalid_0's auc: 0.978836\n",
            "[1050]\tvalid_0's auc: 0.978833\n",
            "[1060]\tvalid_0's auc: 0.978844\n",
            "[1070]\tvalid_0's auc: 0.978837\n",
            "[1080]\tvalid_0's auc: 0.978837\n",
            "[1090]\tvalid_0's auc: 0.978846\n",
            "[1100]\tvalid_0's auc: 0.978864\n",
            "[1110]\tvalid_0's auc: 0.978877\n",
            "[1120]\tvalid_0's auc: 0.978879\n",
            "[1130]\tvalid_0's auc: 0.978878\n",
            "[1140]\tvalid_0's auc: 0.978876\n",
            "[1150]\tvalid_0's auc: 0.978886\n",
            "[1160]\tvalid_0's auc: 0.978878\n",
            "[1170]\tvalid_0's auc: 0.978868\n",
            "[1180]\tvalid_0's auc: 0.978878\n",
            "[1190]\tvalid_0's auc: 0.978882\n",
            "[1200]\tvalid_0's auc: 0.978877\n",
            "[1210]\tvalid_0's auc: 0.978877\n",
            "[1220]\tvalid_0's auc: 0.978874\n",
            "[1230]\tvalid_0's auc: 0.978873\n",
            "[1240]\tvalid_0's auc: 0.978879\n",
            "[1250]\tvalid_0's auc: 0.978885\n",
            "Early stopping, best iteration is:\n",
            "[1153]\tvalid_0's auc: 0.97889\n",
            "Training Accuracy:  91.5837570933746\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "weight = np.ones((len(x_train),), dtype=int)\n",
        "for i in range(len(x_train)):\n",
        "    if y_train[i]== 0:\n",
        "        weight[i]=1\n",
        "    else:\n",
        "        weight[i]=11\n",
        "\n",
        "train_data=lgb.Dataset(x_train[selected_features], label = y_train, weight= weight )\n",
        "valid_data=lgb.Dataset(x_val[selected_features], label = y_val)\n",
        "\n",
        "\n",
        "params = {'boosting_type': 'gbdt',\n",
        "          'max_depth' :7,\n",
        "          'objective': 'binary',\n",
        "          'nthread': 5,\n",
        "          'num_leaves': 32,\n",
        "          'learning_rate': 0.05,\n",
        "          'max_bin': 512,\n",
        "          'subsample_for_bin': 200,\n",
        "          'subsample': 0.7,\n",
        "          'subsample_freq': 1,\n",
        "          'colsample_bytree': 0.8,\n",
        "          'reg_alpha': 20,\n",
        "          'reg_lambda': 20,\n",
        "          'min_split_gain': 0.5,\n",
        "          'min_child_weight': 1,\n",
        "          'min_child_samples': 10,\n",
        "          'scale_pos_weight': 1,\n",
        "          'num_class' : 1,\n",
        "          'metric' : 'auc'\n",
        "          }\n",
        "\n",
        "lgbm = lgb.train(params,\n",
        "                 train_data,\n",
        "                 2500,\n",
        "                 valid_sets=valid_data,\n",
        "                 early_stopping_rounds= 100,\n",
        "                 verbose_eval= 10\n",
        "                 )\n",
        "\n",
        "# model.fit(x_train,y_train,verbose=20,eval_metric='logloss')\n",
        "predicted_val_value = model.predict(x_val)\n",
        "\n",
        "\n",
        "print(\"Training Accuracy: \",accuracy(predicted_val_value,y_val))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "NdwKsDuCrgjl"
      },
      "outputs": [],
      "source": [
        "predicted_test_value = model.predict(x_test)\n",
        "df6 = pd.DataFrame(data=np.column_stack((x_test_first,predicted_test_value)),columns=['SK_ID_CURR','TARGET'])\n",
        "df6.to_csv('/content/drive/MyDrive/AI511-HOMELOAN-2022/submission_lgbm2.csv',index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "ImJyiJUtrzHE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ace3cf3c-77a8-4914-b3c5-47b491f7b4eb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    71750\n",
              "1.0    51255\n",
              "Name: TARGET, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "df6['TARGET'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4uIBPJeQj50W"
      },
      "source": [
        "##**Neural Network**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "8pmeHqjp8phK",
        "outputId": "68a81514-b237-46fe-a9cc-7e35a9a3c64b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "16962/16962 [==============================] - 49s 3ms/step - loss: 0.5136 - accuracy: 0.7464\n",
            "Epoch 2/50\n",
            "16962/16962 [==============================] - 48s 3ms/step - loss: 0.4105 - accuracy: 0.8091\n",
            "Epoch 3/50\n",
            "16962/16962 [==============================] - 47s 3ms/step - loss: 0.3591 - accuracy: 0.8361\n",
            "Epoch 4/50\n",
            "16962/16962 [==============================] - 48s 3ms/step - loss: 0.3274 - accuracy: 0.8519\n",
            "Epoch 5/50\n",
            "16962/16962 [==============================] - 50s 3ms/step - loss: 0.3037 - accuracy: 0.8639\n",
            "Epoch 6/50\n",
            "16962/16962 [==============================] - 48s 3ms/step - loss: 0.2874 - accuracy: 0.8717\n",
            "Epoch 7/50\n",
            "16962/16962 [==============================] - 48s 3ms/step - loss: 0.2739 - accuracy: 0.8788\n",
            "Epoch 8/50\n",
            "16962/16962 [==============================] - 49s 3ms/step - loss: 0.2625 - accuracy: 0.8842\n",
            "Epoch 9/50\n",
            "16962/16962 [==============================] - 51s 3ms/step - loss: 0.2536 - accuracy: 0.8885\n",
            "Epoch 10/50\n",
            "16962/16962 [==============================] - 50s 3ms/step - loss: 0.2469 - accuracy: 0.8920\n",
            "Epoch 11/50\n",
            "16962/16962 [==============================] - 49s 3ms/step - loss: 0.2393 - accuracy: 0.8957\n",
            "Epoch 12/50\n",
            "16962/16962 [==============================] - 48s 3ms/step - loss: 0.2336 - accuracy: 0.8984\n",
            "Epoch 13/50\n",
            "16962/16962 [==============================] - 51s 3ms/step - loss: 0.2280 - accuracy: 0.9012\n",
            "Epoch 14/50\n",
            "16962/16962 [==============================] - 50s 3ms/step - loss: 0.2240 - accuracy: 0.9030\n",
            "Epoch 15/50\n",
            "16962/16962 [==============================] - 48s 3ms/step - loss: 0.2189 - accuracy: 0.9054\n",
            "Epoch 16/50\n",
            "16962/16962 [==============================] - 51s 3ms/step - loss: 0.2157 - accuracy: 0.9073\n",
            "Epoch 17/50\n",
            "16962/16962 [==============================] - 52s 3ms/step - loss: 0.2120 - accuracy: 0.9088\n",
            "Epoch 18/50\n",
            "16962/16962 [==============================] - 49s 3ms/step - loss: 0.2096 - accuracy: 0.9100\n",
            "Epoch 19/50\n",
            "16962/16962 [==============================] - 50s 3ms/step - loss: 0.2052 - accuracy: 0.9122\n",
            "Epoch 20/50\n",
            "16962/16962 [==============================] - 50s 3ms/step - loss: 0.2024 - accuracy: 0.9132\n",
            "Epoch 21/50\n",
            "16962/16962 [==============================] - 54s 3ms/step - loss: 0.2002 - accuracy: 0.9154\n",
            "Epoch 22/50\n",
            "16962/16962 [==============================] - 49s 3ms/step - loss: 0.1979 - accuracy: 0.9154\n",
            "Epoch 23/50\n",
            "16962/16962 [==============================] - 49s 3ms/step - loss: 0.1966 - accuracy: 0.9168\n",
            "Epoch 24/50\n",
            "16962/16962 [==============================] - 49s 3ms/step - loss: 0.1926 - accuracy: 0.9184\n",
            "Epoch 25/50\n",
            "16962/16962 [==============================] - 51s 3ms/step - loss: 0.1909 - accuracy: 0.9191\n",
            "Epoch 26/50\n",
            "16962/16962 [==============================] - 50s 3ms/step - loss: 0.1883 - accuracy: 0.9206\n",
            "Epoch 27/50\n",
            "16962/16962 [==============================] - 49s 3ms/step - loss: 0.1870 - accuracy: 0.9210\n",
            "Epoch 28/50\n",
            "16962/16962 [==============================] - 51s 3ms/step - loss: 0.1858 - accuracy: 0.9216\n",
            "Epoch 29/50\n",
            "14880/16962 [=========================>....] - ETA: 6s - loss: 0.1826 - accuracy: 0.9228"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-44-40c5dc0f34fe>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     31\u001b[0m               metrics=['accuracy'])\n\u001b[1;32m     32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1407\u001b[0m                 _r=1):\n\u001b[1;32m   1408\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1409\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1410\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1411\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    914\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 915\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    916\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    917\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    945\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    946\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 947\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    948\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2451\u001b[0m       (graph_function,\n\u001b[1;32m   2452\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[0;32m-> 2453\u001b[0;31m     return graph_function._call_flat(\n\u001b[0m\u001b[1;32m   2454\u001b[0m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[1;32m   2455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1858\u001b[0m         and executing_eagerly):\n\u001b[1;32m   1859\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1860\u001b[0;31m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[1;32m   1861\u001b[0m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[1;32m   1862\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    495\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    496\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 497\u001b[0;31m           outputs = execute.execute(\n\u001b[0m\u001b[1;32m    498\u001b[0m               \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    499\u001b[0m               \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     52\u001b[0m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m     55\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[1;32m     56\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras import regularizers\n",
        "\n",
        "\n",
        "# model = keras.Sequential([\n",
        "#     keras.layers.Flatten(input_shape=(len(selected_features),)),\n",
        "#     keras.layers.Dense(16, activation=tf.nn.relu),\n",
        "# \tkeras.layers.Dense(16, activation=tf.nn.relu),\n",
        "#     keras.layers.Dense(1, activation=tf.nn.sigmoid),\n",
        "# ])\n",
        "\n",
        "model = Sequential() \n",
        "model.add(Dense(128, activation='relu', input_dim=len(selected_features)))\n",
        "\n",
        "model.add(Dense(128, activation='relu'))\n",
        "\n",
        "model.add(Dense(128, activation='relu'))\n",
        "\n",
        "model.add(Dense(128, activation='relu'))\n",
        "\n",
        "model.add(Dense(1, activation='sigmoid')) \n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.fit(x_train,y_train, epochs=50, batch_size=16)\n",
        "\n",
        "\n",
        "\n",
        "predicted_val_value= model.predict(x_val)  \n",
        "print(\"Training Accuracy: \",accuracy(predicted_val_value,y_val))\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yySt2WQdsYB7"
      },
      "outputs": [],
      "source": [
        "predicted_test_value = model.predict(x_test)\n",
        "df6 = pd.DataFrame(data=np.column_stack((x_test_first,predicted_test_value)),columns=['SK_ID_CURR','TARGET'])\n",
        "df6.to_csv('/content/drive/MyDrive/AI511-HOMELOAN-2022/submission_nn.csv',index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h5ya_OQ2Si--"
      },
      "outputs": [],
      "source": [
        "df6['TARGET'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m42JKPZk4RFy"
      },
      "source": [
        "##**SVM**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SgmBj5hv18F5"
      },
      "outputs": [],
      "source": [
        "# import required libraries\n",
        "\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "classifier = SVC(kernel='rbf',random_state=1,C=1,gamma='auto')\n",
        "classifier.fit(x_train,y_train)\n",
        "\n",
        "# perform prediction on x_test data\n",
        "predicted_val_value = classifier.predict(x_val)\n",
        "\n",
        "# model.fit(x_train,y_train,verbose=20,eval_metric='logloss')\n",
        "\n",
        "print(\"Training Accuracy: \",accuracy(predicted_val_value,y_val))\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7Dk2fYJb6DgK"
      },
      "outputs": [],
      "source": [
        "predicted_test_value = model.predict(x_test)\n",
        "df6 = pd.DataFrame(data=np.column_stack((x_test_first,predicted_test_value)),columns=['SK_ID_CURR','TARGET'])\n",
        "df6.to_csv('/content/drive/MyDrive/AI511-HOMELOAN-2022/submission_svm.csv',index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZJ1ClvyT6SQb"
      },
      "outputs": [],
      "source": [
        "df6['TARGET'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vprvhJLUypuc"
      },
      "source": [
        "##**LGBM With Hypertuning**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WAETSK71HGSt"
      },
      "outputs": [],
      "source": [
        "# !pip install optuna"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RDU1puhoHHV9"
      },
      "outputs": [],
      "source": [
        "# import optuna\n",
        "# from sklearn.model_selection import train_test_split\n",
        "# import sklearn\n",
        "# def objective(trial):\n",
        "\n",
        "#   X_train, X_test, y_train, y_test = train_test_split(x_train_res,y_train_res, stratify = y_train_res, test_size=0.4, random_state=62)\n",
        "#   dtrain = lgb.Dataset(X_train, label=y_train)\n",
        "\n",
        "#   param = {\n",
        "#         \"objective\": \"binary\",\n",
        "#         \"metric\": \"auc\",\n",
        "#         \"verbosity\": -1,\n",
        "#         \"boosting_type\": \"gbdt\",\n",
        "#         \"max_depth\":trial.suggest_int(\"num_leaves\", -2, 10),\n",
        "#         \"min_split_gain\":trial.suggest_float(\"min split g\", 0, 20),\n",
        "       \n",
        "#         \"scale_pos_weight\":trial.suggest_int(\"scale_pos_weight\", 1, 17),\n",
        "#         \"learning_rate\":trial.suggest_float(\"subsample_for_bin\", 0,10),\n",
        "#         \"lambda_l1\": trial.suggest_float(\"lambda_l1\", 1e-8, 10.0, log=True),\n",
        "#         \"lambda_l2\": trial.suggest_float(\"lambda_l2\", 1e-8, 10.0, log=True),\n",
        "#         \"num_leaves\": 128,\n",
        "#         \"feature_fraction\": trial.suggest_float(\"feature_fraction\", 0.2, 1.0),\n",
        "#         \"bagging_fraction\": trial.suggest_float(\"bagging_fraction\", 0.1, 1.0),\n",
        "#         \"bagging_freq\": trial.suggest_int(\"bagging_freq\", 1, 7),\n",
        "#         \"min_child_samples\": trial.suggest_int(\"min_child_samples\", 5, 150),\n",
        "#     }\n",
        "\n",
        "#   gbm = lgb.train(param, dtrain)\n",
        "#   preds = gbm.predict(X_test)\n",
        "#   pred_labels = np.rint(preds)\n",
        "#   accuracy = sklearn.metrics.accuracy_score(y_test, pred_labels)\n",
        "#   return accuracy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7JfvjwrMHMqf"
      },
      "outputs": [],
      "source": [
        "# study = optuna.create_study(direction=\"maximize\")\n",
        "# study.optimize(objective, n_trials=200)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9Rs4xfP2HOxf"
      },
      "outputs": [],
      "source": [
        "# print(\"Best trial:\")\n",
        "# trial = study.best_trial\n",
        "\n",
        "# print(\"  Value: {}\".format(trial.value))\n",
        "\n",
        "# print(\"  Params: \")\n",
        "# for key, value in trial.params.items():\n",
        "#     print(\"    {}: {}\".format(key, value))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4p9bhjfVHPji"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "TPU",
    "colab": {
      "collapsed_sections": [
        "-x4Dz-QRfrIJ"
      ],
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}