Telco_Churn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "%matplotlib inline\n",
    "from mlxtend.plotting import plot_decision_regions\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "plt.style.use('ggplot')\n",
    "import missingno as msno\n",
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dataset import\n",
    "data= pd.read_csv('WA_Fn-UseC_-Telco-Customer-Churn.train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Churn</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>...</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>4223-BKEOR</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>21</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>64.85</td>\n",
       "      <td>1336.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>6035-RIIOM</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>54</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>97.20</td>\n",
       "      <td>5129.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>3797-VTIDR</td>\n",
       "      <td>Male</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>...</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>23.45</td>\n",
       "      <td>23.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yes</td>\n",
       "      <td>2568-BRGYX</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>...</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>70.20</td>\n",
       "      <td>237.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>2775-SEFEE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>61.90</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Churn  customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
       "0    No  4223-BKEOR  Female              0      No        Yes      21   \n",
       "1    No  6035-RIIOM  Female              0      No         No      54   \n",
       "2   Yes  3797-VTIDR    Male              0     Yes         No       1   \n",
       "3   Yes  2568-BRGYX    Male              0      No         No       4   \n",
       "4    No  2775-SEFEE    Male              0      No        Yes       0   \n",
       "\n",
       "  PhoneService     MultipleLines InternetService  ... OnlineBackup  \\\n",
       "0          Yes                No             DSL  ...           No   \n",
       "1          Yes               Yes     Fiber optic  ...          Yes   \n",
       "2           No  No phone service             DSL  ...           No   \n",
       "3          Yes                No     Fiber optic  ...           No   \n",
       "4          Yes               Yes             DSL  ...          Yes   \n",
       "\n",
       "  DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
       "0              Yes          No          No             Yes        One year   \n",
       "1               No          No         Yes             Yes        Two year   \n",
       "2               No          No          No              No  Month-to-month   \n",
       "3               No          No          No              No  Month-to-month   \n",
       "4               No         Yes          No              No        Two year   \n",
       "\n",
       "  PaperlessBilling              PaymentMethod MonthlyCharges  TotalCharges  \n",
       "0               No               Mailed check          64.85        1336.8  \n",
       "1              Yes  Bank transfer (automatic)          97.20       5129.45  \n",
       "2              Yes           Electronic check          23.45         23.45  \n",
       "3              Yes           Electronic check          70.20        237.95  \n",
       "4              Yes  Bank transfer (automatic)          61.90                \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5634 entries, 0 to 5633\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Churn             5634 non-null   object \n",
      " 1   customerID        5634 non-null   object \n",
      " 2   gender            5634 non-null   object \n",
      " 3   SeniorCitizen     5634 non-null   int64  \n",
      " 4   Partner           5634 non-null   object \n",
      " 5   Dependents        5634 non-null   object \n",
      " 6   tenure            5634 non-null   int64  \n",
      " 7   PhoneService      5634 non-null   object \n",
      " 8   MultipleLines     5634 non-null   object \n",
      " 9   InternetService   5634 non-null   object \n",
      " 10  OnlineSecurity    5634 non-null   object \n",
      " 11  OnlineBackup      5634 non-null   object \n",
      " 12  DeviceProtection  5634 non-null   object \n",
      " 13  TechSupport       5634 non-null   object \n",
      " 14  StreamingTV       5634 non-null   object \n",
      " 15  StreamingMovies   5634 non-null   object \n",
      " 16  Contract          5634 non-null   object \n",
      " 17  PaperlessBilling  5634 non-null   object \n",
      " 18  PaymentMethod     5634 non-null   object \n",
      " 19  MonthlyCharges    5634 non-null   float64\n",
      " 20  TotalCharges      5634 non-null   object \n",
      "dtypes: float64(1), int64(2), object(18)\n",
      "memory usage: 924.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info(verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\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>1</td>\n",
       "      <td>24</td>\n",
       "      <td>3149</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5634 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Churn  customerID  gender  SeniorCitizen  Partner  Dependents  tenure  \\\n",
       "0         0        2370       0              0        0           1      21   \n",
       "1         0        3457       0              0        0           0      54   \n",
       "2         1        2149       1              0        1           0       1   \n",
       "3         1        1426       1              0        0           0       4   \n",
       "4         0        1545       1              0        0           1       0   \n",
       "...     ...         ...     ...            ...      ...         ...     ...   \n",
       "5629      1         375       1              0        1           0       1   \n",
       "5630      0        3418       0              0        1           1      23   \n",
       "5631      0        4591       1              0        1           1      12   \n",
       "5632      1        4247       1              1        0           0      12   \n",
       "5633      0        2700       1              0        0           0      26   \n",
       "\n",
       "      PhoneService  MultipleLines  InternetService  ...  OnlineBackup  \\\n",
       "0                1              0                0  ...             0   \n",
       "1                1              2                1  ...             2   \n",
       "2                0              1                0  ...             0   \n",
       "3                1              0                1  ...             0   \n",
       "4                1              2                0  ...             2   \n",
       "...            ...            ...              ...  ...           ...   \n",
       "5629             1              0                1  ...             0   \n",
       "5630             1              2                0  ...             2   \n",
       "5631             1              0                2  ...             1   \n",
       "5632             1              2                1  ...             0   \n",
       "5633             1              0                2  ...             1   \n",
       "\n",
       "      DeviceProtection  TechSupport  StreamingTV  StreamingMovies  Contract  \\\n",
       "0                    2            0            0                2         1   \n",
       "1                    0            0            2                2         2   \n",
       "2                    0            0            0                0         0   \n",
       "3                    0            0            0                0         0   \n",
       "4                    0            2            0                0         2   \n",
       "...                ...          ...          ...              ...       ...   \n",
       "5629                 0            0            2                2         0   \n",
       "5630                 2            2            2                2         2   \n",
       "5631                 1            1            1                1         0   \n",
       "5632                 2            0            2                2         0   \n",
       "5633                 1            1            1                1         1   \n",
       "\n",
       "      PaperlessBilling  PaymentMethod  MonthlyCharges  TotalCharges  \n",
       "0                    0              3             586           492  \n",
       "1                    1              0            1170          3511  \n",
       "2                    1              2              59          1566  \n",
       "3                    1              2             674          1627  \n",
       "4                    1              0             546             0  \n",
       "...                ...            ...             ...           ...  \n",
       "5629                 1              2            1129          5231  \n",
       "5630                 1              1            1060          1466  \n",
       "5631                 1              2              51          2167  \n",
       "5632                 1              2            1208           305  \n",
       "5633                 0              1              24          3149  \n",
       "\n",
       "[5634 rows x 21 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Label Encoding\n",
    "le = preprocessing.LabelEncoder()\n",
    "data_encoded = data.apply(le.fit_transform)\n",
    "data_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Missing values bar chart\n",
    "p=msno.bar(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    4138\n",
      "1    1496\n",
      "Name: Churn, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Target values distribution\n",
    "print(data_encoded.Churn.value_counts())\n",
    "p=data_encoded.Churn.value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.274533</td>\n",
       "      <td>-1.025166</td>\n",
       "      <td>-0.437749</td>\n",
       "      <td>-0.969579</td>\n",
       "      <td>1.532186</td>\n",
       "      <td>-0.465683</td>\n",
       "      <td>0.329573</td>\n",
       "      <td>-1.000537</td>\n",
       "      <td>-1.182347</td>\n",
       "      <td>1.410879</td>\n",
       "      <td>-1.038357</td>\n",
       "      <td>1.248658</td>\n",
       "      <td>-0.921484</td>\n",
       "      <td>-1.117461</td>\n",
       "      <td>1.134234</td>\n",
       "      <td>0.372908</td>\n",
       "      <td>-1.200007</td>\n",
       "      <td>1.339267</td>\n",
       "      <td>-0.137217</td>\n",
       "      <td>-1.405444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.393816</td>\n",
       "      <td>-1.025166</td>\n",
       "      <td>-0.437749</td>\n",
       "      <td>-0.969579</td>\n",
       "      <td>-0.652662</td>\n",
       "      <td>0.885537</td>\n",
       "      <td>0.329573</td>\n",
       "      <td>1.108339</td>\n",
       "      <td>0.174618</td>\n",
       "      <td>-0.915399</td>\n",
       "      <td>1.230004</td>\n",
       "      <td>-1.023609</td>\n",
       "      <td>-0.921484</td>\n",
       "      <td>1.140708</td>\n",
       "      <td>1.134234</td>\n",
       "      <td>1.577591</td>\n",
       "      <td>0.833328</td>\n",
       "      <td>-1.470949</td>\n",
       "      <td>1.146114</td>\n",
       "      <td>0.564101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.410417</td>\n",
       "      <td>0.975452</td>\n",
       "      <td>-0.437749</td>\n",
       "      <td>1.031376</td>\n",
       "      <td>-0.652662</td>\n",
       "      <td>-1.284605</td>\n",
       "      <td>-3.034225</td>\n",
       "      <td>0.053901</td>\n",
       "      <td>-1.182347</td>\n",
       "      <td>-0.915399</td>\n",
       "      <td>-1.038357</td>\n",
       "      <td>-1.023609</td>\n",
       "      <td>-0.921484</td>\n",
       "      <td>-1.117461</td>\n",
       "      <td>-1.123814</td>\n",
       "      <td>-0.831774</td>\n",
       "      <td>0.833328</td>\n",
       "      <td>0.402528</td>\n",
       "      <td>-1.295292</td>\n",
       "      <td>-0.704784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.854958</td>\n",
       "      <td>0.975452</td>\n",
       "      <td>-0.437749</td>\n",
       "      <td>-0.969579</td>\n",
       "      <td>-0.652662</td>\n",
       "      <td>-1.161766</td>\n",
       "      <td>0.329573</td>\n",
       "      <td>-1.000537</td>\n",
       "      <td>0.174618</td>\n",
       "      <td>-0.915399</td>\n",
       "      <td>-1.038357</td>\n",
       "      <td>-1.023609</td>\n",
       "      <td>-0.921484</td>\n",
       "      <td>-1.117461</td>\n",
       "      <td>-1.123814</td>\n",
       "      <td>-0.831774</td>\n",
       "      <td>0.833328</td>\n",
       "      <td>0.402528</td>\n",
       "      <td>0.056161</td>\n",
       "      <td>-0.664989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.781790</td>\n",
       "      <td>0.975452</td>\n",
       "      <td>-0.437749</td>\n",
       "      <td>-0.969579</td>\n",
       "      <td>1.532186</td>\n",
       "      <td>-1.325551</td>\n",
       "      <td>0.329573</td>\n",
       "      <td>1.108339</td>\n",
       "      <td>-1.182347</td>\n",
       "      <td>1.410879</td>\n",
       "      <td>1.230004</td>\n",
       "      <td>-1.023609</td>\n",
       "      <td>1.400358</td>\n",
       "      <td>-1.117461</td>\n",
       "      <td>-1.123814</td>\n",
       "      <td>1.577591</td>\n",
       "      <td>0.833328</td>\n",
       "      <td>-1.470949</td>\n",
       "      <td>-0.225117</td>\n",
       "      <td>-1.726416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  customerID    gender SeniorCitizen   Partner Dependents    tenure  \\\n",
       "0  -0.274533 -1.025166     -0.437749 -0.969579   1.532186 -0.465683   \n",
       "1   0.393816 -1.025166     -0.437749 -0.969579  -0.652662  0.885537   \n",
       "2  -0.410417  0.975452     -0.437749  1.031376  -0.652662 -1.284605   \n",
       "3  -0.854958  0.975452     -0.437749 -0.969579  -0.652662 -1.161766   \n",
       "4  -0.781790  0.975452     -0.437749 -0.969579   1.532186 -1.325551   \n",
       "\n",
       "  PhoneService MultipleLines InternetService OnlineSecurity OnlineBackup  \\\n",
       "0     0.329573     -1.000537       -1.182347       1.410879    -1.038357   \n",
       "1     0.329573      1.108339        0.174618      -0.915399     1.230004   \n",
       "2    -3.034225      0.053901       -1.182347      -0.915399    -1.038357   \n",
       "3     0.329573     -1.000537        0.174618      -0.915399    -1.038357   \n",
       "4     0.329573      1.108339       -1.182347       1.410879     1.230004   \n",
       "\n",
       "  DeviceProtection TechSupport StreamingTV StreamingMovies  Contract  \\\n",
       "0         1.248658   -0.921484   -1.117461        1.134234  0.372908   \n",
       "1        -1.023609   -0.921484    1.140708        1.134234  1.577591   \n",
       "2        -1.023609   -0.921484   -1.117461       -1.123814 -0.831774   \n",
       "3        -1.023609   -0.921484   -1.117461       -1.123814 -0.831774   \n",
       "4        -1.023609    1.400358   -1.117461       -1.123814  1.577591   \n",
       "\n",
       "  PaperlessBilling PaymentMethod MonthlyCharges TotalCharges  \n",
       "0        -1.200007      1.339267      -0.137217    -1.405444  \n",
       "1         0.833328     -1.470949       1.146114     0.564101  \n",
       "2         0.833328      0.402528      -1.295292    -0.704784  \n",
       "3         0.833328      0.402528       0.056161    -0.664989  \n",
       "4         0.833328     -1.470949      -0.225117    -1.726416  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Scaling (Standardization)\n",
    "sc= StandardScaler()\n",
    "X = pd.DataFrame(sc.fit_transform(data_encoded.drop([\"Churn\"],axis = 1),\n",
    "                               ),\n",
    "                columns=[data_encoded.drop([\"Churn\"], axis =1).columns])\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Target\n",
    "y = data_encoded.Churn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Stratify parameter splits the data so that the proportion of values in the sample produced will be the same as the proportion of values provided to parameter stratify( i.e. if the y variable is a binary categorical variable with values 0 and 1 and there are 25% of zeros and 75% of ones, stratify=y will make sure that your random split has 25% of 0's and 75% of 1's).\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train-Test Split\n",
    "train_X,test_X,y_train,y_test = train_test_split(X,y,test_size=1/5,random_state=2, stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have chosen to oversample the data, given the fact that the \"Yes\" outcomes were considerably less than the \"No\" outcomes.\n",
    "What my model's performance has lost in precision, it has gained in recall and f1-score. I made this choice due to the alarming amount of false negatives in my previous attempt's results ( and I also believe a better precision on positive outcomes is more important because of the context ). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before OverSampling, counts of label '1': 1197\n",
      "Before OverSampling, counts of label '0': 3310 \n",
      "\n",
      "After OverSampling, counts of label '1': 3310\n",
      "After OverSampling, counts of label '0': 3310\n"
     ]
    }
   ],
   "source": [
    "print(\"Before OverSampling, counts of label '1': {}\".format(sum(y_train==1)))\n",
    "print(\"Before OverSampling, counts of label '0': {} \\n\".format(sum(y_train==0)))\n",
    "\n",
    "sm = SMOTE(random_state=2)\n",
    "train_X, y_train = sm.fit_sample(train_X, y_train.ravel())\n",
    "\n",
    "print(\"After OverSampling, counts of label '1': {}\".format(sum(y_train==1)))\n",
    "print(\"After OverSampling, counts of label '0': {}\".format(sum(y_train==0)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Models penalized with the L2 norm have many of their coefficients estimated as zero.\n",
    "The goal is to reduce the dimensionality of the data which will then be passed on to another classifier and they can be used along with feature_selection.SelectFromModel to select the non-zero coefficients.\n",
    "\n",
    "Advantages:\n",
    "\n",
    "Reduces Overfitting: Less redundant data means less opportunity to make decisions based on noise.\n",
    "\n",
    "Improves Accuracy: Less misleading data means the accuracy of the model improves.\n",
    "\n",
    "Reduces Training Time: Less data means that algorithms train faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Feature Selection \n",
    "logistic = LogisticRegression(C = 10, penalty = \"l2\", max_iter = 1000).fit(train_X, y_train)\n",
    "model = SelectFromModel(logistic, prefit = True)\n",
    "\n",
    "selections = model.transform(train_X)\n",
    "X_columns = pd.DataFrame(model.inverse_transform(selections),\n",
    "                         \n",
    " columns = train_X.columns)\n",
    "\n",
    "selected_columns = X_columns.columns[X_columns.var() != 0]\n",
    "\n",
    "X_train = train_X[selected_columns]\n",
    "X_test = test_X[selected_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([(          'tenure',),\n",
       "            (    'PhoneService',),\n",
       "            (  'OnlineSecurity',),\n",
       "            (     'TechSupport',),\n",
       "            (        'Contract',),\n",
       "            ('PaperlessBilling',),\n",
       "            (  'MonthlyCharges',)],\n",
       "           )"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Selected Features\n",
    "selected_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Model Training and # of neighbors selection\n",
    "test_scores = []\n",
    "train_scores = []\n",
    "\n",
    "for i in range(1,50):\n",
    "\n",
    "    knn = KNeighborsClassifier(i)\n",
    "    knn.fit(X_train,y_train)\n",
    "    \n",
    "    train_scores.append(knn.score(X_train,y_train))\n",
    "    test_scores.append(knn.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max train score 99.41087613293051 % and k = [1]\n"
     ]
    }
   ],
   "source": [
    "max_train_score = max(train_scores)\n",
    "train_scores_ind = [i for i, v in enumerate(train_scores) if v == max_train_score]\n",
    "print('Max train score {} % and k = {}'.format(max_train_score*100,list(map(lambda x: x+1, train_scores_ind))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max test score 77.10736468500443 % and k = [16]\n"
     ]
    }
   ],
   "source": [
    "max_test_score = max(test_scores)\n",
    "test_scores_ind = [i for i, v in enumerate(test_scores) if v == max_test_score]\n",
    "print('Max test score {} % and k = {}'.format(max_test_score*100,list(map(lambda x: x+1, test_scores_ind))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "p = sns.lineplot(range(1,50),train_scores,marker='*',label='Train Score')\n",
    "p = sns.lineplot(range(1,50),test_scores,marker='o',label='Test Score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7710736468500443"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Final Model and Accuracy\n",
    "knn= KNeighborsClassifier(16)\n",
    "knn.fit(X_train,y_train)\n",
    "knn.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 12.5, 'Predicted label')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = knn.predict(X_test)\n",
    "\n",
    "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
    "p = sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n",
    "plt.title('Confusion matrix', y=1.1)\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.81      0.84       828\n",
      "           1       0.56      0.68      0.61       299\n",
      "\n",
      "    accuracy                           0.77      1127\n",
      "   macro avg       0.71      0.74      0.72      1127\n",
      "weighted avg       0.79      0.77      0.78      1127\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_pred))"
   ]
  }
 ],
 "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
}