{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "356dea3b",
   "metadata": {},
   "source": [
    "# Telco Customer Churn Analysis using Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f1e1bd8",
   "metadata": {},
   "source": [
    "We will analyse the data and answer the following questions:\n",
    "1. Which categories had the most affect on the customer churn rate?\n",
    "2. Can we generate a predictive model to determine which customer has the potential to churn?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59b6fde8",
   "metadata": {},
   "source": [
    "## Data Dictionary\n",
    "1. **customerID**: Customer ID\n",
    "2. **gender**: Whether the customer is a male or a female\n",
    "3. **SeniorCitizen**: Whether the customer is a senior citizen or not (1, 0)\n",
    "4. **Partner**: Whether the customer has a partner or not (Yes, No)\n",
    "5. **Dependents**: Whether the customer has dependents or not (Yes, No)\n",
    "6. **tenure**: Number of months the customer has stayed with the company\n",
    "7. **PhoneService**: Whether the customer has a phone service or not (Yes, No)\n",
    "8. **MultipleLines**: Whether the customer has multiple lines or not (Yes, No, No phone service)\n",
    "9. **InternetService**: Customer’s internet service provider (DSL, Fiber optic, No)\n",
    "10. **OnlineSecurity**: Whether the customer has online security or not (Yes, No, No internet service)\n",
    "11. **OnlineBackup**: Whether the customer has online backup or not (Yes, No, No internet service)\n",
    "12. **DeviceProtection**: Whether the customer has device protection or not (Yes, No, No internet service)\n",
    "13. **TechSupport**: Whether the customer has tech support or not (Yes, No, No internet service)\n",
    "14. **StreamingTV**: Whether the customer has streaming TV or not (Yes, No, No internet service)\n",
    "15. **StreamingMovies**: Whether the customer has streaming movies or not (Yes, No, No internet service)\n",
    "16. **Contract**: The contract term of the customer (Month-to-month, One year, Two year)\n",
    "17. **PaperlessBilling**: Whether the customer has paperless billing or not (Yes, No)\n",
    "18. **PaymentMethod**: The customer’s payment method (Electronic check, Mailed check, Bank transfer (automatic), Credit card (automatic))\n",
    "19. **MonthlyCharges**: The amount charged to the customer monthly\n",
    "20. **TotalCharges**: The total amount charged to the customer\n",
    "21. **Churn**: Whether the customer churned or not (Yes or No)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "385bebf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing all the relevant libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "349d5e05",
   "metadata": {},
   "source": [
    "We will start by loading the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6970e471",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "76f18b5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</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>...</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>...</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>...</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>...</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>...</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",
       "<p>5 rows × 21 columns</p>\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  ... DeviceProtection  \\\n",
       "0  No phone service             DSL             No  ...               No   \n",
       "1                No             DSL            Yes  ...              Yes   \n",
       "2                No             DSL            Yes  ...               No   \n",
       "3  No phone service             DSL            Yes  ...              Yes   \n",
       "4                No     Fiber optic             No  ...               No   \n",
       "\n",
       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No          No              No  Month-to-month              Yes   \n",
       "1          No          No              No        One year               No   \n",
       "2          No          No              No  Month-to-month              Yes   \n",
       "3         Yes          No              No        One year               No   \n",
       "4          No          No              No  Month-to-month              Yes   \n",
       "\n",
       "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
       "0           Electronic check          29.85         29.85    No  \n",
       "1               Mailed check          56.95        1889.5    No  \n",
       "2               Mailed check          53.85        108.15   Yes  \n",
       "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
       "4           Electronic check          70.70        151.65   Yes  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.chdir(\"/Users/sarabjotsingh/Documents/Github/Projects/Logistic\")\n",
    "\n",
    "data=pd.read_csv(\"WA_Fn-UseC_-Telco-Customer-Churn 2.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cb1ecd0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID           object\n",
       "gender               object\n",
       "SeniorCitizen         int64\n",
       "Partner              object\n",
       "Dependents           object\n",
       "tenure                int64\n",
       "PhoneService         object\n",
       "MultipleLines        object\n",
       "InternetService      object\n",
       "OnlineSecurity       object\n",
       "OnlineBackup         object\n",
       "DeviceProtection     object\n",
       "TechSupport          object\n",
       "StreamingTV          object\n",
       "StreamingMovies      object\n",
       "Contract             object\n",
       "PaperlessBilling     object\n",
       "PaymentMethod        object\n",
       "MonthlyCharges      float64\n",
       "TotalCharges         object\n",
       "Churn                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8ea44ae",
   "metadata": {},
   "source": [
    "TotalCharges should be float rather than object. Let us change its datatype.\n",
    "But before that, let us explore the column more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bea6a072",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"TotalCharges\"].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e01718ee",
   "metadata": {},
   "source": [
    "At this point, we cannoy observe any missing value. Let us explore the frequencies of various charges in the column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bbb6e8c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "          11\n",
       "20.2      11\n",
       "19.75      9\n",
       "20.05      8\n",
       "19.9       8\n",
       "          ..\n",
       "6849.4     1\n",
       "692.35     1\n",
       "130.15     1\n",
       "3211.9     1\n",
       "6844.5     1\n",
       "Name: TotalCharges, Length: 6531, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"TotalCharges\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06a19aad",
   "metadata": {},
   "source": [
    "The first category is blank. It is not coming up as a missing value because its actually a string \" \". \n",
    "Let us replace that with a numpy missing value i.e. np.nan ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f5e97545",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"TotalCharges\"]=data[\"TotalCharges\"].replace(r'^\\s*$',np.nan,regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7fa2a623",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.001561834445548772"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"TotalCharges\"].isnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "52e9c99e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6250f250",
   "metadata": {},
   "source": [
    "Thus the percentage of missing values in TotalCharges which were masked by emply space character is 0.15%\n",
    "Let us now change the data type of the TotalCharges column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ac897b64",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"TotalCharges\"]=data[\"TotalCharges\"].astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e829dc00",
   "metadata": {},
   "source": [
    "Also, SeniorCitizen has int as its type. Let us change it to object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5a38a647",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"SeniorCitizen\"]=data[\"SeniorCitizen\"].astype(object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9fb24e64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7032 entries, 0 to 7042\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        7032 non-null   object \n",
      " 1   gender            7032 non-null   object \n",
      " 2   SeniorCitizen     7032 non-null   object \n",
      " 3   Partner           7032 non-null   object \n",
      " 4   Dependents        7032 non-null   object \n",
      " 5   tenure            7032 non-null   int64  \n",
      " 6   PhoneService      7032 non-null   object \n",
      " 7   MultipleLines     7032 non-null   object \n",
      " 8   InternetService   7032 non-null   object \n",
      " 9   OnlineSecurity    7032 non-null   object \n",
      " 10  OnlineBackup      7032 non-null   object \n",
      " 11  DeviceProtection  7032 non-null   object \n",
      " 12  TechSupport       7032 non-null   object \n",
      " 13  StreamingTV       7032 non-null   object \n",
      " 14  StreamingMovies   7032 non-null   object \n",
      " 15  Contract          7032 non-null   object \n",
      " 16  PaperlessBilling  7032 non-null   object \n",
      " 17  PaymentMethod     7032 non-null   object \n",
      " 18  MonthlyCharges    7032 non-null   float64\n",
      " 19  TotalCharges      7032 non-null   float64\n",
      " 20  Churn             7032 non-null   object \n",
      "dtypes: float64(2), int64(1), object(18)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dd0f216",
   "metadata": {},
   "source": [
    "The data does not have any missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47947801",
   "metadata": {},
   "source": [
    "Let us look for outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7e896562",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "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": [
    "data.boxplot(\"TotalCharges\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dd0c59ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.boxplot(\"MonthlyCharges\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8d70db8",
   "metadata": {},
   "source": [
    "No outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "893c3547",
   "metadata": {},
   "source": [
    "Let us have a look at the distribution of numerical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7e4c7bea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'MonthlyCharges'}>]], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist(\"MonthlyCharges\",bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9314c7bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'TotalCharges'}>]], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Wy8rKlJycrIyMjJO+AXXh9/vl8/n0wKYwlVe6gjLm2bJ1cs1nrhqSqv4MGjRIkZGR9T0d1IAehTb6E9oaS3+qrqCcymkHmOzsbG3dulVr164NWD927Fjn1926dVObNm00cOBAffLJJzr//PNPd3en5Ha75Xa7q62PjIwMeqPLK10qr7ArwDTk3+zfdyZ6juCiR6GN/oS2ht6f2h7baT1GPW7cOC1atEgrV67UOeecc9Lavn37SvrP5SZJ8nq9Ki4uDqipWq66b+ZENR6Pp8azLwAAoHGpU4AxxmjcuHH65z//qRUrVig1NfWUr6m616VNmzaSpLS0NG3ZskX79u1zanw+nzwejzp37uzU5OXlBYzj8/mUlpZWl+kCAIAGqk4BJjs7W3/729+0YMECNWvWTEVFRSoqKtLRo0clSZ988ommTZumgoIC7d69W6+//rpuvPFG9e/fX927d5ckZWRkqHPnzhoxYoTef/99LV26VPfff7+ys7OdS0C33nqrPv30U91zzz3asWOHnnrqKb300ku66667gnz4AADARnUKME8//bRKS0s1YMAAtWnTxvl58cUXJUlRUVFavny5MjIy1LFjR919990aOnSo/vWvfzljhIeHa9GiRQoPD1daWppuuOEG3XjjjZo6dapTk5qaqjfeeEM+n089evTQY489pj/96U8nfIQaAAA0LnW6ifdUT1wnJydr9erVpxwnJSVFixcvPmnNgAED9N5779VlegAAoJHgu5AAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxTpwAzffp0/ehHP1KzZs2UkJCgq666Sjt37gyo+fbbb5Wdna2WLVuqadOmGjp0qIqLiwNq9uzZo6ysLMXGxiohIUHjx4/X8ePHA2pWrVqlCy64QG63W+3atdO8efNO7wgBAECDU6cAs3r1amVnZ2vdunXy+Xzy+/3KyMjQ4cOHnZq77rpL//rXv7Rw4UKtXr1aX375pa6++mpne0VFhbKysnTs2DG98847ev755zVv3jxNmjTJqdm1a5eysrJ06aWXqrCwUDk5Obrlllu0dOnSIBwyAACwXURdipcsWRKwPG/ePCUkJKigoED9+/dXaWmp/vznP2vBggX66U9/KkmaO3euOnXqpHXr1qlfv35atmyZtm/fruXLlysxMVE9e/bUtGnTNGHCBE2ePFlRUVF65plnlJqaqscee0yS1KlTJ61du1azZs1SZmZmjXMrLy9XeXm5s1xWViZJ8vv98vv9dTnME6oaxx1mgjLe2RSs9yCUVR1jYzhWW9Gj0EZ/Qltj6U9tj69OAeb7SktLJUktWrSQJBUUFMjv9ys9Pd2p6dixo9q2bav8/Hz169dP+fn56tatmxITE52azMxM3Xbbbdq2bZt69eql/Pz8gDGqanJyck44l+nTp2vKlCnV1i9btkyxsbH/y2FWM61PZVDHOxsWL15c31M4a3w+X31PAadAj0Ib/QltDb0/R44cqVXdaQeYyspK5eTk6KKLLlLXrl0lSUVFRYqKilJ8fHxAbWJiooqKipya74aXqu1V205WU1ZWpqNHjyomJqbafCZOnKjc3FxnuaysTMnJycrIyJDH4zndwwzg9/vl8/n0wKYwlVe6gjLm2bJ1cs1nrhqSqv4MGjRIkZGR9T0d1IAehTb6E9oaS3+qrqCcymkHmOzsbG3dulVr16493SGCyu12y+12V1sfGRkZ9EaXV7pUXmFXgGnIv9m/70z0HMFFj0Ib/QltDb0/tT2203qMety4cVq0aJFWrlypc845x1nv9Xp17NgxlZSUBNQXFxfL6/U6Nd9/Kqlq+VQ1Ho+nxrMvAACgcalTgDHGaNy4cfrnP/+pFStWKDU1NWB77969FRkZqby8PGfdzp07tWfPHqWlpUmS0tLStGXLFu3bt8+p8fl88ng86ty5s1Pz3TGqaqrGAAAAjVudLiFlZ2drwYIFeu2119SsWTPnnpW4uDjFxMQoLi5Oo0ePVm5urlq0aCGPx6Pbb79daWlp6tevnyQpIyNDnTt31ogRIzRz5kwVFRXp/vvvV3Z2tnMJ6NZbb9Xvf/973XPPPbr55pu1YsUKvfTSS3rjjTeCfPgAAMBGdToD8/TTT6u0tFQDBgxQmzZtnJ8XX3zRqZk1a5Z+9rOfaejQoerfv7+8Xq9eeeUVZ3t4eLgWLVqk8PBwpaWl6YYbbtCNN96oqVOnOjWpqal644035PP51KNHDz322GP605/+dMJHqAEAQONSpzMwxpz680+io6M1Z84czZkz54Q1KSkpp3ysd8CAAXrvvffqMj0AANBI8F1IAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWqXOAWbNmjYYMGaKkpCS5XC69+uqrAdtHjRoll8sV8HPZZZcF1Bw4cEDDhw+Xx+NRfHy8Ro8erUOHDgXUbN68WZdccomio6OVnJysmTNn1v3oAABAg1TnAHP48GH16NFDc+bMOWHNZZddpq+++sr5+fvf/x6wffjw4dq2bZt8Pp8WLVqkNWvWaOzYsc72srIyZWRkKCUlRQUFBXrkkUc0efJkPfvss3WdLgAAaIAi6vqCwYMHa/DgwSetcbvd8nq9NW774IMPtGTJEm3cuFF9+vSRJM2ePVuXX365Hn30USUlJWn+/Pk6duyYnnvuOUVFRalLly4qLCzU448/HhB0vqu8vFzl5eXOcllZmSTJ7/fL7/fX9TBrVDWOO8wEZbyzKVjvQSirOsbGcKy2okehjf6EtsbSn9oeX50DTG2sWrVKCQkJat68uX7605/qN7/5jVq2bClJys/PV3x8vBNeJCk9PV1hYWFav369fv7znys/P1/9+/dXVFSUU5OZmanf/va3+uabb9S8efNq+5w+fbqmTJlSbf2yZcsUGxsb1OOb1qcyqOOdDYsXL67vKZw1Pp+vvqeAU6BHoY3+hLaG3p8jR47Uqi7oAeayyy7T1VdfrdTUVH3yySf69a9/rcGDBys/P1/h4eEqKipSQkJC4CQiItSiRQsVFRVJkoqKipSamhpQk5iY6GyrKcBMnDhRubm5znJZWZmSk5OVkZEhj8cTlGPz+/3y+Xx6YFOYyitdQRnzbNk6ObO+p3DGVfVn0KBBioyMrO/poAb0KLTRn9DWWPpTdQXlVIIeYIYNG+b8ulu3burevbvOP/98rVq1SgMHDgz27hxut1tut7va+sjIyKA3urzSpfIKuwJMQ/7N/n1noucILnoU2uhPaGvo/antsZ3xx6jPO+88tWrVSh9//LEkyev1at++fQE1x48f14EDB5z7Zrxer4qLiwNqqpZPdG8NAABoPM54gPniiy+0f/9+tWnTRpKUlpamkpISFRQUODUrVqxQZWWl+vbt69SsWbMm4EYen8+nDh061Hj5CAAANC51DjCHDh1SYWGhCgsLJUm7du1SYWGh9uzZo0OHDmn8+PFat26ddu/erby8PF155ZVq166dMjP/cw9Gp06ddNlll2nMmDHasGGD3n77bY0bN07Dhg1TUlKSJOn6669XVFSURo8erW3btunFF1/Uk08+GXCPCwAAaLzqHGA2bdqkXr16qVevXpKk3Nxc9erVS5MmTVJ4eLg2b96sK664Qj/84Q81evRo9e7dW2+99VbA/Snz589Xx44dNXDgQF1++eW6+OKLAz7jJS4uTsuWLdOuXbvUu3dv3X333Zo0adIJH6EGAACNS51v4h0wYICMOfHnoCxduvSUY7Ro0UILFiw4aU337t311ltv1XV6AACgEeC7kAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsE5EfU8AZ8e5975R31M4LbtnZNX3FAAAIYgzMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHXqHGDWrFmjIUOGKCkpSS6XS6+++mrAdmOMJk2apDZt2igmJkbp6en66KOPAmoOHDig4cOHy+PxKD4+XqNHj9ahQ4cCajZv3qxLLrlE0dHRSk5O1syZM+t+dAAAoEGqc4A5fPiwevTooTlz5tS4febMmfrd736nZ555RuvXr1eTJk2UmZmpb7/91qkZPny4tm3bJp/Pp0WLFmnNmjUaO3ass72srEwZGRlKSUlRQUGBHnnkEU2ePFnPPvvsaRwiAABoaCLq+oLBgwdr8ODBNW4zxuiJJ57Q/fffryuvvFKS9Je//EWJiYl69dVXNWzYMH3wwQdasmSJNm7cqD59+kiSZs+ercsvv1yPPvqokpKSNH/+fB07dkzPPfecoqKi1KVLFxUWFurxxx8PCDoAAKBxqnOAOZldu3apqKhI6enpzrq4uDj17dtX+fn5GjZsmPLz8xUfH++EF0lKT09XWFiY1q9fr5///OfKz89X//79FRUV5dRkZmbqt7/9rb755hs1b9682r7Ly8tVXl7uLJeVlUmS/H6//H5/UI6vahx3mAnKeDi1uvSuqjZY/Ubw0aPQRn9CW2PpT22PL6gBpqioSJKUmJgYsD4xMdHZVlRUpISEhMBJRESoRYsWATWpqanVxqjaVlOAmT59uqZMmVJt/bJlyxQbG3uaR1SzaX0qgzoeTmzx4sV1fo3P5zsDM0Ew0aPQRn9CW0Pvz5EjR2pVF9QAU58mTpyo3NxcZ7msrEzJycnKyMiQx+MJyj78fr98Pp8e2BSm8kpXUMbEyW2dnFnr2qr+DBo0SJGRkWdwVjhd9Ci00Z/Q1lj6U3UF5VSCGmC8Xq8kqbi4WG3atHHWFxcXq2fPnk7Nvn37Al53/PhxHThwwHm91+tVcXFxQE3VclXN97ndbrnd7mrrIyMjg97o8kqXyisIMGfD6fTuTPQcwUWPQhv9CW0NvT+1Pbagfg5MamqqvF6v8vLynHVlZWVav3690tLSJElpaWkqKSlRQUGBU7NixQpVVlaqb9++Ts2aNWsCroP5fD516NChxstHAACgcalzgDl06JAKCwtVWFgo6T837hYWFmrPnj1yuVzKycnRb37zG73++uvasmWLbrzxRiUlJemqq66SJHXq1EmXXXaZxowZow0bNujtt9/WuHHjNGzYMCUlJUmSrr/+ekVFRWn06NHatm2bXnzxRT355JMBl4gAAEDjVedLSJs2bdKll17qLFeFipEjR2revHm65557dPjwYY0dO1YlJSW6+OKLtWTJEkVHRzuvmT9/vsaNG6eBAwcqLCxMQ4cO1e9+9ztne1xcnJYtW6bs7Gz17t1brVq10qRJk3iEGgAASDqNADNgwAAZc+LHiF0ul6ZOnaqpU6eesKZFixZasGDBSffTvXt3vfXWW3WdHgAAaAT4LiQAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArBNR3xMATubce9+oda073GjmhVLXyUtVXuE6g7M6ud0zsupt3wDQWHAGBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFgn6AFm8uTJcrlcAT8dO3Z0tn/77bfKzs5Wy5Yt1bRpUw0dOlTFxcUBY+zZs0dZWVmKjY1VQkKCxo8fr+PHjwd7qgAAwFIRZ2LQLl26aPny5f/dScR/d3PXXXfpjTfe0MKFCxUXF6dx48bp6quv1ttvvy1JqqioUFZWlrxer9555x199dVXuvHGGxUZGamHH374TEwXAABY5owEmIiICHm93mrrS0tL9ec//1kLFizQT3/6U0nS3Llz1alTJ61bt079+vXTsmXLtH37di1fvlyJiYnq2bOnpk2bpgkTJmjy5MmKioo6E1MGAAAWOSMB5qOPPlJSUpKio6OVlpam6dOnq23btiooKJDf71d6erpT27FjR7Vt21b5+fnq16+f8vPz1a1bNyUmJjo1mZmZuu2227Rt2zb16tWrxn2Wl5ervLzcWS4rK5Mk+f1++f3+oBxX1TjuMBOU8RBcVX2p7/50uG9Rve7/dGydnHlW9lP1ZyhYfyYRXPQntDWW/tT2+IIeYPr27at58+apQ4cO+uqrrzRlyhRdcskl2rp1q4qKihQVFaX4+PiA1yQmJqqoqEiSVFRUFBBeqrZXbTuR6dOna8qUKdXWL1u2TLGxsf/jUQWa1qcyqOMhuOhP3S1evPis7s/n853V/aFu6E9oa+j9OXLkSK3qgh5gBg8e7Py6e/fu6tu3r1JSUvTSSy8pJiYm2LtzTJw4Ubm5uc5yWVmZkpOTlZGRIY/HE5R9+P1++Xw+PbApTOWVrqCMieBxhxlN61NJf07D2TwD4/P5NGjQIEVGRp6VfaL26E9oayz9qbqCcipn5BLSd8XHx+uHP/yhPv74Yw0aNEjHjh1TSUlJwFmY4uJi554Zr9erDRs2BIxR9ZRSTffVVHG73XK73dXWR0ZGBr3R5ZUulVfwD2Sooj91d7b/MjwTfy4RPPQntDX0/tT22M7458AcOnRIn3zyidq0aaPevXsrMjJSeXl5zvadO3dqz549SktLkySlpaVpy5Yt2rdvn1Pj8/nk8XjUuXPnMz1dAABggaCfgfnVr36lIUOGKCUlRV9++aUefPBBhYeH67rrrlNcXJxGjx6t3NxctWjRQh6PR7fffrvS0tLUr18/SVJGRoY6d+6sESNGaObMmSoqKtL999+v7OzsGs+wAACAxifoAeaLL77Qddddp/3796t169a6+OKLtW7dOrVu3VqSNGvWLIWFhWno0KEqLy9XZmamnnrqKef14eHhWrRokW677TalpaWpSZMmGjlypKZOnRrsqQIAAEsFPcC88MILJ90eHR2tOXPmaM6cOSesSUlJOetPRQCN2bn3vnFW9uMON5p5odR18tL/+T6l3TOygjQrADbiu5AAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFjnjH8SLwCcCWfryalg4skpIHg4AwMAAKzDGRgAOEtC/axRTZ/Tw1kjhCrOwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdfgqAQDACYX61x/UhK8/aBw4AwMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1uGrBAAADQpff9A4cAYGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOvwZY4AANSz2nwBpTvcaOaFUtfJS1Ve4ToLszq5+v4CSs7AAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6IR1g5syZo3PPPVfR0dHq27evNmzYUN9TAgAAISBkA8yLL76o3NxcPfjgg3r33XfVo0cPZWZmat++ffU9NQAAUM9CNsA8/vjjGjNmjG666SZ17txZzzzzjGJjY/Xcc8/V99QAAEA9i6jvCdTk2LFjKigo0MSJE511YWFhSk9PV35+fo2vKS8vV3l5ubNcWloqSTpw4ID8fn9Q5uX3+3XkyBFF+MNUUVn/X2WOQBGVRkeOVNKfEEaPQhv9CW2h1p/9+/efkXEPHjwoSTLGnLQuJAPM119/rYqKCiUmJgasT0xM1I4dO2p8zfTp0zVlypRq61NTU8/IHBGarq/vCeCU6FFooz+hLZT60+qxMzv+wYMHFRcXd8LtIRlgTsfEiROVm5vrLFdWVurAgQNq2bKlXK7gJNWysjIlJyfr888/l8fjCcqYCB76E/roUWijP6GtsfTHGKODBw8qKSnppHUhGWBatWql8PBwFRcXB6wvLi6W1+ut8TVut1tutztgXXx8/BmZn8fjadC/eWxHf0IfPQpt9Ce0NYb+nOzMS5WQvIk3KipKvXv3Vl5enrOusrJSeXl5SktLq8eZAQCAUBCSZ2AkKTc3VyNHjlSfPn104YUX6oknntDhw4d100031ffUAABAPQvZAHPttdfq3//+tyZNmqSioiL17NlTS5YsqXZj79nkdrv14IMPVrtUhdBAf0IfPQpt9Ce00Z9ALnOq55QAAABCTEjeAwMAAHAyBBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgKmDOXPm6Nxzz1V0dLT69u2rDRs21PeUGpzp06frRz/6kZo1a6aEhARdddVV2rlzZ0DNt99+q+zsbLVs2VJNmzbV0KFDq31q8549e5SVlaXY2FglJCRo/PjxOn78eEDNqlWrdMEFF8jtdqtdu3aaN2/emT68BmfGjBlyuVzKyclx1tGf+rV3717dcMMNatmypWJiYtStWzdt2rTJ2W6M0aRJk9SmTRvFxMQoPT1dH330UcAYBw4c0PDhw+XxeBQfH6/Ro0fr0KFDATWbN2/WJZdcoujoaCUnJ2vmzJln5fhsVlFRoQceeECpqamKiYnR+eefr2nTpgV8aSH9qQODWnnhhRdMVFSUee6558y2bdvMmDFjTHx8vCkuLq7vqTUomZmZZu7cuWbr1q2msLDQXH755aZt27bm0KFDTs2tt95qkpOTTV5entm0aZPp16+f+fGPf+xsP378uOnatatJT0837733nlm8eLFp1aqVmThxolPz6aefmtjYWJObm2u2b99uZs+ebcLDw82SJUvO6vHabMOGDebcc8813bt3N3feeaeznv7UnwMHDpiUlBQzatQos379evPpp5+apUuXmo8//tipmTFjhomLizOvvvqqef/9980VV1xhUlNTzdGjR52ayy67zPTo0cOsW7fOvPXWW6Zdu3bmuuuuc7aXlpaaxMREM3z4cLN161bz97//3cTExJg//OEPZ/V4bfPQQw+Zli1bmkWLFpldu3aZhQsXmqZNm5onn3zSqaE/tUeAqaULL7zQZGdnO8sVFRUmKSnJTJ8+vR5n1fDt27fPSDKrV682xhhTUlJiIiMjzcKFC52aDz74wEgy+fn5xhhjFi9ebMLCwkxRUZFT8/TTTxuPx2PKy8uNMcbcc889pkuXLgH7uvbaa01mZuaZPqQG4eDBg6Z9+/bG5/OZn/zkJ06AoT/1a8KECebiiy8+4fbKykrj9XrNI4884qwrKSkxbrfb/P3vfzfGGLN9+3YjyWzcuNGpefPNN43L5TJ79+41xhjz1FNPmebNmzv9qtp3hw4dgn1IDUpWVpa5+eabA9ZdffXVZvjw4cYY+lNXXEKqhWPHjqmgoEDp6enOurCwMKWnpys/P78eZ9bwlZaWSpJatGghSSooKJDf7w/oRceOHdW2bVunF/n5+erWrVvApzZnZmaqrKxM27Ztc2q+O0ZVDf2snezsbGVlZVV7D+lP/Xr99dfVp08f/eIXv1BCQoJ69eqlP/7xj872Xbt2qaioKOC9jYuLU9++fQP6Ex8frz59+jg16enpCgsL0/r1652a/v37KyoqyqnJzMzUzp079c0335zpw7TWj3/8Y+Xl5enDDz+UJL3//vtau3atBg8eLIn+1FXIfpVAKPn6669VUVFR7WsMEhMTtWPHjnqaVcNXWVmpnJwcXXTRRerataskqaioSFFRUdW+aTwxMVFFRUVOTU29qtp2spqysjIdPXpUMTExZ+KQGoQXXnhB7777rjZu3FhtG/2pX59++qmefvpp5ebm6te//rU2btyoO+64Q1FRURo5cqTz/tb03n73vU9ISAjYHhERoRYtWgTUpKamVhujalvz5s3PyPHZ7t5771VZWZk6duyo8PBwVVRU6KGHHtLw4cMlif7UEQEGISs7O1tbt27V2rVr63sq+P8+//xz3XnnnfL5fIqOjq7v6eB7Kisr1adPHz388MOSpF69emnr1q165plnNHLkyHqeHV566SXNnz9fCxYsUJcuXVRYWKicnBwlJSXRn9PAJaRaaNWqlcLDw6s9SVFcXCyv11tPs2rYxo0bp0WLFmnlypU655xznPVer1fHjh1TSUlJQP13e+H1emvsVdW2k9V4PB7+d38SBQUF2rdvny644AJFREQoIiJCq1ev1u9+9ztFREQoMTGR/tSjNm3aqHPnzgHrOnXqpD179kj67/t7sr/LvF6v9u3bF7D9+PHjOnDgQJ16iOrGjx+ve++9V8OGDVO3bt00YsQI3XXXXZo+fbok+lNXBJhaiIqKUu/evZWXl+esq6ysVF5entLS0upxZg2PMUbjxo3TP//5T61YsaLaadDevXsrMjIyoBc7d+7Unj17nF6kpaVpy5YtAX/IfT6fPB6P85d7WlpawBhVNfTz5AYOHKgtW7aosLDQ+enTp4+GDx/u/Jr+1J+LLrqo2scOfPjhh0pJSZEkpaamyuv1Bry3ZWVlWr9+fUB/SkpKVFBQ4NSsWLFClZWV6tu3r1OzZs0a+f1+p8bn86lDhw4N5vLEmXDkyBGFhQX+sxseHq7KykpJ9KfO6vsuYlu88MILxu12m3nz5pnt27ebsWPHmvj4+IAnKfC/u+2220xcXJxZtWqV+eqrr5yfI0eOODW33nqradu2rVmxYoXZtGmTSUtLM2lpac72qsd0MzIyTGFhoVmyZIlp3bp1jY/pjh8/3nzwwQdmzpw5PKZ7mr77FJIx9Kc+bdiwwURERJiHHnrIfPTRR2b+/PkmNjbW/O1vf3NqZsyYYeLj481rr71mNm/ebK688soaH9Pt1auXWb9+vVm7dq1p3759wGO6JSUlJjEx0YwYMcJs3brVvPDCCyY2NrbBPaYbbCNHjjQ/+MEPnMeoX3nlFdOqVStzzz33ODX0p/YIMHUwe/Zs07ZtWxMVFWUuvPBCs27duvqeUoMjqcafuXPnOjVHjx41v/zlL03z5s1NbGys+fnPf26++uqrgHF2795tBg8ebGJiYkyrVq3M3Xffbfx+f0DNypUrTc+ePU1UVJQ577zzAvaB2vt+gKE/9etf//qX6dq1q3G73aZjx47m2WefDdheWVlpHnjgAZOYmGjcbrcZOHCg2blzZ0DN/v37zXXXXWeaNm1qPB6Puemmm8zBgwcDat5//31z8cUXG7fbbX7wgx+YGTNmnPFjs11ZWZm58847Tdu2bU10dLQ577zzzH333RfwuDP9qT2XMd/5CEAAAAALcA8MAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKzz/wCNPmn1xhgycgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist(\"TotalCharges\",bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f27119a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'tenure'}>]], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist(\"tenure\",bins=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dda9472",
   "metadata": {},
   "source": [
    "The distributions of the numerical variables are highly skewed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06b8db2a",
   "metadata": {},
   "source": [
    "## Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c24216a7",
   "metadata": {},
   "source": [
    "There are many categorical variables in our data. Let us visualize the effect of these categories on the churn rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "53c81b5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(style='darkgrid')\n",
    "from scipy.stats import chi2_contingency\n",
    "\n",
    "#creating a function to create a stacked barplot using cross tabulations.\n",
    "def count_barplot(category):\n",
    "    CrosstabResult=pd.crosstab(index=data[category],columns=data['Churn'],normalize=\"index\")\n",
    "    %matplotlib inline\n",
    "    CrosstabResult.plot.bar(stacked=True)\n",
    "    print(CrosstabResult)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4312bed8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Churn         No       Yes\n",
      "gender                    \n",
      "Female  0.730405  0.269595\n",
      "Male    0.737954  0.262046\n"
     ]
    },
    {
     "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": [
    "count_barplot(\"gender\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f699ab0",
   "metadata": {},
   "source": [
    "The churn rate doesn't really change from male to female. So gender does not seem like a significant variable for our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dc8a1d5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Churn                No       Yes\n",
      "SeniorCitizen                    \n",
      "0              0.763497  0.236503\n",
      "1              0.583187  0.416813\n"
     ]
    },
    {
     "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": [
    "count_barplot(\"SeniorCitizen\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c28a089",
   "metadata": {},
   "source": [
    "The churn rate is a bit high for senior citizens as compared to that of non senior citizens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "405677b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Churn          No       Yes\n",
      "Partner                    \n",
      "No       0.670239  0.329761\n",
      "Yes      0.802829  0.197171\n"
     ]
    },
    {
     "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": [
    "count_barplot(\"Partner\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dd778a5",
   "metadata": {},
   "source": [
    "The churn rate is a bit high for people not having a partner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5f6da170",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Churn             No       Yes\n",
      "Dependents                    \n",
      "No          0.687209  0.312791\n",
      "Yes         0.844688  0.155312\n"
     ]
    },
    {
     "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": [
    "count_barplot(\"Dependents\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3fee841",
   "metadata": {},
   "source": [
    "The churn rate is high for people with no dependents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "459295b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Churn               No       Yes\n",
      "PhoneService                    \n",
      "No            0.750000  0.250000\n",
      "Yes           0.732525  0.267475\n"
     ]
    },
    {
     "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": [
    "count_barplot(\"PhoneService\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a0821d2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Churn                   No       Yes\n",
      "MultipleLines                       \n",
      "No                0.749188  0.250812\n",
      "No phone service  0.750000  0.250000\n",
      "Yes               0.713515  0.286485\n"
     ]
    },
    {
     "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": [
    "count_barplot(\"MultipleLines\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9572f978",
   "metadata": {},
   "source": [
    "The churn rate seems more or less similar, for customers having or not having multiple lines and no phone service."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bfbc077b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Churn                  No       Yes\n",
      "InternetService                    \n",
      "DSL              0.810017  0.189983\n",
      "Fiber optic      0.581072  0.418928\n",
      "No               0.925658  0.074342\n"
     ]
    },
    {
     "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": [
    "count_barplot(\"InternetService\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "692d70d0",
   "metadata": {},
   "source": [
    "The churn rate is high for customers using Fiber Optic, as compared to those using DSL or not having an internet service."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5cf51c1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Churn                      No       Yes\n",
      "OnlineSecurity                         \n",
      "No                   0.582213  0.417787\n",
      "No internet service  0.925658  0.074342\n",
      "Yes                  0.853598  0.146402\n"
     ]
    },
    {
     "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": [
    "count_barplot(\"OnlineSecurity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca7b8ee6",
   "metadata": {},
   "source": [
    "The churn rate is high for customers with no online security as compared to those opting for online security or even people with no internet service."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a7c97436",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Churn                      No       Yes\n",
      "OnlineBackup                           \n",
      "No                   0.600583  0.399417\n",
      "No internet service  0.925658  0.074342\n",
      "Yes                  0.784330  0.215670\n"
     ]
    },
    {
     "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": [
    "count_barplot(\"OnlineBackup\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de8fee90",
   "metadata": {},
   "source": [
    "The churn rate is high for customers with no online backup as compared to those opting for online backup or even people with no internet service."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d025fe94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Churn                      No       Yes\n",
      "DeviceProtection                       \n",
      "No                   0.608597  0.391403\n",
      "No internet service  0.925658  0.074342\n",
      "Yes                  0.774607  0.225393\n"
     ]
    },
    {
     "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": [
    "count_barplot(\"DeviceProtection\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0789e6ad",
   "metadata": {},
   "source": [
    "The churn rate is high for customers with no device protection as compared to those opting for device protection or even people with no internet service."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "60408fa9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Churn                      No       Yes\n",
      "TechSupport                            \n",
      "No                   0.583525  0.416475\n",
      "No internet service  0.925658  0.074342\n",
      "Yes                  0.848039  0.151961\n"
     ]
    },
    {
     "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": [
    "count_barplot(\"TechSupport\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a38c738a",
   "metadata": {},
   "source": [
    "The churn rate is high for customers with no technical support as compared to those opting for technical support or even people with no internet service."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0a05f7be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Churn                      No       Yes\n",
      "StreamingTV                            \n",
      "No                   0.664649  0.335351\n",
      "No internet service  0.925658  0.074342\n",
      "Yes                  0.698853  0.301147\n"
     ]
    },
    {
     "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": [
    "count_barplot(\"StreamingTV\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "011a6bbc",
   "metadata": {},
   "source": [
    "The churn rate is quite low for customers who did not buy an internet service with us. However, people with no streaming tv service or with, have similar churn rates. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "40e17022",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Churn                      No       Yes\n",
      "StreamingMovies                        \n",
      "No                   0.662711  0.337289\n",
      "No internet service  0.925658  0.074342\n",
      "Yes                  0.700476  0.299524\n"
     ]
    },
    {
     "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": [
    "count_barplot(\"StreamingMovies\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d4826d3",
   "metadata": {},
   "source": [
    "The churn rate is quite low for customers who did not buy an internet service with us. However, people with or without streaming movies service, have similar churn rates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ee8036a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Churn                 No       Yes\n",
      "Contract                          \n",
      "Month-to-month  0.572903  0.427097\n",
      "One year        0.887228  0.112772\n",
      "Two year        0.971513  0.028487\n"
     ]
    },
    {
     "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": [
    "count_barplot(\"Contract\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2e280ab",
   "metadata": {},
   "source": [
    "The churn rates are high for customers with month-to-month contract and decreases as the contract term increases. This was kind of expected as people with higher contract lengths don't have high flexibility of leaving the subscription as compared to those with month-to-month conracts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "288a8638",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Churn                   No       Yes\n",
      "PaperlessBilling                    \n",
      "No                0.836243  0.163757\n",
      "Yes               0.664107  0.335893\n"
     ]
    },
    {
     "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": [
    "count_barplot(\"PaperlessBilling\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51a4db79",
   "metadata": {},
   "source": [
    "Churn rate for customers using paperless billing is higher than that of customers not using paperless billing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b92de665",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Churn                            No       Yes\n",
      "PaymentMethod                                \n",
      "Bank transfer (automatic)  0.832685  0.167315\n",
      "Credit card (automatic)    0.847469  0.152531\n",
      "Electronic check           0.547146  0.452854\n",
      "Mailed check               0.807980  0.192020\n"
     ]
    },
    {
     "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": [
    "count_barplot(\"PaymentMethod\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1918c9b3",
   "metadata": {},
   "source": [
    "Churn rate for customers using electronic check is higher than that of customers using other methods of payment."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cb87de1",
   "metadata": {},
   "source": [
    "Although, the visualizations do give us some insights on the relationship between churn rates and different categories, it is important that we use something more robust for our feature selection.\n",
    "We will use the Chi-Square contingency test to determine, if the relationship between the category and churn rate is statistically significant or not.\n",
    "\n",
    "Null Hypotheses: Ho: There is no significant relationship between the category and churn rate\n",
    "\n",
    "Alternate Hypothesis: H1: There is a significant relationship between the category and churn rate\n",
    "\n",
    "Threshold of p-value-0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e54744d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replacing no internet service and no phone service by no\n",
    "data[\"StreamingMovies\"]=np.where(data[\"StreamingMovies\"]==\"No internet service\",\"No\",data[\"StreamingMovies\"])\n",
    "data[\"StreamingTV\"]=np.where(data[\"StreamingTV\"]==\"No internet service\",\"No\",data[\"StreamingTV\"])\n",
    "data[\"TechSupport\"]=np.where(data[\"TechSupport\"]==\"No internet service\",\"No\",data[\"TechSupport\"])\n",
    "data[\"DeviceProtection\"]=np.where(data[\"DeviceProtection\"]==\"No internet service\",\"No\",data[\"DeviceProtection\"])\n",
    "data[\"OnlineBackup\"]=np.where(data[\"OnlineBackup\"]==\"No internet service\",\"No\",data[\"OnlineBackup\"])\n",
    "data[\"OnlineSecurity\"]=np.where(data[\"OnlineSecurity\"]==\"No internet service\",\"No\",data[\"OnlineSecurity\"])\n",
    "data[\"MultipleLines\"]=np.where(data[\"MultipleLines\"]==\"No phone service\",\"No\",data[\"MultipleLines\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c0cd0468",
   "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>Categorical Variable</th>\n",
       "      <th>Relationship</th>\n",
       "      <th>p_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gender</td>\n",
       "      <td>No Significant relationship</td>\n",
       "      <td>0.490488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SeniorCitizen</td>\n",
       "      <td>Significant relationship</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Partner</td>\n",
       "      <td>Significant relationship</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dependents</td>\n",
       "      <td>Significant relationship</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PhoneService</td>\n",
       "      <td>No Significant relationship</td>\n",
       "      <td>0.349924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MultipleLines</td>\n",
       "      <td>Significant relationship</td>\n",
       "      <td>0.000869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>InternetService</td>\n",
       "      <td>Significant relationship</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>OnlineSecurity</td>\n",
       "      <td>Significant relationship</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>OnlineBackup</td>\n",
       "      <td>Significant relationship</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DeviceProtection</td>\n",
       "      <td>Significant relationship</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>TechSupport</td>\n",
       "      <td>Significant relationship</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>StreamingTV</td>\n",
       "      <td>Significant relationship</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>StreamingMovies</td>\n",
       "      <td>Significant relationship</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Contract</td>\n",
       "      <td>Significant relationship</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>PaperlessBilling</td>\n",
       "      <td>Significant relationship</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>PaymentMethod</td>\n",
       "      <td>Significant relationship</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Categorical Variable                 Relationship   p_value\n",
       "0                gender  No Significant relationship  0.490488\n",
       "1         SeniorCitizen     Significant relationship       0.0\n",
       "2               Partner     Significant relationship       0.0\n",
       "3            Dependents     Significant relationship       0.0\n",
       "4          PhoneService  No Significant relationship  0.349924\n",
       "5         MultipleLines     Significant relationship  0.000869\n",
       "6       InternetService     Significant relationship       0.0\n",
       "7        OnlineSecurity     Significant relationship       0.0\n",
       "8          OnlineBackup     Significant relationship       0.0\n",
       "9      DeviceProtection     Significant relationship       0.0\n",
       "10          TechSupport     Significant relationship       0.0\n",
       "11          StreamingTV     Significant relationship       0.0\n",
       "12      StreamingMovies     Significant relationship       0.0\n",
       "13             Contract     Significant relationship       0.0\n",
       "14     PaperlessBilling     Significant relationship       0.0\n",
       "15        PaymentMethod     Significant relationship       0.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a function to check whethere the given category \n",
    "# has a significant relationship with our target variable: Churn\n",
    "\n",
    "rel_check=[]\n",
    "p_value=[]\n",
    "def rel_chi(category):\n",
    "    CrosstabResult=pd.crosstab(data['Churn'],data[category])\n",
    "    chi_res = chi2_contingency(pd.crosstab(data['Churn'], data[category]))\n",
    "    p_value.append(chi_res[1])\n",
    "    if chi_res[1]<0.05:\n",
    "        rel_check.append(\"Significant relationship\")\n",
    "    else:\n",
    "        rel_check.append(\"No Significant relationship\")\n",
    "    \n",
    "\n",
    "categories=['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines',\n",
    "            'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport','StreamingTV', \n",
    "            'StreamingMovies', 'Contract','PaperlessBilling','PaymentMethod']\n",
    "\n",
    "#determining significance of relationships using rel_check\n",
    "\n",
    "for cat in categories:\n",
    "            rel_chi(cat)\n",
    "check=pd.DataFrame(data=[categories,rel_check,p_value]).T\n",
    "check.columns=[\"Categorical Variable\",\"Relationship\",\"p_value\"]\n",
    "check"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59d56502",
   "metadata": {},
   "source": [
    "Because the relationships between Gender and Churn and Phone Service and Churn are not statistically significant, including GENDER and PHONE SERVICE in our model, would not be beneficial. So let us delete these columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cc70c84e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</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>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</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>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</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>1889.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</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>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</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>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</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 SeniorCitizen Partner Dependents  tenure MultipleLines  \\\n",
       "0  7590-VHVEG             0     Yes         No       1            No   \n",
       "1  5575-GNVDE             0      No         No      34            No   \n",
       "2  3668-QPYBK             0      No         No       2            No   \n",
       "3  7795-CFOCW             0      No         No      45            No   \n",
       "4  9237-HQITU             0      No         No       2            No   \n",
       "\n",
       "  InternetService OnlineSecurity OnlineBackup DeviceProtection TechSupport  \\\n",
       "0             DSL             No          Yes               No          No   \n",
       "1             DSL            Yes           No              Yes          No   \n",
       "2             DSL            Yes          Yes               No          No   \n",
       "3             DSL            Yes           No              Yes         Yes   \n",
       "4     Fiber optic             No           No               No          No   \n",
       "\n",
       "  StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No              No  Month-to-month              Yes   \n",
       "1          No              No        One year               No   \n",
       "2          No              No  Month-to-month              Yes   \n",
       "3          No              No        One year               No   \n",
       "4          No              No  Month-to-month              Yes   \n",
       "\n",
       "               PaymentMethod  MonthlyCharges  TotalCharges Churn  \n",
       "0           Electronic check           29.85         29.85    No  \n",
       "1               Mailed check           56.95       1889.50    No  \n",
       "2               Mailed check           53.85        108.15   Yes  \n",
       "3  Bank transfer (automatic)           42.30       1840.75    No  \n",
       "4           Electronic check           70.70        151.65   Yes  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data.drop([\"gender\",\"PhoneService\"],axis=1)\n",
    "categories.remove(\"gender\")\n",
    "categories.remove(\"PhoneService\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69129a6f",
   "metadata": {},
   "source": [
    "CustomerID should also be removed as it is just an unique identifier of a customer. It doesn't give any information on customer behaviour."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2e88425a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop([\"customerID\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6c5c1aa",
   "metadata": {},
   "source": [
    "The Chi-Square tests helped us determine which categorical variables to remove. With the help of concept of collinearity, we will see which variables are highly correlated and remove the variable which is highly correlated with the most. This will ensure that multicollinearity is minimum in our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3460909c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Hh5fbd/ny5XTt2pUuXbpUeGwJKCGEsDZVGCQRHx9PXFxcueXh4eFMmDDBOJ+ZmYmrq6tx3s3NjdTU1HL76XQ6Nm7cSGJi4i2PLQElhBDWpgotqLCwMAYNGlRu+fWtJwCl1A0OU/44iYmJ9OnThwYNGtzy2BJQQghhbaoQUH/vyrsZd3d39u7da5zPzMzEzc2t3HZff/0148ePr9SxLeT7xEIIIe4YmypMleTj48Pu3bvJycmhsLCQpKQkevXqVWYbpRSHDh2iU6dOla6mEEIIa6LRVH6qJHd3dyZOnEhoaCiPP/44gYGBeHt7M3bsWH755RegdGi5ra0t9vb2lSpTuviEEMLaVNP3dIOCgggKCiqzbMWKFcbHDRo0IDk5udLlSUAJIYS10VpG55kElBBCWBvLuNORBJQQQlgd+cl3IYQQZkluFiuEEMIsWUY+SUAJIYTVkUESQgghzJK0oIQQQpglGSQhhBDCLMkgCSGEEGbJMi5BSUAJIYTVkRaUEEIIs1SFHyw0pVsG1JkzZ+jduzchISFER0cbl6elpfH4448zd+5cBg8eXKWDbtiwgVq1ahEYGMjkyZN5+OGHy5WxZMkSgDK/2Ph3J06cICYmhrNnzwLQqlUrpk6dSv369Su1vyjPYDAwOzqao0cOY2dnx4zoWTRu0sS4fk38+2zd8iUAj/TqxbMvvIBSir5+j9Lkr+28O3TkxX//2xTVv2e0ergVYfNHMdXvVVNXxaL16dcSV9falJQYSNp6hNzcy8Z1TZvVp8e/mqABMjIu8c1XxwEY91x3cv8sBODcOR0/fPe7Kapeve6lFpSzszPff/89JSUlaLVaALZs2UL9+vX/0UF//vlnHn744X+071UZGRmEhoYSHR2Nv78/SimWL19OeHg469atu62yrdn2b77mypUi1ny4ntQD+3kzJob/LF0KwJnTp9ny+ed8sH4DNjY2PDPyKfz79MHBwYE2bduyZNnbJq79vWFw5BAefdqPovzLt95Y3FSLli5otTZ8uPZn7ruvDr5+D5Cw+RAAtnZafB9tzsb1+yksLKbrw57UrGmLvb2WzIw8Pt100MS1r2b3UkDVqlULLy8vfvrpJ7p37w5AcnIyPj4+AHz77bcsWrQIg8GAp6cn0dHRuLi44O/vz8CBA/nhhx8oLCxk/vz56HQ6tm/fTkpKivH363fs2MG6devIzs7m2WefJSQkxHjsjz76iJSUFN58800A4uLisLOzo6CggJ49e+Lv7w+U/rTw2LFj8fDwoLi4GIDU1FSGDx9ORkYGgwcPZsKECeTl5TFlyhQyMjLIzMykS5cuxMTEsGfPHhYsWIDBYKBly5ZMmzaNSZMm8ccff+Dp6Ul6ejpxcXHcd999xu1LSkoYPHgwzzzzDOnp6bzyyisUFBRgY2PDtGnT6Nix4515le6in//3P3x69gRKW0KHDl37Q3Vv2JBl764wfkjRFxdjZ2/Hr78eIjMjgzHPhOFgb0/k5Fdp2qyZSep/Lzj/23nmDp7Dv9dIK/R23O9Rl5O/5wBw/vwl3BvWMa5r1MiJCxfy8fV7gLp1a/JL6nkKC/U0buJM7Tp2DBvegWJ9CTu+/Y0/cwpNdQrVx0IGSVS6mv3792fbtm1A6Rt/69atsbW1JTs7m+nTp7N06VISExN56KGHynQFOjs78/HHHzN8+HCWL1+Oj48P/v7+RERE8MgjjwBw5coVPvroI5YvX05sbGyZ4w4YMIDdu3eTn5+PUorExESCg4NJS0vD29u7zLZarZbAwEBq1CjN3ezsbFavXs0nn3zCypUrycvLY8eOHbRp04YNGzawbds29u/fz6FDpZ+qTp48SXx8PPPnz2fp0qU0a9aML774ghdeeIEjR44AsHHjRgA2b97Mxx9/zDfffMPevXv5+OOPefTRR9m0aRORkZHs27evSi+EucjPy6NO7Wt/yFobrTHwbW1tqVevHkop3oyJwcurDU2bNsPV1ZUxY8ex8v14xowbz5SoSaaq/j1h96ZdlOiLTV0Ni2dvr6WoqMQ4r5QyNhxqOtri6enMdztPsOnjVDp3uZ969WqSl3eFPSl/8NH6A+xJ+YMBj3mZqPbVrBp+sLA6VHqQhJ+fn7GV9OWXX9K/f3+2bNlCzZo18fb2xsPDA4CQkBDeffdd435XQ6hly5YkJSXdsOzevXuj0Who2bIlf/75Z5l1tWrVwtfXl6SkJDw9PfH09MTd3R2NRoNSqsI6P/LII9jZ2VG/fn3q1avHxYsXCQwMJDU1lffff58TJ06Qm5tLQUEBAM2aNaNOndI35+TkZBYuXAhA+/btad26NQC7d+8mLS2NlJQUAAoKCjhy5Ag9evRgwoQJpKWl4evry8iRIyv71JqVWrVrk5+fb5w3KIMx8AGKioqYMW0qjo61mDp9OgBtH2xHjb9aVQ917kxmZuZfbwaW0Y0g7k1FRSXY2WmN86XvGaWPLxfqSU+/REG+HoAzpy/i6labE79lYzCUbnT2rI5atSv3y68Wx0L+NivdgqpduzZeXl7s27ePlJQUY/eewWAos51SyviJGzD+tG9Fb1ZXu4xuts2QIUP4/PPPSUxMNA6maNeuHQcPlu0nNhgMhIeHc+HCBYAyb6xXA23NmjXExMRQv359Ro4cyQMPPGAMOgcHhzJ1ulEAlpSUEBkZSUJCAgkJCWzYsIEhQ4bQuXNnvvjiC3r27MmWLVt49tlnb3q+5qxTp4f44fvvAEg9sJ+WLVsZ1ymleDH8BVq1bs301183vm7vLFvKB2tWA3Dk8GEaNmwo4SRM7tzZizRrXnqd/L776nAh69oHr4yMPFxcHKlZswYaDdzXyIns7Hx6/KsJnbuUfth2da3FpUtFJql7ddNoNZWeTKlKw8z79+/Pm2++Sbt27Yxv/pcvX+bAgQOcOXMGDw8PNmzYQLdu3SosR6vVUlJSUuE21+vSpQvp6emcPXuWqVOnAqUtteDgYHbu3Imvry9KKZYtW0Z2djYuLi43LSs5OZmQkBCCgoI4duwYhw8fxmAwYGNTNqt9fHxITEzEy8uLI0eOcOzYMTQaDd27d2fjxo34+flx5coVnnzySV5//XV27tyJm5sbzzzzDN26dWPQoEGVPj9z4t+nD7t37SL0yREopYiePYfV779P48aNKTGUsO+nn9BfuULy998DEDFxImP+byxToibx/c6daLU1mDVnronPQgg4dvQCTZrWY8RTHQEN2748TOcuHuTmFvLb8Wy+/+53hgwrvUxw5HAW2RcK2JNymgGBXjR7oD4Gg2LblsMmPYdqYyEfIKsUUH5+fkydOpUXX3zRuMzFxYXo6GjCw8PR6/U0atSI2bNnV1iOj48Pb731lrE7rTL69u1Lbm4udnZ2ALi6urJixQpiYmJYuHAhJSUltG3blqV/jTi7mbCwMGbOnMl///tfatWqRadOnThz5gyNGzcus93zzz/Pq6++SlBQEI0bN8bFxQUHBweGDx/OqVOnGDRoEMXFxQwePJhu3brRuHFjXn75ZTZv3oxWq2XGjBmVPjdzYmNjw2szZ5ZZ1qx5c+Pjn/YfuOF+ce8sr85qWZ3MU5lE9njF1NWweF8nHSszn5Nzxvj4yOEsjhzOKrO+qKiYzZ/c4yP4sJh8QqNudSHHxJRS6PV6Ro0axZQpU3jwwQfvynETEhLw8PCgc+fOnDt3jpEjR/L111+Xa2ndCZdLDLfeSNyWJ2oMNHUVrILv/EhTV+Ge9/Ik39suY9F7eyq97Uv/V/mvBCUmJvL222+j1+t55plneOqpp8qsP3HiBDNmzODixYu4urry1ltvUbdu3ZuWZ/aDDbOysvjXv/5Fhw4d7lo4ATRv3pzZs2fz+OOPEx4eTnR0dLWEkxBC3HU2VZgqKSMjg9jYWNatW2e8Pn/8+HHjeqUUzz33HGPHjuWzzz6jTZs2ZQbU3YjZ3+rIzc2Nn3766a4ft3379mzatOmuH1cIIaqbpgoftnU6HTqdrtxyJycnnJycjPO7du2ie/fuODs7AxAQEMDWrVsJDw8H4NChQzg6OtKrVy8Ann322RuWez2zDyghhBB3VlWuQcXHxxMXF1dueXh4eJlbyWVmZhpvvgCljYvU1FTj/B9//IGLiwtRUVH8+uuvtGrVitdee63CY0tACSGEtalCQoWFhd1wVPL1rSfghl/Luf7rJsXFxezZs4cPPviA9u3bs2jRIubNm8e8efNuemwJKCGEsDKaKvyi7t+78m7G3d2dvXv3GuczMzNxc3Mzzru6utKkSRPat28PQGBgIBERERWWKVf9hRDC2miqMFWSj48Pu3fvJicnh8LCQpKSkozXmwA6depETk4Ohw+Xfrds+/bttxz4Ji0oIYSwMtVxpxd3d3cmTpxIaGgoer2eoUOH4u3tzdixY4mIiKB9+/YsXbqUadOmUVhYSMOGDYmJiamwTAkoIYSwMlXp4quKoKAggoKCyixbsWKF8XGHDh34+OOPK12eBJQQQlgZS7lXpgSUEEJYGwsZfSABJYQQVkZaUEIIIcyTBJQQQghzZCH5JAElhBDWxtQ/RFhZElBCCGFl5BqUEEII8yQBJYQQwhxZSD5JQAkhhNWxkISSgBJCCCtjI4MkhBBCmCVpQQkhhDBHMopPCCGEebKMfJKAEkIIa1NdP7dxp0lACSGElbGMeJKAEkIIq2MjLSghhBDmyELGSEhACSGEtdFYSCefBJQQQlgZS2lBWcgP/wohhLhTNJrKT1WRmJjIgAED6Nu3L2vXri23Pi4uDj8/P4KDgwkODr7hNteTFpQQQliZ6viibkZGBrGxsWzatAk7OzuGDx9Ot27daNGihXGbgwcP8tZbb9GpU6dKlSkBJYQQVsamCgGl0+nQ6XTlljs5OeHk5GSc37VrF927d8fZ2RmAgIAAtm7dSnh4uHGbgwcPsmLFCk6fPk3Xrl2JiorC3t7+5vWsdC2FEELcE6rSxRcfH0/v3r3LTfHx8WXKzMzMxNXV1Tjv5uZGRkaGcT4/P582bdoQFRXF5s2b0el0LFu2rMJ6SgvKDCzqtcLUVbjn+c6PNHUVrMLOqAWmrsI97+VJvrddRlU6+MLCwhg0aFC55de3ngCUUuWPc11LrVatWqxYce29bvTo0UyZMoWJEyfe9NgSUEIIYWWqcg3q7115N+Pu7s7evXuN85mZmbi5uRnnz507x65duxg6dChQGmg1alQcQdLFJ4QQVqY6RvH5+Piwe/ducnJyKCwsJCkpiV69ehnXOzg4sGDBAk6fPo1SirVr19K3b98Ky5QWlBBCWJmqDJKoLHd3dyZOnEhoaCh6vZ6hQ4fi7e3N2LFjiYiIoH379kRHR/Pcc8+h1+t56KGHGDVqVIVlSkAJIYSVqa4v6gYFBREUFFRm2fXXnQICAggICKh0eRJQQghhZeQHC4UQQpgly4gnCSghhLA6FtKAkoASQghrI118QgghzFJ1jOKrDhJQQghhZSwknySghBDC2khACSGEMEs2FjKOTwJKCCGsjLSghBBCmCUbG8tIKAkoIYSwMpYRTxJQQghhdeR7UEIIIcySheSTBJQQQlgbCSghhBBmSWMhV6EkoIQQwsrIKD4hhBBmSbr4hBBCmCUZxSeEEMIsWUY8gY2pKyCEEOLu0mgqP1VFYmIiAwYMoG/fvqxdu/am2+3YsQN/f/9blictKCGEsDLV0cWXkZFBbGwsmzZtws7OjuHDh9OtWzdatGhRZrsLFy4wf/78SpUpLSghhLAyNhpNpafK2rVrF927d8fZ2RlHR0cCAgLYunVrue2mTZtGeHh4pcqUFpQQQliZqjSgdDodOp2u3HInJyecnJyM85mZmbi6uhrn3dzcSE1NLbPP6tWradu2LR06dKjUsSWghBDCylSliy8+Pp64uLhyy8PDw5kwYYJxXilV4XGOHj1KUlIS77//Punp6ZU6tgSUEEJYmaq0oMLCwhg0aFC55de3ngDc3d3Zu3evcT4zMxM3Nzfj/NatW8nKymLIkCHo9XoyMzN58sknWbdu3U2PLQElhBBWpioB9feuvJvx8fFhyZIl5OTkULNmTZKSkpg1a5ZxfUREBBEREQCcOXOG0NDQCsMJqnmQxOuvv05wcDADBgygXbt2BAcHExwczCeffFJu2+3bt7Nq1aoKy9u0aROTJ082zn/66acMGTKE4OBggoKCWL16tXGdv78/Z86cuXMnI4QQ94jqGCTh7u7OxIkTCQ0N5fHHHycwMBBvb2/Gjh3LL7/88o/qWa0tqBkzZgDX0jIhIeGm2x46dKhKZW/YsIH169ezfPly3Nzc0Ol0jB49mpo1azJs2LDbqrdV00DAK4/g1qIBJVdK2DJvJ7lnr10gfXi4N237tkApxe7VP3P0u5PYOtRg4MzeONSxp0RfwudvfEvehQITnoRl6NOvJa6utSkpMZC09Qi5uZeN65o2q0+PfzVBA2RkXOKbr44DMO657uT+WQjAuXM6fvjud1NU/Z7R6uFWhM0fxVS/V01dlbuquu4kERQURFBQUJllK1asKLedh4cH27dvv2V5d72L7/fff2f69Onk5ubi6OjI1KlTcXR0ZP369QA0atSInj17MmXKFC5dukRWVhaPPfYYr7zySply3n77bebPn2/s43RycmL+/Pnk5eUZt1m6dClpaWkUFhYSExNDhw4d2LNnD7GxsVy+fJmLFy8SGRlJ//79mTx5Mrm5uZw6dYrIyEhq1arFG2+8gVarpWPHjvz222+sWbOGU6dOMXPmTHJzc3FwcOC1116jbdu2JCYm8t5776HVavHw8GDBggXY29vfvSf2DmnVqxk17LSsGf8pjR50o/eEHnwyeRsA9rXt6DysHctD1mNbswaj3x/K0e9O0mFgG9KPZJG86n+0H9CK7k915Ov/7DLxmZi3Fi1d0Gpt+HDtz9x3Xx18/R4gYXPphzRbOy2+jzZn4/r9FBYW0/VhT2rWtMXeXktmRh6fbjpo4trfGwZHDuHRp/0oyr98643vMRZyp6O7H1CRkZGMGzeOfv36sX//fl588UW2bdvG8OHDARgyZAgrV64kMDCQQYMGcenSJXx9fRk9erSxjJycHM6fP19uqOIDDzxQZr5FixbMnTuXDz74gJUrV7J48WI++OAD3njjDR544AF2797NnDlz6N+/PwDOzs6888476PV6+vTpw/Lly/Hy8uKNN94wlhkVFcX06dNp27Ytx48f54UXXmDbtm0sWrSIjRs30qBBA2JjYzlx4gRt2rSprqex2nh4N+REymkAzh3KpKHXtWGj+sJidBl52NasgZ2DLcpQOmpn78Zf0Px1d2Qn99pczrty9ytuYe73qMvJ33MAOH/+Eu4N6xjXNWrkxIUL+fj6PUDdujX5JfU8hYV6GjdxpnYdO4YN70CxvoQd3/7GnzmFpjoFi3f+t/PMHTyHf6/5t6mrctfJz23cQH5+Pn/88Qf9+vUDoGPHjtStW5cTJ06U2W7MmDGkpKSwcuVKjh07hl6vp7Dw2h+ijU3ppbMbDWu8Xp8+fYDSoNq2rbQVsGDBAr799lu2bt3KgQMHyM/PN27v7e0NlA6HbNCgAV5eXgAMHTqU2bNnk5+fz8GDB3n11WvdAQUFBfz555/4+fkxYsQIevfuTUBAgEWGE4B9LVuK8q8FjKHEgEarQZWUPte6jDzGfvAEGq0NKWt+Nm6nDIoRiwNxbV6f9RO/uOv1tjT29lqKikqM80opNBpQCmo62uLp6czq+L3or5Qw/MmOnD+nIy/vCntS/uDokQvcf78TAx7zYu11r4Gomt2bduHWxO3WG96DLKUFdVfvJKGUKhcqSilKSkrKLJs3bx5r1qyhUaNGPPfcc9SrV6/Mfs7Oznh6enLwYNmujj179rBw4ULjvFarBcr2tz755JOkpqbSrl07nn322TL7Ozg4GPczGAzl6m8wGLCzsyMhIcE4ffTRRzg7OzNt2jQWL16Ms7MzkZGRFV5vM2dF+XrsHG2N8xqba+HUvIcntV0ceXvYhywbvJaWjzTlvjbXWlgfRnzOB89/xqDZfe96vS1NUVEJdnZa47xGo+Hqf/HLhXrS0y9RkK9Hrzdw5vRFXN1qk5F+iePHsgE4e1ZHrdqW14UszEN1DJKolnrezYPVrl0bT09PkpKSANi/fz8XLlygZcuWaLVaiouLAUhOTmbMmDH079+f8+fPk5GRUS4wxowZw7x588jKygJKu/3mzZtHkyZNbnr83NxcTp48yYsvvoivry/JycnlwhGgefPm6HQ6jhw5ApTeABGgTp06NG3a1Bg+ycnJPPXUUxQXF9OvXz/q1avH+PHjCQ4OJi0t7TafLdM4+0s6D/RoDECjB93I+i3HuO7ypSKKi0oouVI6FeVdwb6OPd2f7siDAS0B0BfqjYEmbu7c2Ys0a14fgPvuq8OFrGst+YyMPFxcHKlZswYaDdzXyIns7Hx6/KsJnbt4AODqWotLl4pMUndh+WxsKj+Z0l2/BrVgwQJmzpzJkiVLsLW1ZcmSJdjZ2dG1a1eioqJwcXFh/PjxTJo0CScnJxo0aEC7du3KDRkfMWIEer2e0aNH//XpUxESElLhCD5nZ2eGDRvGY489Ru3atenYsSOXL1+moKDsiDM7OztiYmKIiorCxsaGZs2aGVtXV+v/3nvvYWtrS2xsLLa2tkRERDBq1CgcHByMAzYs0ZGdv9O0qwcj3wlGo9HwxewddA1pz59ndRz/4RTnu2QR+u7jKAVnUtM5uecMmceyCZzmR4dALzRaDV/M2WHq0zB7x45eoEnTeox4qiOgYduXh+ncxYPc3EJ+O57N99/9zpBhpV3ORw5nkX2hgD0ppxkQ6EWzB+pjMCi2bTls0nMQlstSrkFp1K0u5Fghg8HAwoULCQ8Px9HRkVWrVpGRkVHmO1h30rx/La+WcsU1tsFepq6CVdgZtcDUVbjnfaY+v+0yjqWXv7fezbRseOsv6VYXuZPEDdjY2ODs7MzQoUOxtbXl/vvvZ/bs2aaulhBC3BHyi7oWbty4cYwbN87U1RBCiDvOQvJJAkoIIayNtKCEEEKYJVMPH68sCSghhLAyFpJPElBCCGFtLCSfJKCEEMLqWEgTSgJKCCGsjGXEkwSUEEJYHRsLSSgJKCGEsDbSxSeEEMIcWUY8SUAJIYTVsZAGlASUEEJYH8tIKBP/2ocQQoi7TaOp/FQViYmJDBgwgL59+7J27dpy67/66iuCgoJ47LHHmDx5MleuXLlBKddIQAkhhJWx0VR+qqyMjAxiY2NZt24dCQkJbNiwgePHjxvXFxQUEB0dzapVq/jiiy8oKipi8+bNFdfzn56gEEIIS6WpwlQ5u3btonv37jg7O+Po6EhAQABbt241rnd0dGT79u24uLhQUFBAdnY2Tk4V/9aUXIMSQggrU5WuO51Oh05X/gcOnZycygRMZmYmrq6uxnk3NzdSU1PL7GNra8vOnTuZNGkSbm5u9OzZs8JjSwtKCCGsTFXaT/Hx8fTu3bvcFB8fX6bMG/04+41+1sPX15cff/wRPz8/Zs6cWWE9pQUlhBDWpgotqLCwMAYNGlRu+d+759zd3dm7d69xPjMzEzc3N+N8bm4uBw8eNLaagoKCmDhxYoXHlhaUEEJYGU0V/jk5OeHh4VFu+ntA+fj4sHv3bnJycigsLCQpKYlevXoZ1yuliIyM5Ny5cwB8+eWXPPTQQxXWU1pQQghhZarjXnzu7u5MnDiR0NBQ9Ho9Q4cOxdvbm7FjxxIREUH79u2ZNWsW48ePR6PR0KJFC15//fUKy9SoG3Ucirtq3r+Wm7oK9zzbYC9TV8Eq7IxaYOoq3PM+U5/fdhl/FuorvW29mra3fbx/SlpQQghhZSzjPhISUEIIYXXkXnxCCCHMkoXkkwSUEEJYmxt9P8kcSUAJIYSVsZB8koASQgjrYxkJJQElhBBWRlpQQgghzJKF5JMElBBCWBtpQQkhhDBLljKKT251JIQQwizJ3cyFEEKYJQkoIYQQZkkCSgghhFmSgBJCCGGWJKCEEEKYJQkoIYQQZkkCSgghhFmSgBJCCGGWJKCEEEKYJQkoK3Xp0iWef/55U1fD5M6cOUPr1q2ZPn16meVpaWm0bt2aTZs2VbnMDRs28PnnnwMwefLkG5axZMkSlixZUmE5J06c4NlnnyUoKIigoCBefvllcnJyKr2/JXj99dcJDg5mwIABtGvXjuDgYIKDg/nkk0/Kbbt9+3ZWrVpVYXmbNm1i8uTJxvlPP/2UIUOGEBwcTFBQEKtXrzau8/f358yZM3fuZMQdJ/fis1IXL17k8OHDpq6GWXB2dub777+npKQErVYLwJYtW6hfv/4/Ku/nn3/m4Ycfvq06ZWRkEBoaSnR0NP7+/iilWL58OeHh4axbt+62yjYnM2bMAEo/KISGhpKQkHDTbQ8dOlSlsjds2MD69etZvnw5bm5u6HQ6Ro8eTc2aNRk2bNht1VvcHRJQVuqNN94gMzOTF154gb59+xIfH4/BYODBBx9kxowZ2Nvb07NnTwICAti3bx9arZZFixbh6emJv78/q1evxsPDgx9//JG4uDjWrFnD008/Td26dTl27BiLFi0iKyuLxYsXU1xcjIeHB7NmzaJevXqmPvVyatWqhZeXFz/99BPdu3cHIDk5GR8fHwC+/fZbFi1ahMFgwNPTk+joaFxcXPD392fgwIH88MMPFBYWMn/+fHQ6Hdu3byclJQVXV1cAduzYwbp168jOzubZZ58lJCTEeOyPPvqIlJQU3nzzTQDi4uKws7OjoKCAnj174u/vD5Te3HPs2LF4eHhQXFwMQGpqKsOHDycjI4PBgwczYcIE8vLymDJlChkZGWRmZtKlSxdiYmLYs2cPCxYswGAw0LJlS6ZNm8akSZP4448/8PT0JD09nbi4OO677z7j9iUlJQwePJhnnnmG9PR0XnnlFQoKCrCxsWHatGl07NixWl6P33//nenTp5Obm4ujoyNTp07F0dGR9evXA9CoUSN69uzJlClTuHTpEllZWTz22GO88sorZcp5++23mT9/Pm5ubgA4OTkxf/588vLyjNssXbqUtLQ0CgsLiYmJoUOHDuzZs4fY2FguX77MxYsXiYyMpH///kyePJnc3FxOnTpFZGQktWrV4o033kCr1dKxY0d+++031qxZw6lTp5g5cya5ubk4ODjw2muv0bZtWxITE3nvvffQarV4eHiwYMEC7O3tq+U5vGcoYZVOnz6t/Pz81NGjR9WIESPU5cuXlVJKLVy4UC1dulQppVSrVq3UV199pZRSau7cuWru3LlKKaX8/PzU6dOnlVJKpaSkqJEjRyqllBo5cqRavHixUkqp7OxsNXDgQJWbm6uUUurDDz9UU6ZMuXsnWElXn4fPPvtMzZw5Uyml1IEDB9TkyZNVVFSUevfdd1XPnj2N57tixQo1YcIEpVTp87Bq1SqllFKrV69W4eHhSimloqKi1CeffGJ8PH78eGUwGNSRI0dUt27dlFJKLV68WC1evFjl5eWpHj16qLy8PGUwGFS/fv1Uenq6GjdunFq7du1N67148WI1aNAgVVRUpLKzs1WHDh3UpUuXVGJiolq2bJlSSqmioiLVp08f9csvv6iUlBTVuXNnpdPplFKlr+f8+fOVUkqlpqYqLy8vdfr0abVu3To1Z84c4/4jR45UP/30k1qyZIlasWKFUqr0NX/vvffuzAvwl6uvg1JKDRkyRG3btk0ppdTPP/+sHn30UVVUVGR8zpRS6r333lObNm1SSiml0+lUp06dVHZ2tvrkk09UVFSUys7OVq1atVKFhYU3Paafn5/xPNasWWN8XSdMmKCOHz+ulFJq165dKjAwUClV+lpGRUUppZS6cuWK6tWrl0pLS1NKKTVr1izj30FISIg6dOiQUkqpY8eOqX79+imllPL391cXLlxQSin11ltvqV9//fW2n7d7nbSgrNyPP/7IqVOneOKJJwDQ6/W0bdvWuP6RRx4BoGXLluzdu/eW5Xl7ewNw4MABzp8/T2hoKAAGg4G6deve6erfMX5+fsZW0pdffkn//v3ZsmULNWvWxNvbGw8PDwBCQkJ49913jftd//wkJSXdsOzevXuj0Who2bIlf/75Z5l1tWrVwtfXl6SkJDw9PfH09MTd3R2NRoO6xQ8NPPLII9jZ2VG/fn3q1avHxYsXCQwMJDU1lffff58TJ06Qm5tLQUEBAM2aNaNOnTpAaQtx4cKFALRv357WrVsDsHv3btLS0khJSQGgoKCAI0eO0KNHDyZMmEBaWhq+vr6MHDmySs9vZeXn5/PHH3/Qr18/ADp27EjdunU5ceJEme3GjBlDSkoKK1eu5NixY+j1egoLC43rbWxKL6/f6jns06cPAC1atGDbtm0ALFiwgG+//ZatW7dy4MAB8vPzjdtf/f999OhRGjRogJeXFwBDhw5l9uzZ5Ofnc/DgQV599VXjPgUFBfz555/4+fkxYsQIevfuTUBAAG3atPlHz5E1kYCyciUlJfTv359p06YBpW8QJSUlxvVXuyD+/oZ59fHV7qarHBwcjOU+9NBDvPPOOwAUFRWV+UM3N7Vr18bLy4t9+/aRkpLCyy+/zJYtWzAYDGW2U0qVOefrn5+buXpd62bbDBkyhLfffhsPDw8GDx4MQLt27Th48GCZ7QwGAxEREcycOROAGjWu/flefX3WrFnDtm3beOKJJ/Dx8eHo0aPG1+rqa3O1Tjd68y4pKSEyMtIYEDk5OTg6OuLg4MAXX3zBjh072LJlC5s3b77lgIV/QilVrl5KqTL/JwHmzZvH6dOnCQwMpE+fPuzatavMfs7Oznh6enLw4EG6du1qXL5nzx6+++47Y3fgjV6bJ598km7dutGtWzd69OhRpuvw6nOo1WrL/d+A0tfIzs6uzLW09PR0nJ2dmTZtGocPH2bnzp1ERkYSHh5OcHBwlZ8jayKj+KxUjRo1KC4uplu3bnz11VdkZ2ejlGLmzJnEx8dXuG+9evU4fvw4AN98880Nt+nQoQP79+/n999/B2DZsmXExMTc2ZO4w/r378+bb75Ju3btjG/+ly9f5sCBA8bRXhs2bKBbt24VlqPVasu9oVakS5cupKen8+OPPxo/0YeEhLBz50527twJlL5JL1u2jOzsbFxcXG5aVnJyMiEhIQwcOBCNRsPhw4dv+Ebq4+NDYmIiAEeOHOHYsWNoNBq6d+/Oxo0b0ev15Ofn8+STT3LgwAFiYmJISEhg0KBBTJ8+nV9//bXS51cVtWvXxtPT09ga3b9/PxcuXKBly5ZotVrjh4Pk5GTGjBlD//79OX/+PBkZGeXOc8yYMcybN4+srCygNGznzZtHkyZNbnr83NxcTp48yYsvvoivry/Jyck3fC2bN2+OTqfjyJEjAMbnsk6dOjRt2tQYUMnJyTz11FMUFxfTr18/6tWrx/jx4wkODiYtLe02n617n7SgrFSDBg1o1KgRs2fPJjw8nLCwMAwGA23atGHcuHEV7hsREcGsWbOIi4ujZ8+eN9zG1dWVOXPm8NJLL2EwGHB3d2fBggXVcSp3jJ+fH1OnTuXFF180LnNxcSE6Oprw8HD0er3xOauIj48Pb731lrE7rTL69u1Lbm4udnZ2QOnzt2LFCmJiYli4cCElJSW0bduWpUuXVlhOWFgYM2fO5L///S+1atWiU6dOnDlzhsaNG5fZ7vnnn+fVV18lKCiIxo0b4+LigoODA8OHD+fUqVMMGjSI4uJiBg8eTLdu3WjcuDEvv/wymzdvRqvVGkffVYcFCxYwc+ZMlixZgq2tLUuWLMHOzo6uXbsSFRWFi4sL48ePZ9KkSTg5OdGgQQPatWtXbsj4iBEj0Ov1jB492tjCDAkJqXAEn7OzM8OGDeOxxx6jdu3adOzYkcuXLxu7Sa+ys7MjJiaGqKgobGxsaNasmbF1dbX+7733Hra2tsTGxmJra0tERASjRo3CwcHBOGBDVEx+UVcIE1JKodfrGTVqFFOmTOHBBx+8K8dNSEjAw8ODzp07c+7cOUaOHMnXX39tvHYjKmYwGFi4cCHh4eE4OjqyatUqMjIyynwHS9w+aUEJYUJXh0gPGzbsroUTlHZRzZgxA4PBgI2NDdHR0RJOVWBjY4OzszNDhw7F1taW+++//5Yta1F10oISQghhluQjkxBCCLMkASWEEMIsSUAJIYQwSxJQQgghzJIElBBCCLMkASWEEMIs/X9qo00TwwoukQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"white\")\n",
    "sns.heatmap(data.corr(),cmap=\"BuPu\",annot=True)\n",
    "data.drop([\"TotalCharges\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5a03462",
   "metadata": {},
   "source": [
    "TotalCharges has a really high correlation with Tenure as well as Monthly Charges. So, we will delete this column."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19aaeb44",
   "metadata": {},
   "source": [
    "Now its time for, splitting the data into test and train data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "777c319d",
   "metadata": {},
   "source": [
    "## Splitting the data into train and test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2bd1a93f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "y=data[\"Churn\"]\n",
    "X=data.drop([\"Churn\"],axis=1)\n",
    "x_train,x_test,y_train,y_test=train_test_split(X,y,test_size=0.25,random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9efa06c",
   "metadata": {},
   "source": [
    "25% of the data is now the test data and the rest 75% is now the training data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98b086be",
   "metadata": {},
   "source": [
    "## Encoding of categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4c07a317",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# label encoding (binary variables) in data\n",
    "label_encoding_columns = ['Partner', 'Dependents', 'PaperlessBilling']\n",
    "\n",
    "# ohe (binary variables) in data\n",
    "one_hot_encoding_columns = ['MultipleLines','InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', \n",
    "                            'TechSupport', 'StreamingTV',  'StreamingMovies', 'Contract','PaymentMethod']\n",
    "\n",
    "# encode categorical binary features using label encoding in training data\n",
    "for column in label_encoding_columns:\n",
    "    x_train[column] = x_train[column].map({'Yes': 1, 'No': 0}) \n",
    "\n",
    "y_train=y_train.map({'Yes': 1, 'No': 0})\n",
    "\n",
    "# encode categorical variables with more than two levels using one-hot encoding n training data\n",
    "x_train= pd.get_dummies(x_train, columns = one_hot_encoding_columns)\n",
    "\n",
    "# encode categorical binary features using label encoding in test data\n",
    "for column in label_encoding_columns:\n",
    "    x_test[column] = x_test[column].map({'Yes': 1, 'No': 0}) \n",
    "\n",
    "y_test=y_test.map({'Yes': 1, 'No': 0})\n",
    "\n",
    "# encode categorical variables with more than two levels using one-hot encoding in test data\n",
    "x_test= pd.get_dummies(x_test, columns = one_hot_encoding_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "384d8193",
   "metadata": {},
   "source": [
    "I am not removing the extra dummy variables (which help us avoid dummy trap) because sklearn can handle multiple dummy variables and avoid dummy trap."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87dbe00c",
   "metadata": {},
   "source": [
    "## Normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8933cebe",
   "metadata": {},
   "source": [
    "Because our numerical features, tenure and monthly charges, have different units of measurement, we will normalize them using Min-Max scaling technique.\n",
    "We will calculate min and max of the respective column using training data and transform training and test data using those values. This helps us protect our model from information leakage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ee21200f",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_max_columns = ['tenure', 'MonthlyCharges']\n",
    "\n",
    "# scale numerical variables using min max scaler\n",
    "for column in min_max_columns:\n",
    "        # minimum value of the column\n",
    "        min_column = x_train[column].min()\n",
    "        # maximum value of the column\n",
    "        max_column = x_train[column].max()\n",
    "        # min max scaler\n",
    "        x_train[column] = (x_train[column] - min_column) / (max_column - min_column)\n",
    "        \n",
    "for column in min_max_columns:\n",
    "        x_test[column] = (x_test[column] - min_column) / (max_column - min_column)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b6732e2",
   "metadata": {},
   "source": [
    "## Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fa6ff42",
   "metadata": {},
   "source": [
    "Creating a Logistic Regression Model that can help us predict if a customer will churn or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "66d4bcfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LogisticRegression(solver='liblinear')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression(solver='liblinear')\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13c2e552",
   "metadata": {},
   "source": [
    "Predicted probabilities of Churn on our test data. According to the concept of Logistic Regression, if the probability is >0.5, the customer will churn else not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8be37e4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.32549275, 0.2250375 , 0.22242904, ..., 0.61365948, 0.13201921,\n",
       "       0.0199735 ])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(x_test)[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "355c76b2",
   "metadata": {},
   "source": [
    "Predicted values of Churn on our test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2d55d102",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 ... 1 0 0]\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(x_test)\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1758685",
   "metadata": {},
   "source": [
    "Variables and their coefficients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "71f20102",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                   Variable  Coefficient\n",
      "3                                    tenure    -2.445148\n",
      "5                            MonthlyCharges    -1.430608\n",
      "10                       InternetService_No    -1.179425\n",
      "9               InternetService_Fiber optic     1.109856\n",
      "25                        Contract_Two year    -0.734331\n",
      "23                  Contract_Month-to-month     0.646045\n",
      "4                          PaperlessBilling     0.391355\n",
      "0                             SeniorCitizen     0.289957\n",
      "28           PaymentMethod_Electronic check     0.277548\n",
      "21                       StreamingMovies_No    -0.268153\n",
      "27    PaymentMethod_Credit card (automatic)    -0.257476\n",
      "19                           StreamingTV_No    -0.244995\n",
      "6                          MultipleLines_No    -0.226745\n",
      "2                                Dependents    -0.220407\n",
      "18                          TechSupport_Yes    -0.201836\n",
      "12                       OnlineSecurity_Yes    -0.174241\n",
      "22                      StreamingMovies_Yes     0.156106\n",
      "15                      DeviceProtection_No    -0.152368\n",
      "20                          StreamingTV_Yes     0.132948\n",
      "7                         MultipleLines_Yes     0.114698\n",
      "14                         OnlineBackup_Yes    -0.106037\n",
      "29               PaymentMethod_Mailed check    -0.097024\n",
      "17                           TechSupport_No     0.089789\n",
      "11                        OnlineSecurity_No     0.062194\n",
      "8                       InternetService_DSL    -0.042478\n",
      "16                     DeviceProtection_Yes     0.040321\n",
      "1                                   Partner     0.040256\n",
      "26  PaymentMethod_Bank transfer (automatic)    -0.035096\n",
      "24                        Contract_One year    -0.023761\n",
      "13                          OnlineBackup_No    -0.006010\n"
     ]
    }
   ],
   "source": [
    "model_features = x_train.columns\n",
    "coefficient_df = pd.DataFrame({\"Variable\" : model_features, \"Coefficient\": model.coef_[0]})\n",
    "print(coefficient_df.sort_values(by=\"Coefficient\",ascending=False, key=abs))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "325fc391",
   "metadata": {},
   "source": [
    "### Accuracy of our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3fa86f46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.7718998862343572\n"
     ]
    }
   ],
   "source": [
    "\n",
    "score = model.score(x_test, y_test)\n",
    "print(\"Accuracy score: \",score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d26f470",
   "metadata": {},
   "source": [
    "### Classification Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6b7ad1e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.80      0.84      1311\n",
      "           1       0.54      0.70      0.61       447\n",
      "\n",
      "    accuracy                           0.77      1758\n",
      "   macro avg       0.71      0.75      0.72      1758\n",
      "weighted avg       0.80      0.77      0.78      1758\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "report=classification_report(y_test, predictions)\n",
    "print(report,sep=\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b61cc72",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa2f1703",
   "metadata": {},
   "source": [
    "true positives (TP): These are cases in which we predicted yes (the customer churned), and they actually churned.\n",
    "\n",
    "true negatives (TN): We predicted no, and the customers did not churn.\n",
    "\n",
    "false positives (FP): We predicted yes, but the customers did not actually churn. (Also known as a \"Type I error.\")\n",
    "\n",
    "false negatives (FN): We predicted no, but the customers churned. (Also known as a \"Type II error.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b10433f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm =confusion_matrix(y_test,predictions)\n",
    "group_names = ['True Neg','False Pos','False Neg','True Pos']\n",
    "group_counts = [\"{0:0.0f}\".format(value) for value in\n",
    "                cm.flatten()]\n",
    "group_percentages = [\"{0:.2%}\".format(value) for value in\n",
    "                     cm.flatten()/np.sum(cm)]\n",
    "labels = [f\"{v1}\\n{v2}\\n{v3}\" for v1, v2, v3 in\n",
    "          zip(group_names,group_counts,group_percentages)]\n",
    "labels = np.asarray(labels).reshape(2,2)\n",
    "sns.heatmap(cm, annot=labels, fmt='', cmap='Blues')\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}