OverSampling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f10be068",
   "metadata": {},
   "source": [
    "# Oversampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "64bebac0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import imblearn\n",
    "from collections import Counter\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from imblearn.over_sampling import SMOTE, BorderlineSMOTE, SVMSMOTE, ADASYN\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix,precision_score,classification_report,plot_confusion_matrix\n",
    "from sklearn.metrics import plot_confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d749726",
   "metadata": {},
   "source": [
    "## Loading and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ba3006a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age        job  marital    education  default housing loan    contact  \\\n",
       "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
       "1   57   services  married  high.school  unknown      no   no  telephone   \n",
       "2   37   services  married  high.school       no     yes   no  telephone   \n",
       "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
       "4   56   services  married  high.school       no      no  yes  telephone   \n",
       "\n",
       "  month day_of_week  ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n",
       "0   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "1   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "2   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "3   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "4   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "\n",
       "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
       "0          93.994          -36.4      4.857       5191.0  no  \n",
       "1          93.994          -36.4      4.857       5191.0  no  \n",
       "2          93.994          -36.4      4.857       5191.0  no  \n",
       "3          93.994          -36.4      4.857       5191.0  no  \n",
       "4          93.994          -36.4      4.857       5191.0  no  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"bank-additional-full.csv\", delimiter=';')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f7d825e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = df.columns\n",
    "var_numerical = list(df._get_numeric_data().columns)\n",
    "var_categorical = list(set(cols) - set(var_numerical) - set('y'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "05130dde",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Default variable and Day of week has no impact on the client subscribing for term deposit.\n",
    "df.drop([\"default\"], axis = 1, inplace=True)\n",
    "df.drop([\"day_of_week\"], axis = 1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "445b9b21",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['emp.var.rate', 'nr.employed'], axis = 1, inplace=True)\n",
    "var_numerical = list(set(var_numerical) - set(['emp.var.rate', 'nr.employed']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ca8733a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the dummy variables for contact, poutcome , job, month, marital,  education\n",
    "# Let's drop the first column from  using 'drop_first = True' as first column can be derived using other columns \n",
    "\n",
    "df['job'] = df['job'].apply(lambda x: -1 if x=='unknown' or x=='unemployed' else (15 if x=='entrepreneur' else (8 if x == 'blue-collar' else ( 6 if x=='technician' or x=='services' or  x=='admin.' or x=='management' else (4 if x== 'self-employed' or x=='student' else (2 if x=='housemaid' or x=='retired' else None) )))))\n",
    "df['housing'] = df['housing'].apply(lambda x: 0 if x=='no' else (1 if x=='yes' else -1))\n",
    "df['loan'] = df['loan'].apply(lambda x: 0 if x=='no' else (1 if x=='yes' else -1))\n",
    "df['y'] = df['y'].apply(lambda x: 0 if x=='no' else (1 if x=='yes' else -1))\n",
    "df['poutcome'] = df['poutcome'].apply(lambda x: 0 if x=='failure' else (2 if x=='failure' else - 1))\n",
    "df['pdays'] = df['pdays'].apply(lambda x: 0 if x==999 else(20 if x<=10 else(6 if x<=20 else 3)))\n",
    "\n",
    "df  = pd.get_dummies(df, drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d0561bbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.pop('y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7b812d5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>age</th>\n",
       "      <th>job</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>...</th>\n",
       "      <th>contact_telephone</th>\n",
       "      <th>month_aug</th>\n",
       "      <th>month_dec</th>\n",
       "      <th>month_jul</th>\n",
       "      <th>month_jun</th>\n",
       "      <th>month_mar</th>\n",
       "      <th>month_may</th>\n",
       "      <th>month_nov</th>\n",
       "      <th>month_oct</th>\n",
       "      <th>month_sep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>307</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  job  housing  loan  duration  campaign  pdays  previous  poutcome  \\\n",
       "0   56    2        0     0       261         1      0         0        -1   \n",
       "1   57    6        0     0       149         1      0         0        -1   \n",
       "2   37    6        1     0       226         1      0         0        -1   \n",
       "3   40    6        0     0       151         1      0         0        -1   \n",
       "4   56    6        0     1       307         1      0         0        -1   \n",
       "\n",
       "   cons.price.idx  ...  contact_telephone  month_aug  month_dec  month_jul  \\\n",
       "0          93.994  ...                  1          0          0          0   \n",
       "1          93.994  ...                  1          0          0          0   \n",
       "2          93.994  ...                  1          0          0          0   \n",
       "3          93.994  ...                  1          0          0          0   \n",
       "4          93.994  ...                  1          0          0          0   \n",
       "\n",
       "   month_jun  month_mar  month_may  month_nov  month_oct  month_sep  \n",
       "0          0          0          1          0          0          0  \n",
       "1          0          0          1          0          0          0  \n",
       "2          0          0          1          0          0          0  \n",
       "3          0          0          1          0          0          0  \n",
       "4          0          0          1          0          0          0  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "6707e222",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({0: 36548, 1: 4640})\n"
     ]
    }
   ],
   "source": [
    "counter = Counter(y)\n",
    "print(counter)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3b7a517",
   "metadata": {},
   "source": [
    "## SMOTE\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "38cced44",
   "metadata": {},
   "outputs": [],
   "source": [
    "oversample = SMOTE()\n",
    "X_s, y_s = oversample.fit_resample(df, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "308d2e2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({0: 36548, 1: 36548})\n"
     ]
    }
   ],
   "source": [
    "cnt_1 = Counter(y_s)\n",
    "print(cnt_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "44d9bff6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_s, X_test_s, y_train_s, y_test_s = train_test_split(X_s, y_s, test_size = 0.2, random_state = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "55db7463",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtclf = DecisionTreeClassifier( max_depth=8, criterion='gini',class_weight= {0:1,1:1})\n",
    "dtclf= dtclf.fit(X_train_s,y_train_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6d8e0aa0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9224350205198358\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.89      0.92      7290\n",
      "           1       0.90      0.96      0.93      7330\n",
      "\n",
      "    accuracy                           0.92     14620\n",
      "   macro avg       0.92      0.92      0.92     14620\n",
      "weighted avg       0.92      0.92      0.92     14620\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_s = dtclf.predict(X_test_s)\n",
    "print(accuracy_score(y_test_s,y_pred_s))\n",
    "print(classification_report(y_test_s, y_pred_s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "3c96d4b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abhishek\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(dtclf, X_test_s, y_test_s)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "cad1a26f",
   "metadata": {},
   "outputs": [],
   "source": [
    "nbclf = GaussianNB()\n",
    "nbclf = nbclf.fit(X_train_s, y_train_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "a43e21d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8683310533515732\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.87      0.87      7290\n",
      "           1       0.87      0.87      0.87      7330\n",
      "\n",
      "    accuracy                           0.87     14620\n",
      "   macro avg       0.87      0.87      0.87     14620\n",
      "weighted avg       0.87      0.87      0.87     14620\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_s = nbclf.predict(X_test_s)\n",
    "print(accuracy_score(y_test_s,y_pred_s))\n",
    "print(classification_report(y_test_s, y_pred_s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "9568706f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abhishek\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(nbclf, X_test_s, y_test_s)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "02111fe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfclf = RandomForestClassifier(max_depth = 10, max_features= 25, n_estimators= 100,class_weight= {0:1,1:1})\n",
    "rfclf = rfclf.fit(X_train_s,y_train_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "45726fce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9389192886456909\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.91      0.94      7290\n",
      "           1       0.92      0.96      0.94      7330\n",
      "\n",
      "    accuracy                           0.94     14620\n",
      "   macro avg       0.94      0.94      0.94     14620\n",
      "weighted avg       0.94      0.94      0.94     14620\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_s = rfclf.predict(X_test_s)\n",
    "print(accuracy_score(y_test_s,y_pred_s))\n",
    "print(classification_report(y_test_s, y_pred_s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "d17f938a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abhishek\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(rfclf, X_test_s, y_test_s)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51f6fa1e",
   "metadata": {},
   "source": [
    "## BorderlineSMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f3195d99",
   "metadata": {},
   "outputs": [],
   "source": [
    "oversample_bs = BorderlineSMOTE()\n",
    "X_bs, y_bs = oversample_bs.fit_resample(df, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "c2b81af5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({0: 36548, 1: 36548})\n"
     ]
    }
   ],
   "source": [
    "cnt_3 = Counter(y_bs)\n",
    "print(cnt_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "62b7af8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_bs, X_test_bs, y_train_bs, y_test_bs = train_test_split(X_bs, y_bs, test_size = 0.2, random_state = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "74ecc697",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtclf = DecisionTreeClassifier( max_depth=8, criterion='gini',class_weight= {0:1,1:1})\n",
    "dtclf= dtclf.fit(X_train_bs,y_train_bs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "9bc76769",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.918467852257182\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.88      0.91      7290\n",
      "           1       0.89      0.96      0.92      7330\n",
      "\n",
      "    accuracy                           0.92     14620\n",
      "   macro avg       0.92      0.92      0.92     14620\n",
      "weighted avg       0.92      0.92      0.92     14620\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_bs = dtclf.predict(X_test_bs)\n",
    "print(accuracy_score(y_test_bs,y_pred_bs))\n",
    "print(classification_report(y_test_bs, y_pred_bs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "99cc6917",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abhishek\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(dtclf, X_test_bs, y_test_bs)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "fa7699ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfclf = RandomForestClassifier(max_depth = 10, max_features= 25, n_estimators= 100,class_weight= {0:1,1:1})\n",
    "rfclf = rfclf.fit(X_train_bs, y_train_bs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "18f6a9ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9415184678522572\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.91      0.94      7290\n",
      "           1       0.92      0.97      0.94      7330\n",
      "\n",
      "    accuracy                           0.94     14620\n",
      "   macro avg       0.94      0.94      0.94     14620\n",
      "weighted avg       0.94      0.94      0.94     14620\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_bs = rfclf.predict(X_test_bs)\n",
    "print(accuracy_score(y_test_bs,y_pred_bs))\n",
    "print(classification_report(y_test_bs, y_pred_bs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "197dcfb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abhishek\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(rfclf, X_test_bs, y_test_bs)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "3a27f83f",
   "metadata": {},
   "outputs": [],
   "source": [
    "nbclf = GaussianNB()\n",
    "nbclf = nbclf.fit(X_train_bs, y_train_bs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "a6301431",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8646374829001368\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.85      0.86      7290\n",
      "           1       0.86      0.88      0.87      7330\n",
      "\n",
      "    accuracy                           0.86     14620\n",
      "   macro avg       0.86      0.86      0.86     14620\n",
      "weighted avg       0.86      0.86      0.86     14620\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_bs = nbclf.predict(X_test_bs)\n",
    "print(accuracy_score(y_test_bs,y_pred_bs))\n",
    "print(classification_report(y_test_bs, y_pred_bs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "cbc50d66",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abhishek\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(nbclf, X_test_bs, y_test_bs)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b57ee2f",
   "metadata": {},
   "source": [
    "## SVMSMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "82dd6524",
   "metadata": {},
   "outputs": [],
   "source": [
    "oversample_ss = SVMSMOTE()\n",
    "X_ss, y_ss = oversample_ss.fit_resample(df, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "43b41386",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({0: 36548, 1: 36548})\n"
     ]
    }
   ],
   "source": [
    "cnt_4 = Counter(y_ss)\n",
    "print(cnt_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "aba9e09f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_ss, X_test_ss, y_train_ss, y_test_ss = train_test_split(X_ss, y_ss, test_size = 0.2, random_state = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "57932493",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtclf = DecisionTreeClassifier( max_depth=8, criterion='gini',class_weight= {0:1,1:1})\n",
    "dtclf= dtclf.fit(X_train_ss, y_train_ss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "51019bde",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9018467852257182\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.88      0.90      7290\n",
      "           1       0.88      0.93      0.90      7330\n",
      "\n",
      "    accuracy                           0.90     14620\n",
      "   macro avg       0.90      0.90      0.90     14620\n",
      "weighted avg       0.90      0.90      0.90     14620\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_ss = dtclf.predict(X_test_ss)\n",
    "print(accuracy_score(y_test_ss, y_pred_ss))\n",
    "print(classification_report(y_test_ss, y_pred_ss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "26ffd8c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abhishek\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(dtclf, X_test_ss, y_test_ss)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "b012aa9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfclf = RandomForestClassifier(max_depth = 10, max_features= 25, n_estimators= 100,class_weight= {0:1,1:1})\n",
    "rfclf = rfclf.fit(X_train_ss, y_train_ss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "85db0ed4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9399452804377565\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.91      0.94      7290\n",
      "           1       0.92      0.97      0.94      7330\n",
      "\n",
      "    accuracy                           0.94     14620\n",
      "   macro avg       0.94      0.94      0.94     14620\n",
      "weighted avg       0.94      0.94      0.94     14620\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_ss = rfclf.predict(X_test_ss)\n",
    "print(accuracy_score(y_test_ss, y_pred_ss))\n",
    "print(classification_report(y_test_ss, y_pred_ss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "6c7f29b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abhishek\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(rfclf, X_test_ss, y_test_ss)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "d9ee5789",
   "metadata": {},
   "outputs": [],
   "source": [
    "nbclf = GaussianNB()\n",
    "nbclf = nbclf.fit(X_train_ss, y_train_ss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "3031aeec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8253077975376197\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.89      0.84      7290\n",
      "           1       0.87      0.76      0.81      7330\n",
      "\n",
      "    accuracy                           0.83     14620\n",
      "   macro avg       0.83      0.83      0.82     14620\n",
      "weighted avg       0.83      0.83      0.82     14620\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_ss = nbclf.predict(X_test_ss)\n",
    "print(accuracy_score(y_test_ss, y_pred_ss))\n",
    "print(classification_report(y_test_ss, y_pred_ss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "181a1797",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abhishek\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(nbclf, X_test_ss, y_test_ss)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4966220",
   "metadata": {},
   "source": [
    "## ADASYN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "a59f17ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "oversample_as = ADASYN()\n",
    "X_as, y_as = oversample_as.fit_resample(df, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "85bb3f9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({1: 36900, 0: 36548})\n"
     ]
    }
   ],
   "source": [
    "cnt_5 = Counter(y_as)\n",
    "print(cnt_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "0daa2f2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_as, X_test_as, y_train_as, y_test_as = train_test_split(X_as, y_as, test_size = 0.2, random_state = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "c4b8a53b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtclf = DecisionTreeClassifier( max_depth=8, criterion='gini',class_weight= {0:1,1:1})\n",
    "dtclf= dtclf.fit(X_train_as, y_train_as)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "dfcf3f29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9221238938053097\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.90      0.92      7271\n",
      "           1       0.90      0.95      0.92      7419\n",
      "\n",
      "    accuracy                           0.92     14690\n",
      "   macro avg       0.92      0.92      0.92     14690\n",
      "weighted avg       0.92      0.92      0.92     14690\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_as = dtclf.predict(X_test_as)\n",
    "print(accuracy_score(y_test_as, y_pred_as))\n",
    "print(classification_report(y_test_as, y_pred_as))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "0542188f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abhishek\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(dtclf, X_test_as, y_test_as)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "e1365c47",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfclf = RandomForestClassifier(max_depth = 10, max_features= 25, n_estimators= 100,class_weight= {0:1,1:1})\n",
    "rfclf = rfclf.fit(X_train_as, y_train_as)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "f81c2c4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9394826412525528\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.91      0.94      7271\n",
      "           1       0.92      0.97      0.94      7419\n",
      "\n",
      "    accuracy                           0.94     14690\n",
      "   macro avg       0.94      0.94      0.94     14690\n",
      "weighted avg       0.94      0.94      0.94     14690\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_as = rfclf.predict(X_test_as)\n",
    "print(accuracy_score(y_test_as, y_pred_as))\n",
    "print(classification_report(y_test_as, y_pred_as))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "3970fcb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abhishek\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(rfclf, X_test_as, y_test_as)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "3f1046cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "nbclf = GaussianNB()\n",
    "nbclf = nbclf.fit(X_train_as, y_train_as)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "2557ca10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8730428863172226\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.87      0.87      7271\n",
      "           1       0.87      0.88      0.87      7419\n",
      "\n",
      "    accuracy                           0.87     14690\n",
      "   macro avg       0.87      0.87      0.87     14690\n",
      "weighted avg       0.87      0.87      0.87     14690\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_as = nbclf.predict(X_test_as)\n",
    "print(accuracy_score(y_test_as, y_pred_as))\n",
    "print(classification_report(y_test_as, y_pred_as))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "5371a22c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abhishek\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(nbclf, X_test_as, y_test_as)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}