ML-Telecom_Churn_Prediction-Kaggle.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import scale, StandardScaler\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, mean_squared_error, r2_score, roc_auc_score, roc_curve, classification_report\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.svm import SVC\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('WA_Fn-UseC_-Telco-Customer-Churn.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**EDA**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity OnlineBackup  \\\n",
       "0  No phone service             DSL             No          Yes   \n",
       "1                No             DSL            Yes           No   \n",
       "2                No             DSL            Yes          Yes   \n",
       "\n",
       "  DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
       "0               No          No          No              No  Month-to-month   \n",
       "1              Yes          No          No              No        One year   \n",
       "2               No          No          No              No  Month-to-month   \n",
       "\n",
       "  PaperlessBilling     PaymentMethod  MonthlyCharges TotalCharges Churn  \n",
       "0              Yes  Electronic check           29.85        29.85    No  \n",
       "1               No      Mailed check           56.95       1889.5    No  \n",
       "2              Yes      Mailed check           53.85       108.15   Yes  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 21)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        7043 non-null   object \n",
      " 1   gender            7043 non-null   object \n",
      " 2   SeniorCitizen     7043 non-null   int64  \n",
      " 3   Partner           7043 non-null   object \n",
      " 4   Dependents        7043 non-null   object \n",
      " 5   tenure            7043 non-null   int64  \n",
      " 6   PhoneService      7043 non-null   object \n",
      " 7   MultipleLines     7043 non-null   object \n",
      " 8   InternetService   7043 non-null   object \n",
      " 9   OnlineSecurity    7043 non-null   object \n",
      " 10  OnlineBackup      7043 non-null   object \n",
      " 11  DeviceProtection  7043 non-null   object \n",
      " 12  TechSupport       7043 non-null   object \n",
      " 13  StreamingTV       7043 non-null   object \n",
      " 14  StreamingMovies   7043 non-null   object \n",
      " 15  Contract          7043 non-null   object \n",
      " 16  PaperlessBilling  7043 non-null   object \n",
      " 17  PaymentMethod     7043 non-null   object \n",
      " 18  MonthlyCharges    7043 non-null   float64\n",
      " 19  TotalCharges      7043 non-null   object \n",
      " 20  Churn             7043 non-null   object \n",
      "dtypes: float64(1), int64(2), object(18)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df['TotalCharges'] = df['TotalCharges'].apply(pd.to_numeric)\n",
    "#df = df.astype({\"TotalCharges\": float64})\n",
    "#df[\"TotalCharges\"] = pd.to_numeric(df[\"TotalCharges\"], downcast=\"float\")\n",
    "df['TotalCharges'] = df['TotalCharges'].apply(pd.to_numeric, downcast='float', errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        7043 non-null   object \n",
      " 1   gender            7043 non-null   object \n",
      " 2   SeniorCitizen     7043 non-null   int64  \n",
      " 3   Partner           7043 non-null   object \n",
      " 4   Dependents        7043 non-null   object \n",
      " 5   tenure            7043 non-null   int64  \n",
      " 6   PhoneService      7043 non-null   object \n",
      " 7   MultipleLines     7043 non-null   object \n",
      " 8   InternetService   7043 non-null   object \n",
      " 9   OnlineSecurity    7043 non-null   object \n",
      " 10  OnlineBackup      7043 non-null   object \n",
      " 11  DeviceProtection  7043 non-null   object \n",
      " 12  TechSupport       7043 non-null   object \n",
      " 13  StreamingTV       7043 non-null   object \n",
      " 14  StreamingMovies   7043 non-null   object \n",
      " 15  Contract          7043 non-null   object \n",
      " 16  PaperlessBilling  7043 non-null   object \n",
      " 17  PaymentMethod     7043 non-null   object \n",
      " 18  MonthlyCharges    7043 non-null   float64\n",
      " 19  TotalCharges      7032 non-null   float64\n",
      " 20  Churn             7043 non-null   object \n",
      "dtypes: float64(2), int64(2), object(17)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID           0\n",
       "gender               0\n",
       "SeniorCitizen        0\n",
       "Partner              0\n",
       "Dependents           0\n",
       "tenure               0\n",
       "PhoneService         0\n",
       "MultipleLines        0\n",
       "InternetService      0\n",
       "OnlineSecurity       0\n",
       "OnlineBackup         0\n",
       "DeviceProtection     0\n",
       "TechSupport          0\n",
       "StreamingTV          0\n",
       "StreamingMovies      0\n",
       "Contract             0\n",
       "PaperlessBilling     0\n",
       "PaymentMethod        0\n",
       "MonthlyCharges       0\n",
       "TotalCharges        11\n",
       "Churn                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7032.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.162147</td>\n",
       "      <td>32.371149</td>\n",
       "      <td>64.761692</td>\n",
       "      <td>2283.300441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.368612</td>\n",
       "      <td>24.559481</td>\n",
       "      <td>30.090047</td>\n",
       "      <td>2266.771363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.250000</td>\n",
       "      <td>18.799999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>35.500000</td>\n",
       "      <td>401.449997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>70.350000</td>\n",
       "      <td>1397.475037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>89.850000</td>\n",
       "      <td>3794.737488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>118.750000</td>\n",
       "      <td>8684.799805</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SeniorCitizen       tenure  MonthlyCharges  TotalCharges\n",
       "count    7043.000000  7043.000000     7043.000000   7032.000000\n",
       "mean        0.162147    32.371149       64.761692   2283.300441\n",
       "std         0.368612    24.559481       30.090047   2266.771363\n",
       "min         0.000000     0.000000       18.250000     18.799999\n",
       "25%         0.000000     9.000000       35.500000    401.449997\n",
       "50%         0.000000    29.000000       70.350000   1397.475037\n",
       "75%         0.000000    55.000000       89.850000   3794.737488\n",
       "max         1.000000    72.000000      118.750000   8684.799805"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.corr()['TotalCharges'].sort_values().head(10).plot.barh();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5901\n",
       "1    1142\n",
       "Name: SeniorCitizen, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SeniorCitizen'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['SeniorCitizen'] = df['SeniorCitizen'].map({0:'No', 1:'Yes'}) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"SeniorCitizen\"] = df[\"SeniorCitizen\"].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.850000</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.500000</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.150002</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  7590-VHVEG  Female            No     Yes         No       1           No   \n",
       "1  5575-GNVDE    Male            No      No         No      34          Yes   \n",
       "2  3668-QPYBK    Male            No      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity OnlineBackup  \\\n",
       "0  No phone service             DSL             No          Yes   \n",
       "1                No             DSL            Yes           No   \n",
       "2                No             DSL            Yes          Yes   \n",
       "\n",
       "  DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
       "0               No          No          No              No  Month-to-month   \n",
       "1              Yes          No          No              No        One year   \n",
       "2               No          No          No              No  Month-to-month   \n",
       "\n",
       "  PaperlessBilling     PaymentMethod  MonthlyCharges  TotalCharges Churn  \n",
       "0              Yes  Electronic check           29.85     29.850000    No  \n",
       "1               No      Mailed check           56.95   1889.500000    No  \n",
       "2              Yes      Mailed check           53.85    108.150002   Yes  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('customerID', axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 20)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df, drop_first= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 31)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>SeniorCitizen_Yes</th>\n",
       "      <th>Partner_Yes</th>\n",
       "      <th>Dependents_Yes</th>\n",
       "      <th>PhoneService_Yes</th>\n",
       "      <th>MultipleLines_No phone service</th>\n",
       "      <th>MultipleLines_Yes</th>\n",
       "      <th>InternetService_Fiber optic</th>\n",
       "      <th>InternetService_No</th>\n",
       "      <th>OnlineSecurity_No internet service</th>\n",
       "      <th>OnlineSecurity_Yes</th>\n",
       "      <th>OnlineBackup_No internet service</th>\n",
       "      <th>OnlineBackup_Yes</th>\n",
       "      <th>DeviceProtection_No internet service</th>\n",
       "      <th>DeviceProtection_Yes</th>\n",
       "      <th>TechSupport_No internet service</th>\n",
       "      <th>TechSupport_Yes</th>\n",
       "      <th>StreamingTV_No internet service</th>\n",
       "      <th>StreamingTV_Yes</th>\n",
       "      <th>StreamingMovies_No internet service</th>\n",
       "      <th>StreamingMovies_Yes</th>\n",
       "      <th>Contract_One year</th>\n",
       "      <th>Contract_Two year</th>\n",
       "      <th>PaperlessBilling_Yes</th>\n",
       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <th>PaymentMethod_Mailed check</th>\n",
       "      <th>Churn_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.850000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <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",
       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.500000</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.150002</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>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>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tenure  MonthlyCharges  TotalCharges  gender_Male  SeniorCitizen_Yes  \\\n",
       "0       1           29.85     29.850000            0                  0   \n",
       "1      34           56.95   1889.500000            1                  0   \n",
       "2       2           53.85    108.150002            1                  0   \n",
       "\n",
       "   Partner_Yes  Dependents_Yes  PhoneService_Yes  \\\n",
       "0            1               0                 0   \n",
       "1            0               0                 1   \n",
       "2            0               0                 1   \n",
       "\n",
       "   MultipleLines_No phone service  MultipleLines_Yes  \\\n",
       "0                               1                  0   \n",
       "1                               0                  0   \n",
       "2                               0                  0   \n",
       "\n",
       "   InternetService_Fiber optic  InternetService_No  \\\n",
       "0                            0                   0   \n",
       "1                            0                   0   \n",
       "2                            0                   0   \n",
       "\n",
       "   OnlineSecurity_No internet service  OnlineSecurity_Yes  \\\n",
       "0                                   0                   0   \n",
       "1                                   0                   1   \n",
       "2                                   0                   1   \n",
       "\n",
       "   OnlineBackup_No internet service  OnlineBackup_Yes  \\\n",
       "0                                 0                 1   \n",
       "1                                 0                 0   \n",
       "2                                 0                 1   \n",
       "\n",
       "   DeviceProtection_No internet service  DeviceProtection_Yes  \\\n",
       "0                                     0                     0   \n",
       "1                                     0                     1   \n",
       "2                                     0                     0   \n",
       "\n",
       "   TechSupport_No internet service  TechSupport_Yes  \\\n",
       "0                                0                0   \n",
       "1                                0                0   \n",
       "2                                0                0   \n",
       "\n",
       "   StreamingTV_No internet service  StreamingTV_Yes  \\\n",
       "0                                0                0   \n",
       "1                                0                0   \n",
       "2                                0                0   \n",
       "\n",
       "   StreamingMovies_No internet service  StreamingMovies_Yes  \\\n",
       "0                                    0                    0   \n",
       "1                                    0                    0   \n",
       "2                                    0                    0   \n",
       "\n",
       "   Contract_One year  Contract_Two year  PaperlessBilling_Yes  \\\n",
       "0                  0                  0                     1   \n",
       "1                  1                  0                     0   \n",
       "2                  0                  0                     1   \n",
       "\n",
       "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "0                                      0                               1   \n",
       "1                                      0                               0   \n",
       "2                                      0                               0   \n",
       "\n",
       "   PaymentMethod_Mailed check  Churn_Yes  \n",
       "0                           0          0  \n",
       "1                           1          0  \n",
       "2                           1          1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.corr()['Churn_Yes'].sort_values().head(15).plot.barh();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.corr()['Churn_Yes'].sort_values().tail(16).plot.barh();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "df.corr()['Churn_Yes'].sort_values(ascending = False).plot(kind='bar');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sns.pairplot(df);  # takes too much time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = sns.boxplot(data=df, y = 'MonthlyCharges')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gJ/BegMzcHhEfA+4s/T6amdsbrFuStJfGwiIzb2Py8w0ASyfpn8BFUxzrauDqg1edJGl/eAe3JKnKsJAkVRkWkqQqw0KSVGVYSJKqDAtJUpVhIUmqavKmvFlt27ZtzNn5BPPuXT/oUiS13JydP2Xbtt2DLqMnRxaSpCpHFg0ZHh7mx8/MZddp5w66FEktN+/e9QwPnzzoMnpyZCFJqjIsJElVhoUkqcqwkCRVGRaSpCrDQpJUZVhIkqoMC0lSlWEhSaoyLCRJVYaFJKnKsJAkVRkWkqQqw0KSVGVYSJKqDAtJUpVhIUmqMiwkSVWGhSSpyrCQJFXNHXQBh7I5O7cz7971gy5DLXLY008C8Msjjx1wJWqTOTu3AycPuoyeDIuGjIyMDLoEtdDY2FMAjLyi3X8YNN1Obv3fDMOiIatWrRp0CWqh1atXA7BmzZoBVyLtH89ZSJKqDAtJUpVhIUmqmjFhERFnR8R9ETEWEZcMuh5Jmk1mRFhExBzgc8A5wGLgXRGxeLBVSdLsMVOuhloCjGXmAwARcR2wDLhnoFWpL2vXrmVsbGzQZbTCxH+HiauiZruRkRGvHJwhZsTIAhgGHu5a31rapBll3rx5zJs3b9BlSPttpowsYpK2fEGHiJXASoBTTjllOmpSn/yXozTzzZSRxVZgQdf6fOCR7g6ZuS4zRzNzdGhoaFqLk6RD3UwJizuBRRFxakQcASwHbhpwTZI0a8yIaajM3B0RfwHcDMwBrs7MuwdcliTNGjMiLAAycz3gI1wlaQBmyjSUJGmADAtJUpVhIUmqMiwkSVWRmfVeM0xEjAM/GnQd0hROAn4y6CKkSfx6Zk56o9ohGRZSm0XExswcHXQd0v5wGkqSVGVYSJKqDAtp+q0bdAHS/vKchSSpypGFJKnKsJAkVRkWkqQqw0KSVGVYSJKq/h+8cC01thK1bgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = sns.boxplot(data=df, y = 'TotalCharges')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = sns.boxplot(data=df, y = 'tenure')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['TotalCharges'], density=True, bins=30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(x=\"TotalCharges\", data=df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaling may work "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tenure                                   0\n",
       "MonthlyCharges                           0\n",
       "TotalCharges                             0\n",
       "gender_Male                              0\n",
       "SeniorCitizen_Yes                        0\n",
       "Partner_Yes                              0\n",
       "Dependents_Yes                           0\n",
       "PhoneService_Yes                         0\n",
       "MultipleLines_No phone service           0\n",
       "MultipleLines_Yes                        0\n",
       "InternetService_Fiber optic              0\n",
       "InternetService_No                       0\n",
       "OnlineSecurity_No internet service       0\n",
       "OnlineSecurity_Yes                       0\n",
       "OnlineBackup_No internet service         0\n",
       "OnlineBackup_Yes                         0\n",
       "DeviceProtection_No internet service     0\n",
       "DeviceProtection_Yes                     0\n",
       "TechSupport_No internet service          0\n",
       "TechSupport_Yes                          0\n",
       "StreamingTV_No internet service          0\n",
       "StreamingTV_Yes                          0\n",
       "StreamingMovies_No internet service      0\n",
       "StreamingMovies_Yes                      0\n",
       "Contract_One year                        0\n",
       "Contract_Two year                        0\n",
       "PaperlessBilling_Yes                     0\n",
       "PaymentMethod_Credit card (automatic)    0\n",
       "PaymentMethod_Electronic check           0\n",
       "PaymentMethod_Mailed check               0\n",
       "Churn_Yes                                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# correlation is quite low for some features, like gender_Male, PhoneService_Yes, MultipleLines_Nophone service, but I will not drop them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data pre-Processing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('Churn_Yes',axis=1)\n",
    "y = df['Churn_Yes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Logistic Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = log_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1151,  149],\n",
       "       [ 226,  232]], dtype=int64)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.89      0.86      1300\n",
      "           1       0.61      0.51      0.55       458\n",
      "\n",
      "    accuracy                           0.79      1758\n",
      "   macro avg       0.72      0.70      0.71      1758\n",
      "weighted avg       0.78      0.79      0.78      1758\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Decision Tree**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_model=DecisionTreeClassifier().fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1034,  266],\n",
       "       [ 230,  228]], dtype=int64)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = dt_model.predict(X_test)\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.80      0.81      1300\n",
      "           1       0.46      0.50      0.48       458\n",
      "\n",
      "    accuracy                           0.72      1758\n",
      "   macro avg       0.64      0.65      0.64      1758\n",
      "weighted avg       0.73      0.72      0.72      1758\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Random Forest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_model=RandomForestClassifier().fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1160,  140],\n",
       "       [ 235,  223]], dtype=int64)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rf_model.predict(X_test)\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.89      0.86      1300\n",
      "           1       0.61      0.49      0.54       458\n",
      "\n",
      "    accuracy                           0.79      1758\n",
      "   macro avg       0.72      0.69      0.70      1758\n",
      "weighted avg       0.77      0.79      0.78      1758\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**XGBOOST**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_model = XGBClassifier().fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1131,  169],\n",
       "       [ 231,  227]], dtype=int64)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = xgb_model.predict(X_test)\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.87      0.85      1300\n",
      "           1       0.57      0.50      0.53       458\n",
      "\n",
      "    accuracy                           0.77      1758\n",
      "   macro avg       0.70      0.68      0.69      1758\n",
      "weighted avg       0.76      0.77      0.77      1758\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**KNN**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_model = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1097,  203],\n",
       "       [ 230,  228]], dtype=int64)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = knn_model.predict(X_test)\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.84      0.84      1300\n",
      "           1       0.53      0.50      0.51       458\n",
      "\n",
      "    accuracy                           0.75      1758\n",
      "   macro avg       0.68      0.67      0.67      1758\n",
      "weighted avg       0.75      0.75      0.75      1758\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**SVM**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_model = SVC().fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1174,  126],\n",
       "       [ 238,  220]], dtype=int64)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = svm_model.predict(X_test)\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.90      0.87      1300\n",
      "           1       0.64      0.48      0.55       458\n",
      "\n",
      "    accuracy                           0.79      1758\n",
      "   macro avg       0.73      0.69      0.71      1758\n",
      "weighted avg       0.78      0.79      0.78      1758\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**GridSearch with SVM**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_params = {\"kernel\": [\"linear\",\"rbf\", \"poly\", \"sigmoid\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   8 out of  20 | elapsed:    4.1s remaining:    6.3s\n",
      "[Parallel(n_jobs=-1)]: Done  20 out of  20 | elapsed:    7.1s finished\n"
     ]
    }
   ],
   "source": [
    "svm_cv_model = GridSearchCV(svm, svm_params, cv = 5, n_jobs = -1, verbose = 2).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'kernel': 'linear'}"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_cv_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm = SVC(kernel=\"linear\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_params = {\"C\": [0.1,0.5,1,10,20,50]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  23 out of  30 | elapsed:   19.3s remaining:    5.8s\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed:   28.6s finished\n"
     ]
    }
   ],
   "source": [
    "svm_cv_model = GridSearchCV(svm, svm_params, cv = 5, n_jobs = -1, verbose = 2).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 10}"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_cv_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_tuned = SVC(C = 10, kernel=\"rbf\").fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1122,  178],\n",
       "       [ 228,  230]], dtype=int64)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = svm_tuned.predict(X_test)\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.86      0.85      1300\n",
      "           1       0.56      0.50      0.53       458\n",
      "\n",
      "    accuracy                           0.77      1758\n",
      "   macro avg       0.70      0.68      0.69      1758\n",
      "weighted avg       0.76      0.77      0.76      1758\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_new = SVC(C = 10, kernel=\"linear\").fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1156,  144],\n",
       "       [ 212,  246]], dtype=int64)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = svm_new.predict(X_test)\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.89      0.87      1300\n",
      "           1       0.63      0.54      0.58       458\n",
      "\n",
      "    accuracy                           0.80      1758\n",
      "   macro avg       0.74      0.71      0.72      1758\n",
      "weighted avg       0.79      0.80      0.79      1758\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_new2 = SVC(C = 1, kernel=\"linear\").fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1156,  144],\n",
       "       [ 214,  244]], dtype=int64)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = svm_new2.predict(X_test)\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.89      0.87      1300\n",
      "           1       0.63      0.53      0.58       458\n",
      "\n",
      "    accuracy                           0.80      1758\n",
      "   macro avg       0.74      0.71      0.72      1758\n",
      "weighted avg       0.79      0.80      0.79      1758\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Random Forest Tuning**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_params = {'n_estimators' : [50, 100, 300],\n",
    "             'max_depth': [3, 5, 7],\n",
    "             'max_features': [2, 4, 6, 8],\n",
    "             'min_samples_split': [2, 4, 6]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 108 candidates, totalling 540 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed:    4.2s\n",
      "[Parallel(n_jobs=-1)]: Done 138 tasks      | elapsed:   20.9s\n",
      "[Parallel(n_jobs=-1)]: Done 341 tasks      | elapsed:   49.8s\n",
      "[Parallel(n_jobs=-1)]: Done 540 out of 540 | elapsed:  1.6min finished\n"
     ]
    }
   ],
   "source": [
    "rf_cv_model = GridSearchCV(rf, rf_params, cv = 5, n_jobs = -1, verbose = 2).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 7, 'max_features': 4, 'min_samples_split': 4, 'n_estimators': 50}"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_cv_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_tuned = RandomForestClassifier(max_depth = 7,\n",
    "                                  max_features = 4,\n",
    "                                  min_samples_split = 4,\n",
    "                                  n_estimators = 50).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.91      0.86      1300\n",
      "           1       0.64      0.45      0.53       458\n",
      "\n",
      "    accuracy                           0.79      1758\n",
      "   macro avg       0.73      0.68      0.70      1758\n",
      "weighted avg       0.78      0.79      0.78      1758\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = rf_tuned.predict(X_test)\n",
    "confusion_matrix(y_test, y_pred)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Tuning for XGBOOST**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb = XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_params = {\"n_estimators\": [50, 100, 300],\n",
    "             \"subsample\":[0.5,0.8,1],\n",
    "             \"max_depth\":[3,5,7],\n",
    "             \"learning_rate\":[0.1,0.01,0.3]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 81 candidates, totalling 405 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed:   13.3s\n",
      "[Parallel(n_jobs=-1)]: Done 138 tasks      | elapsed:   48.9s\n",
      "[Parallel(n_jobs=-1)]: Done 341 tasks      | elapsed:  1.6min\n",
      "[Parallel(n_jobs=-1)]: Done 405 out of 405 | elapsed:  2.1min finished\n"
     ]
    }
   ],
   "source": [
    "xgb_cv_model = GridSearchCV(xgb, xgb_params, cv = 5,\n",
    "                            n_jobs = -1, verbose = 2).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 100, 'subsample': 1}"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_cv_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_tuned = XGBClassifier(learning_rate= 0.1,\n",
    "                                max_depth= 3,\n",
    "                                n_estimators= 300,\n",
    "                                subsample= 1).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = xgb_tuned.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.90      0.87      1300\n",
      "           1       0.63      0.51      0.56       458\n",
      "\n",
      "    accuracy                           0.80      1758\n",
      "   macro avg       0.74      0.70      0.72      1758\n",
      "weighted avg       0.78      0.80      0.79      1758\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1165,  135],\n",
       "       [ 225,  233]], dtype=int64)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_imp = pd.Series(xgb_tuned.feature_importances_,\n",
    "                        index=X_train.columns).sort_values(ascending=False)\n",
    "sns.barplot(x=feature_imp, y=feature_imp.index)\n",
    "plt.title(\"Feature Importance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}