Over and Under Sampling_Bank Data (2).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import tree\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "bankdata= pd.read_csv(r\"C:\\Sansys UK\\DataSceince\\Datascience Training\\Oct2021\\W4_1 Nov 2021\\W4_Tuesday 2 Nov 2021\\Universalbank.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
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       "      <th>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Income</th>\n",
       "      <th>ZIP Code</th>\n",
       "      <th>Family</th>\n",
       "      <th>CCAvg</th>\n",
       "      <th>Education</th>\n",
       "      <th>Mortgage</th>\n",
       "      <th>Personal Loan</th>\n",
       "      <th>Securities Account</th>\n",
       "      <th>CD Account</th>\n",
       "      <th>Online</th>\n",
       "      <th>CreditCard</th>\n",
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       "      <td>11</td>\n",
       "      <td>94720</td>\n",
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       "      <th>3</th>\n",
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       "      <td>35</td>\n",
       "      <td>9</td>\n",
       "      <td>100</td>\n",
       "      <td>94112</td>\n",
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       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>8</td>\n",
       "      <td>45</td>\n",
       "      <td>91330</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  Age  Experience  Income  ZIP Code  Family  CCAvg  Education  Mortgage  \\\n",
       "0   1   25           1      49     91107       4    1.6          1         0   \n",
       "1   2   45          19      34     90089       3    1.5          1         0   \n",
       "2   3   39          15      11     94720       1    1.0          1         0   \n",
       "3   4   35           9     100     94112       1    2.7          2         0   \n",
       "4   5   35           8      45     91330       4    1.0          2         0   \n",
       "\n",
       "   Personal Loan  Securities Account  CD Account  Online  CreditCard  \n",
       "0              0                   1           0       0           0  \n",
       "1              0                   1           0       0           0  \n",
       "2              0                   0           0       0           0  \n",
       "3              0                   0           0       0           0  \n",
       "4              0                   0           0       0           1  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bankdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
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       "      <th>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Income</th>\n",
       "      <th>ZIP Code</th>\n",
       "      <th>Family</th>\n",
       "      <th>CCAvg</th>\n",
       "      <th>Education</th>\n",
       "      <th>Mortgage</th>\n",
       "      <th>Personal Loan</th>\n",
       "      <th>Securities Account</th>\n",
       "      <th>CD Account</th>\n",
       "      <th>Online</th>\n",
       "      <th>CreditCard</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>4995</th>\n",
       "      <td>4996</td>\n",
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       "      <th>4998</th>\n",
       "      <td>4999</td>\n",
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       "      <td>49</td>\n",
       "      <td>90034</td>\n",
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       "      <th>4999</th>\n",
       "      <td>5000</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>83</td>\n",
       "      <td>92612</td>\n",
       "      <td>3</td>\n",
       "      <td>0.8</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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      "text/plain": [
       "        ID  Age  Experience  Income  ZIP Code  Family  CCAvg  Education  \\\n",
       "4995  4996   29           3      40     92697       1    1.9          3   \n",
       "4996  4997   30           4      15     92037       4    0.4          1   \n",
       "4997  4998   63          39      24     93023       2    0.3          3   \n",
       "4998  4999   65          40      49     90034       3    0.5          2   \n",
       "4999  5000   28           4      83     92612       3    0.8          1   \n",
       "\n",
       "      Mortgage  Personal Loan  Securities Account  CD Account  Online  \\\n",
       "4995         0              0                   0           0       1   \n",
       "4996        85              0                   0           0       1   \n",
       "4997         0              0                   0           0       0   \n",
       "4998         0              0                   0           0       1   \n",
       "4999         0              0                   0           0       1   \n",
       "\n",
       "      CreditCard  \n",
       "4995           0  \n",
       "4996           0  \n",
       "4997           0  \n",
       "4998           0  \n",
       "4999           1  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bankdata.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5000 entries, 0 to 4999\n",
      "Data columns (total 14 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   ID                  5000 non-null   int64  \n",
      " 1   Age                 5000 non-null   int64  \n",
      " 2   Experience          5000 non-null   int64  \n",
      " 3   Income              5000 non-null   int64  \n",
      " 4   ZIP Code            5000 non-null   int64  \n",
      " 5   Family              5000 non-null   int64  \n",
      " 6   CCAvg               5000 non-null   float64\n",
      " 7   Education           5000 non-null   int64  \n",
      " 8   Mortgage            5000 non-null   int64  \n",
      " 9   Personal Loan       5000 non-null   int64  \n",
      " 10  Securities Account  5000 non-null   int64  \n",
      " 11  CD Account          5000 non-null   int64  \n",
      " 12  Online              5000 non-null   int64  \n",
      " 13  CreditCard          5000 non-null   int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 547.0 KB\n"
     ]
    }
   ],
   "source": [
    "bankdata.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class 0: 3530\n",
      "Class 1: 1470\n",
      "Proportion: 2.4 : 1\n"
     ]
    }
   ],
   "source": [
    "CreditCard_count = bankdata.CreditCard.value_counts()\n",
    "print('Class 0:', CreditCard_count[0])\n",
    "print('Class 1:', CreditCard_count[1])\n",
    "print('Proportion:', round(CreditCard_count[0] / CreditCard_count[1], 2), ': 1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3530\n",
      "1470\n"
     ]
    }
   ],
   "source": [
    "# Class count of target variable\n",
    "\n",
    "count_class_0, count_class_1 = bankdata.CreditCard.value_counts()\n",
    "print(count_class_0)\n",
    "print(count_class_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3530, 14)\n",
      "(1470, 14)\n"
     ]
    }
   ],
   "source": [
    "# Divide bank data by class\n",
    "\n",
    "bankdata_class_0 = bankdata[bankdata['CreditCard'] == 0]\n",
    "bankdata_class_1 = bankdata[bankdata['CreditCard'] == 1]\n",
    "print(bankdata_class_0.shape)\n",
    "print(bankdata_class_1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Random Under-sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sample data of major class '0'\n",
    "\n",
    "bankdata_class_0_under = bankdata_class_0.sample(count_class_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2940, 14)\n"
     ]
    }
   ],
   "source": [
    "# Concat the sample data of  major class '0' dataset with 100% minor class data\n",
    "\n",
    "bankdata_under = pd.concat([bankdata_class_0_under, bankdata_class_1], axis=0)\n",
    "print(bankdata_under.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Random Over-Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# copies of minor class '1' \n",
    "\n",
    "bankdata_class_1_over = bankdata_class_1.sample(count_class_0, replace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7060, 14)\n"
     ]
    }
   ],
   "source": [
    "# Concat the 100% of major class '0' dataset with copies of minor class data\n",
    "\n",
    "bankdata_over = pd.concat([bankdata_class_0, bankdata_class_1_over], axis=0)\n",
    "print(bankdata_over.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary packages\n",
    "\n",
    "from sklearn.metrics import confusion_matrix  \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.tree import DecisionTreeClassifier \n",
    "from sklearn.metrics import accuracy_score \n",
    "from sklearn.metrics import classification_report \n",
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Identify the independent and Depentent (target) variables\n",
    "\n",
    "x = bankdata.iloc[:,:-1]   # independent variables\n",
    "y = bankdata.iloc[:,-1]    # dependent (target) variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset into train and test \n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state = 42) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to build the decision tree model\n",
    "\n",
    "bankdc = DecisionTreeClassifier()\n",
    "bankdc = bankdc.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': None, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'presort': 'deprecated', 'random_state': None, 'splitter': 'best'}\n"
     ]
    }
   ],
   "source": [
    "#To display the algorithm parameters\n",
    "params = bankdc.get_params()\n",
    "print(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predict the DC model with test data\n",
    "\n",
    "y_pred = bankdc.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[755 318]\n",
      " [254 173]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.70      0.73      1073\n",
      "           1       0.35      0.41      0.38       427\n",
      "\n",
      "    accuracy                           0.62      1500\n",
      "   macro avg       0.55      0.55      0.55      1500\n",
      "weighted avg       0.64      0.62      0.63      1500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Confusion matrix & Classification Report\n",
    "\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6186666666666667\n",
      "Precision: 0.35234215885947046\n",
      "Recall: 0.405152224824356\n",
      "f1-score: 0.3769063180827887\n"
     ]
    }
   ],
   "source": [
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "\n",
    "# Model Accuracy: how often is the classifier correct?\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test,y_pred))\n",
    "\n",
    "# Model Precision: what percentage of positive tuples are labeled as such?\n",
    "print(\"Precision:\",metrics.precision_score(y_test,y_pred))\n",
    "\n",
    "# Model Recall: what percentage of positive tuples are labelled as such?\n",
    "print(\"Recall:\",metrics.recall_score(y_test,y_pred))\n",
    "\n",
    "# Model Recall: what percentage of positive tuples are labelled as such?\n",
    "print(\"f1-score:\",metrics.f1_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROCAUC score: 0.5534614805389254\n"
     ]
    }
   ],
   "source": [
    "# Area under ROC curve\n",
    "\n",
    "print('ROCAUC score:',roc_auc_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>92121</td>\n",
       "      <td>4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2</td>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  Age  Experience  Income  ZIP Code  Family  CCAvg  Education  Mortgage  \\\n",
       "0   1   25           1      49     91107       4    1.6          1         0   \n",
       "1   2   45          19      34     90089       3    1.5          1         0   \n",
       "2   3   39          15      11     94720       1    1.0          1         0   \n",
       "3   4   35           9     100     94112       1    2.7          2         0   \n",
       "5   6   37          13      29     92121       4    0.4          2       155   \n",
       "\n",
       "   Personal Loan  Securities Account  CD Account  Online  CreditCard  \n",
       "0              0                   1           0       0           0  \n",
       "1              0                   1           0       0           0  \n",
       "2              0                   0           0       0           0  \n",
       "3              0                   0           0       0           0  \n",
       "5              0                   0           0       1           0  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bankdata_over.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2717</th>\n",
       "      <td>2718</td>\n",
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       "      <td>-2</td>\n",
       "      <td>45</td>\n",
       "      <td>95422</td>\n",
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       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2072</th>\n",
       "      <td>2073</td>\n",
       "      <td>29</td>\n",
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       "      <td>39</td>\n",
       "      <td>95831</td>\n",
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       "      <td>0.2</td>\n",
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       "      <td>137</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4815</th>\n",
       "      <td>4816</td>\n",
       "      <td>58</td>\n",
       "      <td>32</td>\n",
       "      <td>99</td>\n",
       "      <td>92697</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "        ID  Age  Experience  Income  ZIP Code  Family  CCAvg  Education  \\\n",
       "1499  1500   52          26      91     92173       1    4.3          2   \n",
       "153    154   60          36      22     94551       2    1.0          1   \n",
       "2717  2718   23          -2      45     95422       4    0.6          2   \n",
       "2072  2073   29           3      39     95831       4    0.2          1   \n",
       "4815  4816   58          32      99     92697       2    1.4          1   \n",
       "\n",
       "      Mortgage  Personal Loan  Securities Account  CD Account  Online  \\\n",
       "1499         0              1                   0           1       1   \n",
       "153          0              0                   1           1       1   \n",
       "2717         0              0                   0           0       1   \n",
       "2072       137              0                   0           0       1   \n",
       "4815         0              0                   0           0       0   \n",
       "\n",
       "      CreditCard  \n",
       "1499           1  \n",
       "153            1  \n",
       "2717           1  \n",
       "2072           1  \n",
       "4815           1  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bankdata_over.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7060 entries, 0 to 4815\n",
      "Data columns (total 14 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   ID                  7060 non-null   int64  \n",
      " 1   Age                 7060 non-null   int64  \n",
      " 2   Experience          7060 non-null   int64  \n",
      " 3   Income              7060 non-null   int64  \n",
      " 4   ZIP Code            7060 non-null   int64  \n",
      " 5   Family              7060 non-null   int64  \n",
      " 6   CCAvg               7060 non-null   float64\n",
      " 7   Education           7060 non-null   int64  \n",
      " 8   Mortgage            7060 non-null   int64  \n",
      " 9   Personal Loan       7060 non-null   int64  \n",
      " 10  Securities Account  7060 non-null   int64  \n",
      " 11  CD Account          7060 non-null   int64  \n",
      " 12  Online              7060 non-null   int64  \n",
      " 13  CreditCard          7060 non-null   int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 827.3 KB\n"
     ]
    }
   ],
   "source": [
    "bankdata_over.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Identify the independent and Dependent (target) variables\n",
    "\n",
    "xo = bankdata_over.iloc[:,:-1]\n",
    "yo = bankdata_over.iloc[:,-1] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset into train and test \n",
    "\n",
    "xo_train, xo_test, yo_train, yo_test = train_test_split(xo, yo, test_size = 0.3, random_state = 42) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to build the decision tree model\n",
    "\n",
    "bankdco = DecisionTreeClassifier()\n",
    "bankdco = bankdco.fit(xo_train,yo_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': None, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'presort': 'deprecated', 'random_state': None, 'splitter': 'best'}\n"
     ]
    }
   ],
   "source": [
    "#To display the algorithm parameters\n",
    "params = bankdco.get_params()\n",
    "print(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predict the DC model with test data\n",
    "\n",
    "yo_pred = bankdco.predict(xo_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[771 293]\n",
      " [132 922]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.72      0.78      1064\n",
      "           1       0.76      0.87      0.81      1054\n",
      "\n",
      "    accuracy                           0.80      2118\n",
      "   macro avg       0.81      0.80      0.80      2118\n",
      "weighted avg       0.81      0.80      0.80      2118\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Confusion matrix & Classification Report\n",
    "\n",
    "print(confusion_matrix(yo_test, yo_pred))\n",
    "print(classification_report(yo_test, yo_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7993389990557129\n",
      "Precision: 0.7588477366255144\n",
      "Recall: 0.8747628083491461\n",
      "f1-score: 0.8126928162185985\n"
     ]
    }
   ],
   "source": [
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "\n",
    "# Model Accuracy: how often is the classifier correct?\n",
    "print(\"Accuracy:\",metrics.accuracy_score(yo_test, yo_pred))\n",
    "\n",
    "# Model Precision: what percentage of positive tuples are labeled as such?\n",
    "print(\"Precision:\",metrics.precision_score(yo_test, yo_pred))\n",
    "\n",
    "# Model Recall: what percentage of positive tuples are labelled as such?\n",
    "print(\"Recall:\",metrics.recall_score(yo_test, yo_pred))\n",
    "\n",
    "# Model Recall: what percentage of positive tuples are labelled as such?\n",
    "print(\"f1-score:\",metrics.f1_score(yo_test, yo_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROCAUC score: 0.799693434249761\n"
     ]
    }
   ],
   "source": [
    "# Area under ROC curve\n",
    "\n",
    "print('ROCAUC score:',roc_auc_score(yo_test, yo_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>953</th>\n",
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       "      <td>28</td>\n",
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       "      <th>2414</th>\n",
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      "text/plain": [
       "        ID  Age  Experience  Income  ZIP Code  Family  CCAvg  Education  \\\n",
       "4775  4776   44          14      33     94063       1   0.75          3   \n",
       "887    888   41          16     118     94720       2   3.30          1   \n",
       "3454  3455   47          21     132     92120       1   0.30          1   \n",
       "953    954   51          26      28     92130       4   0.20          2   \n",
       "2414  2415   34          10     134     91775       1   4.00          1   \n",
       "\n",
       "      Mortgage  Personal Loan  Securities Account  CD Account  Online  \\\n",
       "4775       171              0                   0           0       0   \n",
       "887          0              0                   0           0       1   \n",
       "3454         0              0                   0           0       1   \n",
       "953          0              0                   1           0       1   \n",
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       "\n",
       "      CreditCard  \n",
       "4775           0  \n",
       "887            0  \n",
       "3454           0  \n",
       "953            0  \n",
       "2414           0  "
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bankdata_under.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "        ID  Age  Experience  Income  ZIP Code  Family  CCAvg  Education  \\\n",
       "4775  4776   44          14      33     94063       1   0.75          3   \n",
       "887    888   41          16     118     94720       2   3.30          1   \n",
       "3454  3455   47          21     132     92120       1   0.30          1   \n",
       "953    954   51          26      28     92130       4   0.20          2   \n",
       "2414  2415   34          10     134     91775       1   4.00          1   \n",
       "\n",
       "      Mortgage  Personal Loan  Securities Account  CD Account  Online  \\\n",
       "4775       171              0                   0           0       0   \n",
       "887          0              0                   0           0       1   \n",
       "3454         0              0                   0           0       1   \n",
       "953          0              0                   1           0       1   \n",
       "2414         0              0                   0           0       0   \n",
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       "      CreditCard  \n",
       "4775           0  \n",
       "887            0  \n",
       "3454           0  \n",
       "953            0  \n",
       "2414           0  "
      ]
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     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bankdata_under.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2940 entries, 4775 to 4999\n",
      "Data columns (total 14 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   ID                  2940 non-null   int64  \n",
      " 1   Age                 2940 non-null   int64  \n",
      " 2   Experience          2940 non-null   int64  \n",
      " 3   Income              2940 non-null   int64  \n",
      " 4   ZIP Code            2940 non-null   int64  \n",
      " 5   Family              2940 non-null   int64  \n",
      " 6   CCAvg               2940 non-null   float64\n",
      " 7   Education           2940 non-null   int64  \n",
      " 8   Mortgage            2940 non-null   int64  \n",
      " 9   Personal Loan       2940 non-null   int64  \n",
      " 10  Securities Account  2940 non-null   int64  \n",
      " 11  CD Account          2940 non-null   int64  \n",
      " 12  Online              2940 non-null   int64  \n",
      " 13  CreditCard          2940 non-null   int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 344.5 KB\n"
     ]
    }
   ],
   "source": [
    "bankdata_under.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Identify the independent and Dependent (target) variables\n",
    "\n",
    "xu = bankdata_under.iloc[:,:-1]\n",
    "yu = bankdata_under.iloc[:,-1] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset into train and test \n",
    "\n",
    "xu_train, xu_test, yu_train, yu_test = train_test_split(xu, yu, test_size = 0.3, random_state = 42) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to build the decision tree model\n",
    "\n",
    "bankdcu = DecisionTreeClassifier()\n",
    "bankdcu = bankdcu.fit(xu_train,yu_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': None, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'presort': 'deprecated', 'random_state': None, 'splitter': 'best'}\n"
     ]
    }
   ],
   "source": [
    "#To display the algorithm parameters\n",
    "params = bankdcu.get_params()\n",
    "print(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predict the DC model with test data\n",
    "\n",
    "yu_pred = bankdcu.predict(xu_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[231 215]\n",
      " [195 241]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.54      0.52      0.53       446\n",
      "           1       0.53      0.55      0.54       436\n",
      "\n",
      "    accuracy                           0.54       882\n",
      "   macro avg       0.54      0.54      0.54       882\n",
      "weighted avg       0.54      0.54      0.54       882\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Confusion matrix & Classification Report\n",
    "\n",
    "print(confusion_matrix(yu_test, yu_pred))\n",
    "print(classification_report(yu_test, yu_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.5351473922902494\n",
      "Precision: 0.5285087719298246\n",
      "Recall: 0.5527522935779816\n",
      "f1-score: 0.5403587443946187\n"
     ]
    }
   ],
   "source": [
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "\n",
    "# Model Accuracy: how often is the classifier correct?\n",
    "print(\"Accuracy:\",metrics.accuracy_score(yu_test, yu_pred))\n",
    "\n",
    "# Model Precision: what percentage of positive tuples are labeled as such?\n",
    "print(\"Precision:\",metrics.precision_score(yu_test, yu_pred))\n",
    "\n",
    "# Model Recall: what percentage of positive tuples are labelled as such?\n",
    "print(\"Recall:\",metrics.recall_score(yu_test, yu_pred))\n",
    "\n",
    "# Model Recall: what percentage of positive tuples are labelled as such?\n",
    "print(\"f1-score:\",metrics.f1_score(yu_test, yu_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROCAUC score: 0.5353447566544617\n"
     ]
    }
   ],
   "source": [
    "# Area under ROC curve\n",
    "\n",
    "print('ROCAUC score:',roc_auc_score(yu_test, yu_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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