churn-prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "912b3e4c",
   "metadata": {},
   "source": [
    "# Predicting Customer Churn for a Telecommunications Company\n",
    "by P.Chamisa"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beacc471",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "Telecommunications companies rely heavily on customer retention to maintain their profitability. Losing customers to competitors can result in significant revenue loss, especially considering the high acquisition costs associated with attracting new customers. Therefore, it is crucial for these companies to identify customers who are at risk of churning and take proactive measures to retain them.\n",
    "\n",
    "In this portfolio project, we will use machine learning to analyze customer data and predict which customers are likely to churn, allowing the company to take preventative measures. Specifically, we will be working with a dataset from a telecommunications company containing information on customer demographics, account information, and service usage. Our goal is to build a model that can accurately predict whether a customer will churn or not.\n",
    "\n",
    "## Data Exploration and Preparation\n",
    "First, we'll load the dataset and perform some initial exploratory data analysis (EDA) to better understand the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c8441467",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "from PIL import Image\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import os\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "14addc4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>State</th>\n",
       "      <th>Account length</th>\n",
       "      <th>Area code</th>\n",
       "      <th>International plan</th>\n",
       "      <th>Voice mail plan</th>\n",
       "      <th>Number vmail messages</th>\n",
       "      <th>Total day minutes</th>\n",
       "      <th>Total day calls</th>\n",
       "      <th>Total day charge</th>\n",
       "      <th>Total eve minutes</th>\n",
       "      <th>Total eve calls</th>\n",
       "      <th>Total eve charge</th>\n",
       "      <th>Total night minutes</th>\n",
       "      <th>Total night calls</th>\n",
       "      <th>Total night charge</th>\n",
       "      <th>Total intl minutes</th>\n",
       "      <th>Total intl calls</th>\n",
       "      <th>Total intl charge</th>\n",
       "      <th>Customer service calls</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KS</td>\n",
       "      <td>128</td>\n",
       "      <td>415</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>25</td>\n",
       "      <td>265.1</td>\n",
       "      <td>110</td>\n",
       "      <td>45.07</td>\n",
       "      <td>197.4</td>\n",
       "      <td>99</td>\n",
       "      <td>16.78</td>\n",
       "      <td>244.7</td>\n",
       "      <td>91</td>\n",
       "      <td>11.01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OH</td>\n",
       "      <td>107</td>\n",
       "      <td>415</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>26</td>\n",
       "      <td>161.6</td>\n",
       "      <td>123</td>\n",
       "      <td>27.47</td>\n",
       "      <td>195.5</td>\n",
       "      <td>103</td>\n",
       "      <td>16.62</td>\n",
       "      <td>254.4</td>\n",
       "      <td>103</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NJ</td>\n",
       "      <td>137</td>\n",
       "      <td>415</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>243.4</td>\n",
       "      <td>114</td>\n",
       "      <td>41.38</td>\n",
       "      <td>121.2</td>\n",
       "      <td>110</td>\n",
       "      <td>10.30</td>\n",
       "      <td>162.6</td>\n",
       "      <td>104</td>\n",
       "      <td>7.32</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>84</td>\n",
       "      <td>408</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>71</td>\n",
       "      <td>50.90</td>\n",
       "      <td>61.9</td>\n",
       "      <td>88</td>\n",
       "      <td>5.26</td>\n",
       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>8.86</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OK</td>\n",
       "      <td>75</td>\n",
       "      <td>415</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>113</td>\n",
       "      <td>28.34</td>\n",
       "      <td>148.3</td>\n",
       "      <td>122</td>\n",
       "      <td>12.61</td>\n",
       "      <td>186.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.41</td>\n",
       "      <td>10.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State  Account length  Area code International plan Voice mail plan  \\\n",
       "0    KS             128        415                 No             Yes   \n",
       "1    OH             107        415                 No             Yes   \n",
       "2    NJ             137        415                 No              No   \n",
       "3    OH              84        408                Yes              No   \n",
       "4    OK              75        415                Yes              No   \n",
       "\n",
       "   Number vmail messages  Total day minutes  Total day calls  \\\n",
       "0                     25              265.1              110   \n",
       "1                     26              161.6              123   \n",
       "2                      0              243.4              114   \n",
       "3                      0              299.4               71   \n",
       "4                      0              166.7              113   \n",
       "\n",
       "   Total day charge  Total eve minutes  Total eve calls  Total eve charge  \\\n",
       "0             45.07              197.4               99             16.78   \n",
       "1             27.47              195.5              103             16.62   \n",
       "2             41.38              121.2              110             10.30   \n",
       "3             50.90               61.9               88              5.26   \n",
       "4             28.34              148.3              122             12.61   \n",
       "\n",
       "   Total night minutes  Total night calls  Total night charge  \\\n",
       "0                244.7                 91               11.01   \n",
       "1                254.4                103               11.45   \n",
       "2                162.6                104                7.32   \n",
       "3                196.9                 89                8.86   \n",
       "4                186.9                121                8.41   \n",
       "\n",
       "   Total intl minutes  Total intl calls  Total intl charge  \\\n",
       "0                10.0                 3               2.70   \n",
       "1                13.7                 3               3.70   \n",
       "2                12.2                 5               3.29   \n",
       "3                 6.6                 7               1.78   \n",
       "4                10.1                 3               2.73   \n",
       "\n",
       "   Customer service calls  Churn  \n",
       "0                       1  False  \n",
       "1                       1  False  \n",
       "2                       0  False  \n",
       "3                       2  False  \n",
       "4                       3  False  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the dataset\n",
    "df = pd.read_csv('churn-bigml-80.csv')\n",
    "df_test = pd.read_csv('churn-bigml-20.csv')\n",
    "\n",
    "# Examine the first few rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "437fc330",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dataoveriew(df, message):\n",
    "    print(f'{message}:\\n')\n",
    "    print(\"Rows:\", df.shape[0])\n",
    "    print(\"\\nNumber of features:\", df.shape[1])\n",
    "    print(\"\\nFeatures:\")\n",
    "    print(df.columns.tolist())\n",
    "    print(\"\\nMissing values:\", df.isnull().sum().values.sum())\n",
    "    print(\"\\nUnique values:\")\n",
    "    print(df.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c7e51cf3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overiew of the training dataset:\n",
      "\n",
      "Rows: 2666\n",
      "\n",
      "Number of features: 20\n",
      "\n",
      "Features:\n",
      "['State', 'Account length', 'Area code', 'International plan', 'Voice mail plan', 'Number vmail messages', 'Total day minutes', 'Total day calls', 'Total day charge', 'Total eve minutes', 'Total eve calls', 'Total eve charge', 'Total night minutes', 'Total night calls', 'Total night charge', 'Total intl minutes', 'Total intl calls', 'Total intl charge', 'Customer service calls', 'Churn']\n",
      "\n",
      "Missing values: 0\n",
      "\n",
      "Unique values:\n",
      "State                       51\n",
      "Account length             205\n",
      "Area code                    3\n",
      "International plan           2\n",
      "Voice mail plan              2\n",
      "Number vmail messages       42\n",
      "Total day minutes         1489\n",
      "Total day calls            115\n",
      "Total day charge          1489\n",
      "Total eve minutes         1442\n",
      "Total eve calls            120\n",
      "Total eve charge          1301\n",
      "Total night minutes       1444\n",
      "Total night calls          118\n",
      "Total night charge         885\n",
      "Total intl minutes         158\n",
      "Total intl calls            21\n",
      "Total intl charge          158\n",
      "Customer service calls      10\n",
      "Churn                        2\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "dataoveriew(df, 'Overiew of the training dataset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8effaca9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overiew of the training dataset:\n",
      "\n",
      "Rows: 667\n",
      "\n",
      "Number of features: 20\n",
      "\n",
      "Features:\n",
      "['State', 'Account length', 'Area code', 'International plan', 'Voice mail plan', 'Number vmail messages', 'Total day minutes', 'Total day calls', 'Total day charge', 'Total eve minutes', 'Total eve calls', 'Total eve charge', 'Total night minutes', 'Total night calls', 'Total night charge', 'Total intl minutes', 'Total intl calls', 'Total intl charge', 'Customer service calls', 'Churn']\n",
      "\n",
      "Missing values: 0\n",
      "\n",
      "Unique values:\n",
      "State                      51\n",
      "Account length            179\n",
      "Area code                   3\n",
      "International plan          2\n",
      "Voice mail plan             2\n",
      "Number vmail messages      37\n",
      "Total day minutes         562\n",
      "Total day calls           100\n",
      "Total day charge          562\n",
      "Total eve minutes         557\n",
      "Total eve calls            94\n",
      "Total eve charge          528\n",
      "Total night minutes       568\n",
      "Total night calls          96\n",
      "Total night charge        453\n",
      "Total intl minutes        132\n",
      "Total intl calls           17\n",
      "Total intl charge         132\n",
      "Customer service calls      9\n",
      "Churn                       2\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "dataoveriew(df_test, 'Overiew of the training dataset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c0a53fff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.854464\n",
       "1    0.145536\n",
       "Name: Churn, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Churn.value_counts(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a365bfea",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2000x3000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# outlier detection using boxplot\n",
    "cols = df.columns.tolist()\n",
    "df.Churn *= 1\n",
    "\n",
    "plt.figure(figsize=(20, 30))\n",
    "\n",
    "for i, variable in enumerate(df.columns.tolist()):\n",
    "    plt.subplot(8, 3, i + 1)\n",
    "    if df.dtypes[variable]=='object':\n",
    "        sns.countplot(data=df, x=variable, hue='Churn')\n",
    "    else:\n",
    "        sns.boxplot(data=df, x=variable, y=df['Churn'].astype('str'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "625889e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ref: https://www.kaggle.com/code/ahsan81/telecom-churn-98-acc-eda-xgb\n",
    "# defining a function for getting percentages\n",
    "def crosstab_function(df, var):\n",
    "    tab = pd.DataFrame(pd.crosstab(df[var], df[\"Churn\"], margins=True)).reset_index()\n",
    "    tab['Percentage'] = tab[1] / tab['All'] * 100\n",
    "    tab.columns = [var, 'Churn_NO', 'Churn_YES', 'Total', 'Churn Percentage']\n",
    "    return tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b3cc61ef",
   "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>International plan</th>\n",
       "      <th>Churn_NO</th>\n",
       "      <th>Churn_YES</th>\n",
       "      <th>Total</th>\n",
       "      <th>Churn Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>2126</td>\n",
       "      <td>270</td>\n",
       "      <td>2396</td>\n",
       "      <td>11.268781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>152</td>\n",
       "      <td>118</td>\n",
       "      <td>270</td>\n",
       "      <td>43.703704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>All</td>\n",
       "      <td>2278</td>\n",
       "      <td>388</td>\n",
       "      <td>2666</td>\n",
       "      <td>14.553638</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  International plan  Churn_NO  Churn_YES  Total  Churn Percentage\n",
       "0                 No      2126        270   2396         11.268781\n",
       "1                Yes       152        118    270         43.703704\n",
       "2                All      2278        388   2666         14.553638"
      ]
     },
     "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": [
    "sns.countplot(data=df, x=df['International plan'], hue='Churn');\n",
    "crosstab_function(df,'International plan')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6406c219",
   "metadata": {},
   "source": [
    "Customers who subscribe to an international plan churn more than those who do not. This is shown by a churn percentage of 43% among those who subscribe to an International plan as compared to  11% of those who do not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "55dcec8d",
   "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>Voice mail plan</th>\n",
       "      <th>Churn_NO</th>\n",
       "      <th>Churn_YES</th>\n",
       "      <th>Total</th>\n",
       "      <th>Churn Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>1610</td>\n",
       "      <td>323</td>\n",
       "      <td>1933</td>\n",
       "      <td>16.709778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>668</td>\n",
       "      <td>65</td>\n",
       "      <td>733</td>\n",
       "      <td>8.867667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>All</td>\n",
       "      <td>2278</td>\n",
       "      <td>388</td>\n",
       "      <td>2666</td>\n",
       "      <td>14.553638</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Voice mail plan  Churn_NO  Churn_YES  Total  Churn Percentage\n",
       "0              No      1610        323   1933         16.709778\n",
       "1             Yes       668         65    733          8.867667\n",
       "2             All      2278        388   2666         14.553638"
      ]
     },
     "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": [
    "sns.countplot(data=df, x=df['Voice mail plan'], hue='Churn');\n",
    "crosstab_function(df,'Voice mail plan')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9554522f",
   "metadata": {},
   "source": [
    "This shows that those with a voice mail plan are more likely to churn than those without a voice mail plan, as shown by a churn percentage of 16% among those without a voice mail plan as compared to 8% of those with a voice mail plan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dc6577af",
   "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>Customer service calls</th>\n",
       "      <th>Churn_NO</th>\n",
       "      <th>Churn_YES</th>\n",
       "      <th>Total</th>\n",
       "      <th>Churn Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>476</td>\n",
       "      <td>79</td>\n",
       "      <td>555</td>\n",
       "      <td>14.234234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>846</td>\n",
       "      <td>99</td>\n",
       "      <td>945</td>\n",
       "      <td>10.476190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>546</td>\n",
       "      <td>62</td>\n",
       "      <td>608</td>\n",
       "      <td>10.197368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>311</td>\n",
       "      <td>37</td>\n",
       "      <td>348</td>\n",
       "      <td>10.632184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>69</td>\n",
       "      <td>64</td>\n",
       "      <td>133</td>\n",
       "      <td>48.120301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>29</td>\n",
       "      <td>49</td>\n",
       "      <td>59.183673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>58.823529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>62.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>All</td>\n",
       "      <td>2278</td>\n",
       "      <td>388</td>\n",
       "      <td>2666</td>\n",
       "      <td>14.553638</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer service calls  Churn_NO  Churn_YES  Total  Churn Percentage\n",
       "0                       0       476         79    555         14.234234\n",
       "1                       1       846         99    945         10.476190\n",
       "2                       2       546         62    608         10.197368\n",
       "3                       3       311         37    348         10.632184\n",
       "4                       4        69         64    133         48.120301\n",
       "5                       5        20         29     49         59.183673\n",
       "6                       6         7         10     17         58.823529\n",
       "7                       7         3          5      8         62.500000\n",
       "8                       8         0          1      1        100.000000\n",
       "9                       9         0          2      2        100.000000\n",
       "10                    All      2278        388   2666         14.553638"
      ]
     },
     "execution_count": 15,
     "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": [
    "sns.countplot(data=df, x=df['Customer service calls'], hue=df['Churn']);\n",
    "crosstab_function(df,'Customer service calls')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a602185",
   "metadata": {},
   "source": [
    "This shows that the churn rate increases as the number of customer service calls increases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "577099ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "sns.histplot(data=df, x='Account_length', hue='Churn', bins=30, kde=True)\n",
    "plt.xlabel('Account_length (days)', fontsize=12)\n",
    "plt.ylabel('Count', fontsize=12)\n",
    "plt.title('Distribution of Account_length by Churn Status', fontsize=14)\n",
    "plt.legend(labels=['Not Churn', 'Churn'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9b33ef5",
   "metadata": {},
   "source": [
    "We can see that customers with an account lenght of 100 have the highest likelihood to churn. This suggests that the company may need to consider offering more incentives or promotions to customers with an average account length of 100 in order to encourage them to stay."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "808367d1",
   "metadata": {},
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a9c5fee",
   "metadata": {},
   "source": [
    "Next, we'll perform some data cleaning and feature engineering to prepare the data for modeling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "16db11a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State                      object\n",
       "Account length              int64\n",
       "Area code                   int64\n",
       "International plan         object\n",
       "Voice mail plan            object\n",
       "Number vmail messages       int64\n",
       "Total day minutes         float64\n",
       "Total day calls             int64\n",
       "Total day charge          float64\n",
       "Total eve minutes         float64\n",
       "Total eve calls             int64\n",
       "Total eve charge          float64\n",
       "Total night minutes       float64\n",
       "Total night calls           int64\n",
       "Total night charge        float64\n",
       "Total intl minutes        float64\n",
       "Total intl calls            int64\n",
       "Total intl charge         float64\n",
       "Customer service calls      int64\n",
       "Churn                       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d359de68",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing all the whitespace with under score(_) in all column names \n",
    "df.columns = df.columns.str.replace(' ', '_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1dd739d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State                      object\n",
       "Account_length              int64\n",
       "Area_code                   int64\n",
       "International_plan         object\n",
       "Voice_mail_plan            object\n",
       "Number_vmail_messages       int64\n",
       "Total_day_minutes         float64\n",
       "Total_day_calls             int64\n",
       "Total_day_charge          float64\n",
       "Total_eve_minutes         float64\n",
       "Total_eve_calls             int64\n",
       "Total_eve_charge          float64\n",
       "Total_night_minutes       float64\n",
       "Total_night_calls           int64\n",
       "Total_night_charge        float64\n",
       "Total_intl_minutes        float64\n",
       "Total_intl_calls            int64\n",
       "Total_intl_charge         float64\n",
       "Customer_service_calls      int64\n",
       "Churn                       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirming changes\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e640fcc8",
   "metadata": {},
   "source": [
    "## Model Building and Evaluation\n",
    "\n",
    "Now that we have a better understanding of the data, we'll build several machine learning models to predict customer churn. We'll start by splitting the data into training and testing sets and using a logistic regression model as our baseline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "504c7b4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "\n",
    "\n",
    "# Define the features and target variables\n",
    "X = df.drop('Churn', axis=1)\n",
    "y = df['Churn']\n",
    "\n",
    "'''\n",
    "Machine learning algorithms typically work with numerical data, so you need to convert these categorical variables \n",
    "into numeric form before fitting the model. We can use the OneHotEncoder class from sklearn.preprocessing to do this.\n",
    "'''\n",
    "# Define the columns that contain categorical variables\n",
    "categorical_cols = ['State', 'International_plan', 'Voice_mail_plan']\n",
    "\n",
    "# Define a transformer that will one-hot encode the categorical columns\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('cat', OneHotEncoder(handle_unknown='ignore'), categorical_cols)\n",
    "    ])\n",
    "\n",
    "# Fit and apply the transformer to the dataset\n",
    "X_encoded = preprocessor.fit_transform(X)\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_encoded, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ce7a5ef",
   "metadata": {},
   "source": [
    "#### Logistic Regression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "580cac15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8445692883895131\n",
      "Precision: 0.43333333333333335\n",
      "Recall: 0.16455696202531644\n",
      "F1 Score: 0.23853211009174313\n"
     ]
    }
   ],
   "source": [
    "# Fit a logistic regression model\n",
    "lr = LogisticRegression(random_state=42)\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# Evaluate the model\n",
    "y_pred = lr.predict(X_test)\n",
    "\n",
    "print('Accuracy:', accuracy_score(y_test, y_pred))\n",
    "print('Precision:', precision_score(y_test, y_pred))\n",
    "print('Recall:', recall_score(y_test, y_pred))\n",
    "print('F1 Score:', f1_score(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a738b35",
   "metadata": {},
   "source": [
    "#### Random Forest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6f28babd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8426966292134831\n",
      "Precision: 0.41379310344827586\n",
      "Recall: 0.1518987341772152\n",
      "F1 Score: 0.22222222222222224\n"
     ]
    }
   ],
   "source": [
    "# Create a Random Forest object and fit the model\n",
    "rf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the testing set\n",
    "y_pred = rf.predict(X_test)\n",
    "\n",
    "# Evaluate the accuracy of the model\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print('Precision:', precision_score(y_test, y_pred))\n",
    "print('Recall:', recall_score(y_test, y_pred))\n",
    "print('F1 Score:', f1_score(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1af51051",
   "metadata": {},
   "source": [
    "#### Support Vector Machines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "14dbf99b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.846441947565543\n",
      "Precision: 0.4482758620689655\n",
      "Recall: 0.16455696202531644\n",
      "F1 Score: 0.24074074074074076\n"
     ]
    }
   ],
   "source": [
    "# Create a SVM object and fit the model\n",
    "svm = SVC(kernel='rbf', gamma='scale', random_state=42)\n",
    "svm.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the testing set\n",
    "y_pred = svm.predict(X_test)\n",
    "\n",
    "# Evaluate the accuracy of the model\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print('Precision:', precision_score(y_test, y_pred))\n",
    "print('Recall:', recall_score(y_test, y_pred))\n",
    "print('F1 Score:', f1_score(y_test, y_pred))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae6a1f08",
   "metadata": {},
   "source": [
    "#### Gradient Boosting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a4c10e45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.846441947565543\n",
      "Precision: 0.4117647058823529\n",
      "Recall: 0.08860759493670886\n",
      "F1 Score: 0.14583333333333334\n"
     ]
    }
   ],
   "source": [
    "# Create a Gradient Boosting object and fit the model\n",
    "gb = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, max_depth=3, random_state=42)\n",
    "gb.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the testing set\n",
    "y_pred = gb.predict(X_test)\n",
    "\n",
    "# Evaluate the accuracy of the model\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print('Precision:', precision_score(y_test, y_pred))\n",
    "print('Recall:', recall_score(y_test, y_pred))\n",
    "print('F1 Score:', f1_score(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "398e58e4",
   "metadata": {},
   "source": [
    "#### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "69fa27ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8333333333333334\n",
      "Precision: 0.3684210526315789\n",
      "Recall: 0.17721518987341772\n",
      "F1 Score: 0.2393162393162393\n"
     ]
    }
   ],
   "source": [
    "# Train an XGBoost model\n",
    "dtrain = xgb.DMatrix(X_train, label=y_train)\n",
    "dtest = xgb.DMatrix(X_test)\n",
    "params = {'max_depth': 3, 'eta': 0.1, 'objective': 'binary:logistic', 'eval_metric': 'auc'}\n",
    "num_rounds = 100\n",
    "model = xgb.train(params, dtrain, num_rounds)\n",
    "\n",
    "# Make predictions\n",
    "y_pred = model.predict(dtest)\n",
    "y_pred = np.round(y_pred)\n",
    "\n",
    "# Evaluate the model\n",
    "print('Accuracy:', accuracy_score(y_test, y_pred))\n",
    "print('Precision:', precision_score(y_test, y_pred))\n",
    "print('Recall:', recall_score(y_test, y_pred))\n",
    "print('F1 Score:', f1_score(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39a2e36d",
   "metadata": {},
   "source": [
    "Since all models were giving an accuracy of around 84%, I tried to tune the hyperparameters for better accuracies in XGBoost, For this I used GridSearchCV from the sklearn library. This function allows you to search over a grid of hyperparameters or randomly selected values of hyperparameters to find the best combination of hyperparameters for your model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3d79b802",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Hyperparameters: {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 200}\n",
      "Accuracy: 0.8333333333333334\n",
      "Precision: 0.3611111111111111\n",
      "Recall: 0.16455696202531644\n",
      "F1 Score: 0.2260869565217391\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Define the hyperparameters to tune\n",
    "param_grid = {\n",
    "    'max_depth': [3, 5, 7],\n",
    "    'learning_rate': [0.01, 0.1, 0.5],\n",
    "    'n_estimators': [50, 100, 200]\n",
    "}\n",
    "\n",
    "# Create an XGBoost classifier object\n",
    "xgb_model = xgb.XGBClassifier()\n",
    "\n",
    "# Use GridSearchCV to search for the best hyperparameters\n",
    "grid_search = GridSearchCV(estimator=xgb_model, param_grid=param_grid, cv=5, scoring='accuracy')\n",
    "\n",
    "# Fit the grid search object to the data\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Print the best hyperparameters found by GridSearchCV\n",
    "print('Best Hyperparameters:', grid_search.best_params_)\n",
    "\n",
    "# Make predictions with the best model\n",
    "best_model = grid_search.best_estimator_\n",
    "y_pred = best_model.predict(X_test)\n",
    "\n",
    "# Evaluate the best model\n",
    "print('Accuracy:', accuracy_score(y_test, y_pred))\n",
    "print('Precision:', precision_score(y_test, y_pred))\n",
    "print('Recall:', recall_score(y_test, y_pred))\n",
    "print('F1 Score:', f1_score(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4cce47b",
   "metadata": {},
   "source": [
    "The best accuracy was achieved by the Support Vector Machine with an accuracy of 0.846 and an F1 score of 0.239 <br/>\n",
    "However, we can still try tuning the hyperparameters of the models to see if we can improve its performance.\n",
    "\n",
    "## Conclusion\n",
    "In this project, we used machine learning to analyze customer data and predict which customers are likely to churn for a telecommunications company. <br/>\n",
    "We explored the data in greater depth and provided additional insights that may be valuable to the company. \n",
    "We also built and evaluated several machine learning models, achieving a decent baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90b8350b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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