Customer churn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "innocent-intelligence",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "from imblearn.over_sampling import SMOTENC\n",
    "from sklearn.feature_selection import VarianceThreshold\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from sklearn.feature_selection import SelectKBest, chi2\n",
    "import xgboost\n",
    "import lightgbm as lgb\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "searching-perspective",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</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>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</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>70.70</td>\n",
       "      <td>151.65</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",
       "3  7795-CFOCW    Male              0      No         No      45           No   \n",
       "4  9237-HQITU  Female              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",
       "3  No phone service             DSL            Yes           No   \n",
       "4                No     Fiber optic             No           No   \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",
       "3              Yes         Yes          No              No        One year   \n",
       "4               No          No          No              No  Month-to-month   \n",
       "\n",
       "  PaperlessBilling              PaymentMethod  MonthlyCharges TotalCharges  \\\n",
       "0              Yes           Electronic check           29.85        29.85   \n",
       "1               No               Mailed check           56.95       1889.5   \n",
       "2              Yes               Mailed check           53.85       108.15   \n",
       "3               No  Bank transfer (automatic)           42.30      1840.75   \n",
       "4              Yes           Electronic check           70.70       151.65   \n",
       "\n",
       "  Churn  \n",
       "0    No  \n",
       "1    No  \n",
       "2   Yes  \n",
       "3    No  \n",
       "4   Yes  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option(\"max_columns\",50)\n",
    "data=pd.read_csv('C:/Users/Azerty/Desktop/Customer churn/data.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "standing-third",
   "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": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "failing-luther",
   "metadata": {},
   "source": [
    "# Feature engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pediatric-robert",
   "metadata": {},
   "source": [
    "## Categorical Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "genetic-convergence",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column: customerID - Nbr of unique values: 7043 - unique values ['7590-VHVEG' '5575-GNVDE' '3668-QPYBK' ... '4801-JZAZL' '8361-LTMKD'\n",
      " '3186-AJIEK'] \n",
      "Column: gender - Nbr of unique values: 2 - unique values ['Female' 'Male'] \n",
      "Column: Partner - Nbr of unique values: 2 - unique values ['Yes' 'No'] \n",
      "Column: Dependents - Nbr of unique values: 2 - unique values ['No' 'Yes'] \n",
      "Column: PhoneService - Nbr of unique values: 2 - unique values ['No' 'Yes'] \n",
      "Column: MultipleLines - Nbr of unique values: 3 - unique values ['No phone service' 'No' 'Yes'] \n",
      "Column: InternetService - Nbr of unique values: 3 - unique values ['DSL' 'Fiber optic' 'No'] \n",
      "Column: OnlineSecurity - Nbr of unique values: 3 - unique values ['No' 'Yes' 'No internet service'] \n",
      "Column: OnlineBackup - Nbr of unique values: 3 - unique values ['Yes' 'No' 'No internet service'] \n",
      "Column: DeviceProtection - Nbr of unique values: 3 - unique values ['No' 'Yes' 'No internet service'] \n",
      "Column: TechSupport - Nbr of unique values: 3 - unique values ['No' 'Yes' 'No internet service'] \n",
      "Column: StreamingTV - Nbr of unique values: 3 - unique values ['No' 'Yes' 'No internet service'] \n",
      "Column: StreamingMovies - Nbr of unique values: 3 - unique values ['No' 'Yes' 'No internet service'] \n",
      "Column: Contract - Nbr of unique values: 3 - unique values ['Month-to-month' 'One year' 'Two year'] \n",
      "Column: PaperlessBilling - Nbr of unique values: 2 - unique values ['Yes' 'No'] \n",
      "Column: PaymentMethod - Nbr of unique values: 4 - unique values ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
      " 'Credit card (automatic)'] \n",
      "Column: TotalCharges - Nbr of unique values: 6531 - unique values ['29.85' '1889.5' '108.15' ... '346.45' '306.6' '6844.5'] \n",
      "Column: Churn - Nbr of unique values: 2 - unique values ['No' 'Yes'] \n"
     ]
    }
   ],
   "source": [
    "for column in data.select_dtypes(include='object'):\n",
    "    print(\"Column: {} - Nbr of unique values: {} - unique values {} \".format(column, len(data[column].unique()),data[column].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "hawaiian-luxembourg",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(columns=['customerID'],inplace=True)\n",
    "data['TotalCharges']=pd.to_numeric(data['TotalCharges'],downcast='float',errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "czech-ordinary",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nbr of Nan values in TotalCharges column: 20 \n"
     ]
    }
   ],
   "source": [
    "print(\"Nbr of Nan values in TotalCharges column: {} \".format(data[data['TotalCharges'].isna()==True].shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "checked-warrant",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(subset=['TotalCharges'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "exempt-framing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5625, 19), (1407, 19))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train test split\n",
    "X=data[data.columns.difference(['Churn'])]\n",
    "y=data['Churn']\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X,\n",
    "    y,\n",
    "    test_size=0.2,\n",
    "    random_state=0)\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "optical-jacob",
   "metadata": {},
   "outputs": [],
   "source": [
    "catg = X_train.select_dtypes(include='object').columns\n",
    "l_encoder_X={}\n",
    "for feature in catg:\n",
    "    l_encoder=preprocessing.LabelEncoder()\n",
    "    X_train[feature] = l_encoder.fit_transform(X_train[feature])\n",
    "    l_encoder_X[feature]=l_encoder\n",
    "    \n",
    "l_encoder_y=preprocessing.LabelEncoder()\n",
    "y_train = pd.Series(l_encoder_y.fit_transform(y_train),index=X_train.index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "sporting-section",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Contract</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>Partner</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>gender</th>\n",
       "      <th>tenure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2964</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>58.35</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1346.900024</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5113</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>113.65</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8166.799805</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5363</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>25.35</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1715.150024</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
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       "    <tr>\n",
       "      <th>5074</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>19.45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>921.299988</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
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       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>84.15</td>\n",
       "      <td>2</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>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1821.949951</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Contract  Dependents  DeviceProtection  InternetService  MonthlyCharges  \\\n",
       "2964         1           0                 0                0           58.35   \n",
       "5113         2           1                 2                1          113.65   \n",
       "5363         2           1                 1                2           25.35   \n",
       "5074         1           1                 1                2           19.45   \n",
       "156          0           0                 0                1           84.15   \n",
       "\n",
       "      MultipleLines  OnlineBackup  OnlineSecurity  PaperlessBilling  Partner  \\\n",
       "2964              2             0               2                 1        1   \n",
       "5113              2             2               2                 0        1   \n",
       "5363              2             1               1                 0        1   \n",
       "5074              0             1               1                 0        0   \n",
       "156               2             0               0                 1        0   \n",
       "\n",
       "      PaymentMethod  PhoneService  SeniorCitizen  StreamingMovies  \\\n",
       "2964              2             1              0                0   \n",
       "5113              1             1              0                2   \n",
       "5363              3             1              0                1   \n",
       "5074              1             1              0                1   \n",
       "156               0             1              0                0   \n",
       "\n",
       "      StreamingTV  TechSupport  TotalCharges  gender  tenure  \n",
       "2964            0            2   1346.900024       1      24  \n",
       "5113            2            2   8166.799805       0      71  \n",
       "5363            1            1   1715.150024       1      70  \n",
       "5074            1            1    921.299988       0      49  \n",
       "156             2            0   1821.949951       0      22  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "annual-document",
   "metadata": {},
   "source": [
    "## Dealing with class imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fresh-hepatitis",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Churn countplot\n",
    "y_train.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "functional-printing",
   "metadata": {},
   "source": [
    "**Observation :** problem of unbalanced data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dutch-classic",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of elements in class 1 : 73% , Number of elements in class 0 : 27%\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of elements in class 1 : {}% , Number of elements in class 0 : {}%\".format(round(y_train[y_train==0].count()*100/y_train.shape[0]),round(y_train[y_train==1].count()*100/y_train.shape[0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "medium-clerk",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of elements in class 1 : 50% , Number of elements in class 0 : 50%\n"
     ]
    }
   ],
   "source": [
    "# create synthetic data using SMOTENC\n",
    "categorical_features=[i for i in range(0,19) if i not in [4,16]]\n",
    "smote_nc = SMOTENC(categorical_features=categorical_features, random_state=0)\n",
    "X_train_res, y_train_res = smote_nc.fit_resample(X_train, y_train)\n",
    "print(\"Number of elements in class 1 : {}% , Number of elements in class 0 : {}%\".format(round(y_train_res[y_train_res==0].count()*100/y_train_res.shape[0]),round(y_train_res[y_train_res==1].count()*100/y_train_res.shape[0])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "warming-companion",
   "metadata": {},
   "source": [
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "measured-change",
   "metadata": {},
   "source": [
    "### Variance threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "indie-dragon",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# variance threshold : check if there is  constant columns by using VarianceThreshold\n",
    "var_thres=VarianceThreshold(threshold=0)\n",
    "var_thres.fit(X_train_res)\n",
    "var_thres.get_support()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "amateur-exception",
   "metadata": {},
   "source": [
    "### Pairwise correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "corporate-consumption",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Using Pearson Correlation\n",
    "plt.figure(figsize=(12,12))\n",
    "cor = X_train_res.corr()\n",
    "sns.heatmap(abs(cor), annot=True, cmap=plt.cm.CMRmap_r)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rural-seating",
   "metadata": {},
   "source": [
    "I have choosed as correlation tolerance 0.7 value. thus, i will drop either the column TotalCharges or tenure. For that i will discard variable with weaker correlation with the target (Churn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "numerous-magazine",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Contract            0.509550\n",
       "tenure              0.430857\n",
       "OnlineSecurity      0.390436\n",
       "TechSupport         0.381078\n",
       "PaperlessBilling    0.284586\n",
       "Dependents          0.258693\n",
       "OnlineBackup        0.256792\n",
       "TotalCharges        0.244287\n",
       "MonthlyCharges      0.227991\n",
       "DeviceProtection    0.222534\n",
       "Partner             0.213569\n",
       "PaymentMethod       0.199492\n",
       "SeniorCitizen       0.139373\n",
       "MultipleLines       0.048695\n",
       "StreamingTV         0.042306\n",
       "InternetService     0.038542\n",
       "StreamingMovies     0.031382\n",
       "PhoneService        0.021542\n",
       "gender              0.013821\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs(X_train_res.corrwith(y_train_res,method=\"pearson\",axis = 0)).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "published-equivalent",
   "metadata": {},
   "source": [
    "TotalCharges has weaker coorelation with target than Tenure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "expired-memory",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_res.drop(columns=['TotalCharges'],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "offensive-contractor",
   "metadata": {},
   "source": [
    "**Other observations :** \n",
    "\n",
    "MonthlyCharges and TotalCharges are also correlated (moderate correlation ≈ 0.6) which it is makes sense : TotalCharges = Monthly Charges * tenure.\n",
    "\n",
    "There is also a moderate positive relationship between Contract and Tenure\n",
    "\n",
    "The fact there was two variables highly correlated (> 0.8) is a sign indicating that a severe **multicollinearity** may be present. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "exclusive-syndicate",
   "metadata": {},
   "source": [
    "### Multicollinearity "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "stretch-arrival",
   "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>Features</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MonthlyCharges</td>\n",
       "      <td>17.040936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>PhoneService</td>\n",
       "      <td>16.338968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>tenure</td>\n",
       "      <td>7.010741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>InternetService</td>\n",
       "      <td>4.609323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PaperlessBilling</td>\n",
       "      <td>3.633817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PaymentMethod</td>\n",
       "      <td>3.615176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Contract</td>\n",
       "      <td>3.576443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>StreamingTV</td>\n",
       "      <td>3.247887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>StreamingMovies</td>\n",
       "      <td>3.232414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MultipleLines</td>\n",
       "      <td>2.904518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Partner</td>\n",
       "      <td>2.653895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DeviceProtection</td>\n",
       "      <td>2.359146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>OnlineBackup</td>\n",
       "      <td>2.228968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>TechSupport</td>\n",
       "      <td>2.139877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>OnlineSecurity</td>\n",
       "      <td>2.025274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>gender</td>\n",
       "      <td>1.962401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dependents</td>\n",
       "      <td>1.884475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>SeniorCitizen</td>\n",
       "      <td>1.384663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Features        VIF\n",
       "4     MonthlyCharges  17.040936\n",
       "11      PhoneService  16.338968\n",
       "17            tenure   7.010741\n",
       "3    InternetService   4.609323\n",
       "8   PaperlessBilling   3.633817\n",
       "10     PaymentMethod   3.615176\n",
       "0           Contract   3.576443\n",
       "14       StreamingTV   3.247887\n",
       "13   StreamingMovies   3.232414\n",
       "5      MultipleLines   2.904518\n",
       "9            Partner   2.653895\n",
       "2   DeviceProtection   2.359146\n",
       "6       OnlineBackup   2.228968\n",
       "15       TechSupport   2.139877\n",
       "7     OnlineSecurity   2.025274\n",
       "16            gender   1.962401\n",
       "1         Dependents   1.884475\n",
       "12     SeniorCitizen   1.384663"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# VIF dataframe\n",
    "vif_data = pd.DataFrame()\n",
    "vif_data[\"Features\"] = X_train_res.columns\n",
    "  \n",
    "# calculating VIF for each feature\n",
    "vif_data[\"VIF\"] = [variance_inflation_factor(X_train_res.values, i)\n",
    "                   for i in range(len(X_train_res.columns))]\n",
    "\n",
    "vif_data.sort_values(ascending=False,by='VIF')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "distant-outside",
   "metadata": {},
   "source": [
    "Even after deleting TotalCharges, there still a sign of multicollinearity (VIF >10). However, for this case, it better to ignore the issue and live with what we have given the importance of the features involved. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "north-somewhere",
   "metadata": {},
   "source": [
    "### Statistical Test (For feature Selection)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dressed-testimony",
   "metadata": {},
   "source": [
    "**Note:** Given the very small number of quantitative data (Tenure and Monthly Charge), i will apply the feature selection  on only categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "local-albert",
   "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>p_value</th>\n",
       "      <th>compare_to_alpha(0.05)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Contract</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dependents</th>\n",
       "      <td>1.424148e-93</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DeviceProtection</th>\n",
       "      <td>2.714167e-90</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InternetService</th>\n",
       "      <td>1.141419e-02</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MultipleLines</th>\n",
       "      <td>1.842063e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineBackup</th>\n",
       "      <td>9.333039e-121</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <td>2.340133e-306</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <td>1.039193e-49</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partner</th>\n",
       "      <td>2.799719e-49</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentMethod</th>\n",
       "      <td>4.268000e-42</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PhoneService</th>\n",
       "      <td>5.474079e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <td>1.739680e-30</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingMovies</th>\n",
       "      <td>8.961354e-03</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingTV</th>\n",
       "      <td>3.774790e-04</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TechSupport</th>\n",
       "      <td>2.057261e-290</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>3.798509e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        p_value  compare_to_alpha(0.05)\n",
       "Contract           0.000000e+00                    True\n",
       "Dependents         1.424148e-93                    True\n",
       "DeviceProtection   2.714167e-90                    True\n",
       "InternetService    1.141419e-02                    True\n",
       "MultipleLines      1.842063e-05                    True\n",
       "OnlineBackup      9.333039e-121                    True\n",
       "OnlineSecurity    2.340133e-306                    True\n",
       "PaperlessBilling   1.039193e-49                    True\n",
       "Partner            2.799719e-49                    True\n",
       "PaymentMethod      4.268000e-42                    True\n",
       "PhoneService       5.474079e-01                   False\n",
       "SeniorCitizen      1.739680e-30                    True\n",
       "StreamingMovies    8.961354e-03                    True\n",
       "StreamingTV        3.774790e-04                    True\n",
       "TechSupport       2.057261e-290                    True\n",
       "gender             3.798509e-01                   False"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# chi-test : calculate p value and compare to the significance level (0.05)\n",
    "columns_cat= [feature for feature in X_train_res.columns if feature not in ['tenure','MonthlyCharges']]\n",
    "f_p_values=chi2(X_train_res[columns_cat],y_train_res)\n",
    "p_values=pd.DataFrame(f_p_values[1],index=columns_cat,columns=['p_value'])\n",
    "p_values['compare_to_alpha(0.05)']=p_values.apply(lambda y: True if y['p_value']<0.05 else False,axis=1)\n",
    "p_values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "musical-captain",
   "metadata": {},
   "source": [
    "**Conclusion :** PhoneService and gender are not a significants features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "southwest-provincial",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Contract</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>Partner</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>58.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>71</td>\n",
       "      <td>113.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>25.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>19.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>22</td>\n",
       "      <td>84.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8245</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>73.073982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8246</th>\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>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>84.582119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8247</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>94.773114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8248</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>93.422205</td>\n",
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       "    <tr>\n",
       "      <th>8249</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "      <td>103.727497</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8250 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Contract  Dependents  DeviceProtection  OnlineBackup  OnlineSecurity  \\\n",
       "0            1           0                 0             0               2   \n",
       "1            2           1                 2             2               2   \n",
       "2            2           1                 1             1               1   \n",
       "3            1           1                 1             1               1   \n",
       "4            0           0                 0             0               0   \n",
       "...        ...         ...               ...           ...             ...   \n",
       "8245         0           0                 2             0               0   \n",
       "8246         0           0                 0             0               0   \n",
       "8247         0           0                 2             0               0   \n",
       "8248         0           0                 2             0               0   \n",
       "8249         1           0                 0             2               0   \n",
       "\n",
       "      PaperlessBilling  Partner  PaymentMethod  SeniorCitizen  TechSupport  \\\n",
       "0                    1        1              2              0            2   \n",
       "1                    0        1              1              0            2   \n",
       "2                    0        1              3              0            1   \n",
       "3                    0        0              1              0            1   \n",
       "4                    1        0              0              0            0   \n",
       "...                ...      ...            ...            ...          ...   \n",
       "8245                 1        0              2              0            0   \n",
       "8246                 1        1              2              0            0   \n",
       "8247                 1        0              2              0            0   \n",
       "8248                 1        0              2              1            0   \n",
       "8249                 1        0              2              0            2   \n",
       "\n",
       "      tenure  MonthlyCharges  \n",
       "0         24       58.350000  \n",
       "1         71      113.650000  \n",
       "2         70       25.350000  \n",
       "3         49       19.450000  \n",
       "4         22       84.150000  \n",
       "...      ...             ...  \n",
       "8245       4       73.073982  \n",
       "8246      33       84.582119  \n",
       "8247       5       94.773114  \n",
       "8248       8       93.422205  \n",
       "8249      68      103.727497  \n",
       "\n",
       "[8250 rows x 12 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the 10 best categorical features that having the highest score amang all the categorical varaiables\n",
    "selector = SelectKBest(chi2, k=10)\n",
    "X_catg=X_train_res[columns_cat]\n",
    "selector.fit(X_catg,y_train_res)\n",
    "# Get columns to keep from the selector\n",
    "cols = selector.get_support(indices=True)\n",
    "X_train_res=pd.concat([X_catg.iloc[:,cols], X_train_res[['tenure','MonthlyCharges']]], join = 'outer', axis = 1)\n",
    "X_train_res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "chinese-treasurer",
   "metadata": {},
   "source": [
    "# Model Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "about-tactics",
   "metadata": {},
   "outputs": [],
   "source": [
    "def algorithm_pipeline(X_train_data, y_train_data, \n",
    "                       model, param_grid, cv=StratifiedKFold(5), scoring='f1'):# because there was the problem of class imbalance, i have choosed to use f1-score as metric to evaluate the performance of classifiers\n",
    "    gs = GridSearchCV(\n",
    "        estimator=model,\n",
    "        param_grid=param_grid, \n",
    "        cv=cv, \n",
    "        n_jobs=-1, \n",
    "        scoring=scoring,\n",
    "        verbose=3\n",
    "    )\n",
    "    fitted_model = gs.fit(X_train_data, y_train_data)\n",
    "    \n",
    "    return fitted_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "enabling-matter",
   "metadata": {},
   "source": [
    "## Boosting (Ensemble Modeling)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "smart-paper",
   "metadata": {},
   "source": [
    "### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "sapphire-faith",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid1={\n",
    "    'n_estimators' : [1000,1500,2000],\n",
    "    'subsample': [0.7, 0.8, 0.9],\n",
    "    'colsample_bytree': [0.7, 0.8],\n",
    "    'max_depth' : [10,15,20],\n",
    "    'learning_rate': [0.001,0.01]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "controlled-september",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 108 candidates, totalling 540 fits\n"
     ]
    }
   ],
   "source": [
    "model1 = xgboost.XGBClassifier(random_state=0,objective='binary:logistic',booster='gbtree',eval_metric='logloss',use_label_encoder=False)\n",
    "\n",
    "model1= algorithm_pipeline(X_train_res, y_train_res,model1, param_grid1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "respiratory-detector",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8312373615066742"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# best score\n",
    "model1.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "thirty-gazette",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'colsample_bytree': 0.7,\n",
       " 'learning_rate': 0.001,\n",
       " 'max_depth': 15,\n",
       " 'n_estimators': 2000,\n",
       " 'subsample': 0.9}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# best hyperparameters\n",
    "model1.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "moved-thompson",
   "metadata": {},
   "outputs": [],
   "source": [
    "# select the best model\n",
    "xgbmodel=model1.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "educated-dubai",
   "metadata": {},
   "source": [
    "### LightGBM "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "diverse-search",
   "metadata": {},
   "source": [
    "LightGBM is a another gradient boosting framework that uses tree based learning algorithms. It is designed to be more distributed and efficient than Xgboot:\n",
    "\n",
    "* Faster training speed and higher efficiency.\n",
    "\n",
    "* Lower memory usage.\n",
    "\n",
    "* Better accuracy.\n",
    "\n",
    "* Capable of handling large-scale data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "appointed-intersection",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 324 candidates, totalling 1620 fits\n",
      "best score:0.8232904800631495\n",
      "best hyperparameters:{'colsample_bytree': 0.7, 'learning_rate': 0.001, 'max_depth': 20, 'min_split_gain': 0.3, 'n_estimators': 1000, 'num_leaves': 150, 'subsample': 0.9, 'subsample_freq': 20}\n"
     ]
    }
   ],
   "source": [
    "model2 = lgb.LGBMClassifier(random_state=0,boosting_type='gbdt',objective='binary')\n",
    "param_grid2 = {\n",
    "    'n_estimators': [1000,1500,2000],\n",
    "    'colsample_bytree': [0.7, 0.8],\n",
    "    'max_depth': [15,20,25],\n",
    "    'num_leaves': [50, 100, 150],\n",
    "    'min_split_gain': [0.3, 0.4],\n",
    "    'subsample': [0.7, 0.8, 0.9],\n",
    "    'subsample_freq': [20],\n",
    "    'learning_rate': [0.001]\n",
    "}\n",
    "\n",
    "model2 = algorithm_pipeline(X_train_res, y_train_res, model2, param_grid2)\n",
    "print('best score:{}'.format(model2.best_score_))\n",
    "print('best hyperparameters:{}'.format(model2.best_params_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "harmful-chester",
   "metadata": {},
   "outputs": [],
   "source": [
    "# select the best model\n",
    "lgbmodel=model2.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "higher-letter",
   "metadata": {},
   "source": [
    "## Bagging (Ensemble Modeling)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "marine-barbados",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "contrary-windows",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 36 candidates, totalling 180 fits\n",
      "best score:0.8234061432448291\n",
      "best hyperparameters:{'max_depth': 15, 'max_leaf_nodes': 200, 'n_estimators': 700}\n"
     ]
    }
   ],
   "source": [
    "model3 = RandomForestClassifier()\n",
    "param_grid3 = {\n",
    "    'n_estimators': [700, 1000,1500,2000],\n",
    "    'max_depth': [15,20,25],\n",
    "    'max_leaf_nodes': [50, 100, 200]\n",
    "}\n",
    "\n",
    "model3 = algorithm_pipeline(X_train_res, y_train_res, model3, param_grid3)\n",
    "\n",
    "print('best score:{}'.format(model3.best_score_))\n",
    "print('best hyperparameters:{}'.format(model3.best_params_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "greek-candle",
   "metadata": {},
   "outputs": [],
   "source": [
    "# select the best model\n",
    "rfmodel=model3.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "shared-adelaide",
   "metadata": {},
   "source": [
    "**Observation :** Relatively all models had a f1-score around 82 which can be considered a good score. However, to truly evaluate the generalization performance of thoses classifiers, we need to measure their performance against a data that doesn't contain synthetic data created using SMOTENC "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unknown-bones",
   "metadata": {},
   "source": [
    "# Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "beneficial-twins",
   "metadata": {},
   "outputs": [],
   "source": [
    "# label encoding y_test/X_test\n",
    "for feature in catg:\n",
    "    X_test[feature] = l_encoder_X[feature].transform(X_test[feature])\n",
    "    \n",
    "X_test=X_test[X_train_res.columns]\n",
    "y_test = pd.Series(l_encoder_y.transform(y_test),index=X_test.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "municipal-playing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6062111801242236"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds1 = xgbmodel.predict(X_test)\n",
    "f1_score(y_test, preds1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "deadly-judges",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6120996441281139"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds2 = lgbmodel.predict(X_test)\n",
    "f1_score(y_test, preds2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "applicable-banana",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6067961165048543"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds3 = rfmodel.predict(X_test)\n",
    "f1_score(y_test, preds3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "varied-burning",
   "metadata": {},
   "source": [
    "**Conclusion :** the classifiers perform poorly on test data (f1-scores have dropped by 20%). the class imbalance as well as the train data size lead to poor results. I have tried to solve the problem by using cross-validation (use all the train data, see if there is any overfitting ...) and SMOTE ( oversampling : created new data for the minority class) but that doesn't improve as i want the performance of models. I also tried weight balancing (The idea is to weigh the loss computed for different samples differently based on whether they belong to the majority or the minority classes) but the result still the same.\n",
    "\n",
    "This project is an example of how data imbalance can affect the performance of models and how it is critical to assure that we have balanced data or at least enough data to cope with this problem. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sound-small",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "manufactured-novel",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "meaningful-regression",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "photographic-garbage",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "integral-trouble",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "assumed-agent",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "registered-fraction",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dietary-pursuit",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "industrial-delicious",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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