EDA India Telecom WIth Python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a3d00fac",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "01e38a8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\Henry Oguns\\Desktop\\DATA ANALYSIS\\Portfolio Projects\\PortfolioProjects\\PortfolioProjects\\Project _Datasets\\file.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0714f6a0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</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>phone number</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>...</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>382-4657</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>...</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>371-7191</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>...</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>358-1921</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>...</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>375-9999</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>...</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>330-6626</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>...</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",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  state  account length  area code phone number international plan  \\\n",
       "0    KS             128        415     382-4657                 no   \n",
       "1    OH             107        415     371-7191                 no   \n",
       "2    NJ             137        415     358-1921                 no   \n",
       "3    OH              84        408     375-9999                yes   \n",
       "4    OK              75        415     330-6626                yes   \n",
       "\n",
       "  voice mail plan  number vmail messages  total day minutes  total day calls  \\\n",
       "0             yes                     25              265.1              110   \n",
       "1             yes                     26              161.6              123   \n",
       "2              no                      0              243.4              114   \n",
       "3              no                      0              299.4               71   \n",
       "4              no                      0              166.7              113   \n",
       "\n",
       "   total day charge  ...  total eve calls  total eve charge  \\\n",
       "0             45.07  ...               99             16.78   \n",
       "1             27.47  ...              103             16.62   \n",
       "2             41.38  ...              110             10.30   \n",
       "3             50.90  ...               88              5.26   \n",
       "4             28.34  ...              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  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the 5 first and last rows\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "185918ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>phone number</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>...</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",
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       "      <th>3328</th>\n",
       "      <td>AZ</td>\n",
       "      <td>192</td>\n",
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       "      <td>yes</td>\n",
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       "      <td>156.2</td>\n",
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       "      <td>...</td>\n",
       "      <td>126</td>\n",
       "      <td>18.32</td>\n",
       "      <td>279.1</td>\n",
       "      <td>83</td>\n",
       "      <td>12.56</td>\n",
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       "      <td>WV</td>\n",
       "      <td>68</td>\n",
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       "      <td>370-3271</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>231.1</td>\n",
       "      <td>57</td>\n",
       "      <td>39.29</td>\n",
       "      <td>...</td>\n",
       "      <td>55</td>\n",
       "      <td>13.04</td>\n",
       "      <td>191.3</td>\n",
       "      <td>123</td>\n",
       "      <td>8.61</td>\n",
       "      <td>9.6</td>\n",
       "      <td>4</td>\n",
       "      <td>2.59</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3330</th>\n",
       "      <td>RI</td>\n",
       "      <td>28</td>\n",
       "      <td>510</td>\n",
       "      <td>328-8230</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>180.8</td>\n",
       "      <td>109</td>\n",
       "      <td>30.74</td>\n",
       "      <td>...</td>\n",
       "      <td>58</td>\n",
       "      <td>24.55</td>\n",
       "      <td>191.9</td>\n",
       "      <td>91</td>\n",
       "      <td>8.64</td>\n",
       "      <td>14.1</td>\n",
       "      <td>6</td>\n",
       "      <td>3.81</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3331</th>\n",
       "      <td>CT</td>\n",
       "      <td>184</td>\n",
       "      <td>510</td>\n",
       "      <td>364-6381</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>213.8</td>\n",
       "      <td>105</td>\n",
       "      <td>36.35</td>\n",
       "      <td>...</td>\n",
       "      <td>84</td>\n",
       "      <td>13.57</td>\n",
       "      <td>139.2</td>\n",
       "      <td>137</td>\n",
       "      <td>6.26</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>3332</th>\n",
       "      <td>TN</td>\n",
       "      <td>74</td>\n",
       "      <td>415</td>\n",
       "      <td>400-4344</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>25</td>\n",
       "      <td>234.4</td>\n",
       "      <td>113</td>\n",
       "      <td>39.85</td>\n",
       "      <td>...</td>\n",
       "      <td>82</td>\n",
       "      <td>22.60</td>\n",
       "      <td>241.4</td>\n",
       "      <td>77</td>\n",
       "      <td>10.86</td>\n",
       "      <td>13.7</td>\n",
       "      <td>4</td>\n",
       "      <td>3.70</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     state  account length  area code phone number international plan  \\\n",
       "3328    AZ             192        415     414-4276                 no   \n",
       "3329    WV              68        415     370-3271                 no   \n",
       "3330    RI              28        510     328-8230                 no   \n",
       "3331    CT             184        510     364-6381                yes   \n",
       "3332    TN              74        415     400-4344                 no   \n",
       "\n",
       "     voice mail plan  number vmail messages  total day minutes  \\\n",
       "3328             yes                     36              156.2   \n",
       "3329              no                      0              231.1   \n",
       "3330              no                      0              180.8   \n",
       "3331              no                      0              213.8   \n",
       "3332             yes                     25              234.4   \n",
       "\n",
       "      total day calls  total day charge  ...  total eve calls  \\\n",
       "3328               77             26.55  ...              126   \n",
       "3329               57             39.29  ...               55   \n",
       "3330              109             30.74  ...               58   \n",
       "3331              105             36.35  ...               84   \n",
       "3332              113             39.85  ...               82   \n",
       "\n",
       "      total eve charge  total night minutes  total night calls  \\\n",
       "3328             18.32                279.1                 83   \n",
       "3329             13.04                191.3                123   \n",
       "3330             24.55                191.9                 91   \n",
       "3331             13.57                139.2                137   \n",
       "3332             22.60                241.4                 77   \n",
       "\n",
       "      total night charge  total intl minutes  total intl calls  \\\n",
       "3328               12.56                 9.9                 6   \n",
       "3329                8.61                 9.6                 4   \n",
       "3330                8.64                14.1                 6   \n",
       "3331                6.26                 5.0                10   \n",
       "3332               10.86                13.7                 4   \n",
       "\n",
       "      total intl charge  customer service calls  churn  \n",
       "3328               2.67                       2  False  \n",
       "3329               2.59                       3  False  \n",
       "3330               3.81                       2  False  \n",
       "3331               1.35                       2  False  \n",
       "3332               3.70                       0  False  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c78e3747",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3333, 21)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Diplay the number of rows and columns of the dataset\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "075a9f15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Rows 3333\n",
      "Number of Columns 21\n"
     ]
    }
   ],
   "source": [
    "print (\"Number of Rows\", df.shape[0])\n",
    "print (\"Number of Columns\", df.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "011399bc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3333 entries, 0 to 3332\n",
      "Data columns (total 21 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   state                   3333 non-null   object \n",
      " 1   account length          3333 non-null   int64  \n",
      " 2   area code               3333 non-null   int64  \n",
      " 3   phone number            3333 non-null   object \n",
      " 4   international plan      3333 non-null   object \n",
      " 5   voice mail plan         3333 non-null   object \n",
      " 6   number vmail messages   3333 non-null   int64  \n",
      " 7   total day minutes       3333 non-null   float64\n",
      " 8   total day calls         3333 non-null   int64  \n",
      " 9   total day charge        3333 non-null   float64\n",
      " 10  total eve minutes       3333 non-null   float64\n",
      " 11  total eve calls         3333 non-null   int64  \n",
      " 12  total eve charge        3333 non-null   float64\n",
      " 13  total night minutes     3333 non-null   float64\n",
      " 14  total night calls       3333 non-null   int64  \n",
      " 15  total night charge      3333 non-null   float64\n",
      " 16  total intl minutes      3333 non-null   float64\n",
      " 17  total intl calls        3333 non-null   int64  \n",
      " 18  total intl charge       3333 non-null   float64\n",
      " 19  customer service calls  3333 non-null   int64  \n",
      " 20  churn                   3333 non-null   bool   \n",
      "dtypes: bool(1), float64(8), int64(8), object(4)\n",
      "memory usage: 524.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# Display information of the dataset\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4b3c9956",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state                       51\n",
       "account length             212\n",
       "area code                    3\n",
       "phone number              3333\n",
       "international plan           2\n",
       "voice mail plan              2\n",
       "number vmail messages       46\n",
       "total day minutes         1667\n",
       "total day calls            119\n",
       "total day charge          1667\n",
       "total eve minutes         1611\n",
       "total eve calls            123\n",
       "total eve charge          1440\n",
       "total night minutes       1591\n",
       "total night calls          120\n",
       "total night charge         933\n",
       "total intl minutes         162\n",
       "total intl calls            21\n",
       "total intl charge          162\n",
       "customer service calls      10\n",
       "churn                        2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the numbers of unique values\n",
    "\n",
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c50b85db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['state', 'account length', 'area code', 'phone number',\n",
       "       'international plan', 'voice mail plan', 'number vmail messages',\n",
       "       'total day minutes', 'total day calls', 'total day charge',\n",
       "       'total eve minutes', 'total eve calls', 'total eve charge',\n",
       "       'total night minutes', 'total night calls', 'total night charge',\n",
       "       'total intl minutes', 'total intl calls', 'total intl charge',\n",
       "       'customer service calls', 'churn'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display all the columns in the dataset\n",
    "\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0cfe01f7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state                      object\n",
       "account length              int64\n",
       "area code                   int64\n",
       "phone number               object\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                        bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4e9f0f93",
   "metadata": {
    "scrolled": true
   },
   "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>account length</th>\n",
       "      <th>area code</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>101.064806</td>\n",
       "      <td>437.182418</td>\n",
       "      <td>8.099010</td>\n",
       "      <td>179.775098</td>\n",
       "      <td>100.435644</td>\n",
       "      <td>30.562307</td>\n",
       "      <td>200.980348</td>\n",
       "      <td>100.114311</td>\n",
       "      <td>17.083540</td>\n",
       "      <td>200.872037</td>\n",
       "      <td>100.107711</td>\n",
       "      <td>9.039325</td>\n",
       "      <td>10.237294</td>\n",
       "      <td>4.479448</td>\n",
       "      <td>2.764581</td>\n",
       "      <td>1.562856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>39.822106</td>\n",
       "      <td>42.371290</td>\n",
       "      <td>13.688365</td>\n",
       "      <td>54.467389</td>\n",
       "      <td>20.069084</td>\n",
       "      <td>9.259435</td>\n",
       "      <td>50.713844</td>\n",
       "      <td>19.922625</td>\n",
       "      <td>4.310668</td>\n",
       "      <td>50.573847</td>\n",
       "      <td>19.568609</td>\n",
       "      <td>2.275873</td>\n",
       "      <td>2.791840</td>\n",
       "      <td>2.461214</td>\n",
       "      <td>0.753773</td>\n",
       "      <td>1.315491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.200000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>74.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>143.700000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>24.430000</td>\n",
       "      <td>166.600000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>14.160000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>7.520000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>101.000000</td>\n",
       "      <td>415.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>179.400000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>201.400000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>17.120000</td>\n",
       "      <td>201.200000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>9.050000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.780000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>127.000000</td>\n",
       "      <td>510.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>216.400000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>36.790000</td>\n",
       "      <td>235.300000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>235.300000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>10.590000</td>\n",
       "      <td>12.100000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.270000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>243.000000</td>\n",
       "      <td>510.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>350.800000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>59.640000</td>\n",
       "      <td>363.700000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>30.910000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>17.770000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       account length    area code  number vmail messages  total day minutes  \\\n",
       "count     3333.000000  3333.000000            3333.000000        3333.000000   \n",
       "mean       101.064806   437.182418               8.099010         179.775098   \n",
       "std         39.822106    42.371290              13.688365          54.467389   \n",
       "min          1.000000   408.000000               0.000000           0.000000   \n",
       "25%         74.000000   408.000000               0.000000         143.700000   \n",
       "50%        101.000000   415.000000               0.000000         179.400000   \n",
       "75%        127.000000   510.000000              20.000000         216.400000   \n",
       "max        243.000000   510.000000              51.000000         350.800000   \n",
       "\n",
       "       total day calls  total day charge  total eve minutes  total eve calls  \\\n",
       "count      3333.000000       3333.000000        3333.000000      3333.000000   \n",
       "mean        100.435644         30.562307         200.980348       100.114311   \n",
       "std          20.069084          9.259435          50.713844        19.922625   \n",
       "min           0.000000          0.000000           0.000000         0.000000   \n",
       "25%          87.000000         24.430000         166.600000        87.000000   \n",
       "50%         101.000000         30.500000         201.400000       100.000000   \n",
       "75%         114.000000         36.790000         235.300000       114.000000   \n",
       "max         165.000000         59.640000         363.700000       170.000000   \n",
       "\n",
       "       total eve charge  total night minutes  total night calls  \\\n",
       "count       3333.000000          3333.000000        3333.000000   \n",
       "mean          17.083540           200.872037         100.107711   \n",
       "std            4.310668            50.573847          19.568609   \n",
       "min            0.000000            23.200000          33.000000   \n",
       "25%           14.160000           167.000000          87.000000   \n",
       "50%           17.120000           201.200000         100.000000   \n",
       "75%           20.000000           235.300000         113.000000   \n",
       "max           30.910000           395.000000         175.000000   \n",
       "\n",
       "       total night charge  total intl minutes  total intl calls  \\\n",
       "count         3333.000000         3333.000000       3333.000000   \n",
       "mean             9.039325           10.237294          4.479448   \n",
       "std              2.275873            2.791840          2.461214   \n",
       "min              1.040000            0.000000          0.000000   \n",
       "25%              7.520000            8.500000          3.000000   \n",
       "50%              9.050000           10.300000          4.000000   \n",
       "75%             10.590000           12.100000          6.000000   \n",
       "max             17.770000           20.000000         20.000000   \n",
       "\n",
       "       total intl charge  customer service calls  \n",
       "count        3333.000000             3333.000000  \n",
       "mean            2.764581                1.562856  \n",
       "std             0.753773                1.315491  \n",
       "min             0.000000                0.000000  \n",
       "25%             2.300000                1.000000  \n",
       "50%             2.780000                1.000000  \n",
       "75%             3.270000                2.000000  \n",
       "max             5.400000                9.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the overall statistics of the dataset\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "962aa2a6",
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>phone number</th>\n",
       "      <th>international plan</th>\n",
       "      <th>voice mail plan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3333</td>\n",
       "      <td>3333</td>\n",
       "      <td>3333</td>\n",
       "      <td>3333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>51</td>\n",
       "      <td>3333</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>WV</td>\n",
       "      <td>353-7822</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>106</td>\n",
       "      <td>1</td>\n",
       "      <td>3010</td>\n",
       "      <td>2411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       state phone number international plan voice mail plan\n",
       "count   3333         3333               3333            3333\n",
       "unique    51         3333                  2               2\n",
       "top       WV     353-7822                 no              no\n",
       "freq     106            1               3010            2411"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include ='object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "546cc847",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state                     0\n",
       "account length            0\n",
       "area code                 0\n",
       "phone number              0\n",
       "international plan        0\n",
       "voice mail plan           0\n",
       "number vmail messages     0\n",
       "total day minutes         0\n",
       "total day calls           0\n",
       "total day charge          0\n",
       "total eve minutes         0\n",
       "total eve calls           0\n",
       "total eve charge          0\n",
       "total night minutes       0\n",
       "total night calls         0\n",
       "total night charge        0\n",
       "total intl minutes        0\n",
       "total intl calls          0\n",
       "total intl charge         0\n",
       "customer service calls    0\n",
       "churn                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for null values\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "400de90d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state                     0\n",
       "account length            0\n",
       "area code                 0\n",
       "phone number              0\n",
       "international plan        0\n",
       "voice mail plan           0\n",
       "number vmail messages     0\n",
       "total day minutes         0\n",
       "total day calls           0\n",
       "total day charge          0\n",
       "total eve minutes         0\n",
       "total eve calls           0\n",
       "total eve charge          0\n",
       "total night minutes       0\n",
       "total night calls         0\n",
       "total night charge        0\n",
       "total intl minutes        0\n",
       "total intl calls          0\n",
       "total intl charge         0\n",
       "customer service calls    0\n",
       "churn                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54a4d8a3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "893797a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state                     3333\n",
       "account length            3333\n",
       "area code                 3333\n",
       "phone number              3333\n",
       "international plan        3333\n",
       "voice mail plan           3333\n",
       "number vmail messages     3333\n",
       "total day minutes         3333\n",
       "total day calls           3333\n",
       "total day charge          3333\n",
       "total eve minutes         3333\n",
       "total eve calls           3333\n",
       "total eve charge          3333\n",
       "total night minutes       3333\n",
       "total night calls         3333\n",
       "total night charge        3333\n",
       "total intl minutes        3333\n",
       "total intl calls          3333\n",
       "total intl charge         3333\n",
       "customer service calls    3333\n",
       "churn                     3333\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get values that is not null\n",
    "\n",
    "df.notnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0707a5bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state                     3333\n",
       "account length            3333\n",
       "area code                 3333\n",
       "phone number              3333\n",
       "international plan        3333\n",
       "voice mail plan           3333\n",
       "number vmail messages     3333\n",
       "total day minutes         3333\n",
       "total day calls           3333\n",
       "total day charge          3333\n",
       "total eve minutes         3333\n",
       "total eve calls           3333\n",
       "total eve charge          3333\n",
       "total night minutes       3333\n",
       "total night calls         3333\n",
       "total night charge        3333\n",
       "total intl minutes        3333\n",
       "total intl calls          3333\n",
       "total intl charge         3333\n",
       "customer service calls    3333\n",
       "churn                     3333\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.notna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbf615ce",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1d6d180b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for duplicate values\n",
    "\n",
    "df.duplicated().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7055ded6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6a8339a2",
   "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>state</th>\n",
       "      <th>account length</th>\n",
       "      <th>area code</th>\n",
       "      <th>phone number</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>...</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>382-4657</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>...</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>371-7191</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>...</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>358-1921</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>...</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>375-9999</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>...</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>330-6626</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>...</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",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  state  account length  area code phone number international plan  \\\n",
       "0    KS             128        415     382-4657                 no   \n",
       "1    OH             107        415     371-7191                 no   \n",
       "2    NJ             137        415     358-1921                 no   \n",
       "3    OH              84        408     375-9999                yes   \n",
       "4    OK              75        415     330-6626                yes   \n",
       "\n",
       "  voice mail plan  number vmail messages  total day minutes  total day calls  \\\n",
       "0             yes                     25              265.1              110   \n",
       "1             yes                     26              161.6              123   \n",
       "2              no                      0              243.4              114   \n",
       "3              no                      0              299.4               71   \n",
       "4              no                      0              166.7              113   \n",
       "\n",
       "   total day charge  ...  total eve calls  total eve charge  \\\n",
       "0             45.07  ...               99             16.78   \n",
       "1             27.47  ...              103             16.62   \n",
       "2             41.38  ...              110             10.30   \n",
       "3             50.90  ...               88              5.26   \n",
       "4             28.34  ...              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  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Analyzing the chun variable\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0ea4525b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['churn'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f8f3783a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    2850\n",
       "True      483\n",
       "Name: churn, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df['churn'].value_counts()\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9f912641",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'14.491449144914492%'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str(483/3333*100) + '%'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6cc73738",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "94dda50e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a donut chart\n",
    "\n",
    "plt.pie(df1, labels= ['Not Churned', 'Churned'],\n",
    "       colors=['Yellow', 'Blue'], startangle =50,\n",
    "        shadow=True, radius =1.5, explode=(0,0.2),\n",
    "       autopct='%1.2f%%', pctdistance=0.5)\n",
    "\n",
    "circle = plt.Circle((0,0), 1, color='white')\n",
    "c=plt.gcf()\n",
    "\n",
    "c.gca().add_artist(circle)\n",
    "\n",
    "plt.title('Churned vs Non-Churned (DSL)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "7a72d657",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='churn', ylabel='count'>"
      ]
     },
     "execution_count": 76,
     "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": [
    "sns.countplot(x='churn', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c883579b",
   "metadata": {},
   "source": [
    "# Outcome\n",
    "\n",
    "After analysing the Chun column, we noticed that almost 15% customers have churned. Now, we will analyze other columns to find the possible reason for this churn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "f9dd8773",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['state', 'account length', 'area code', 'phone number',\n",
       "       'international plan', 'voice mail plan', 'number vmail messages',\n",
       "       'total day minutes', 'total day calls', 'total day charge',\n",
       "       'total eve minutes', 'total eve calls', 'total eve charge',\n",
       "       'total night minutes', 'total night calls', 'total night charge',\n",
       "       'total intl minutes', 'total intl calls', 'total intl charge',\n",
       "       'customer service calls', 'churn'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Analysing the State Variable\n",
    "\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "a2055531",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['state'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "6c398ac6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['KS', 'OH', 'NJ', 'OK', 'AL', 'MA', 'MO', 'LA', 'WV', 'IN', 'RI',\n",
       "       'IA', 'MT', 'NY', 'ID', 'VT', 'VA', 'TX', 'FL', 'CO', 'AZ', 'SC',\n",
       "       'NE', 'WY', 'HI', 'IL', 'NH', 'GA', 'AK', 'MD', 'AR', 'WI', 'OR',\n",
       "       'MI', 'DE', 'UT', 'CA', 'MN', 'SD', 'NC', 'WA', 'NM', 'NV', 'DC',\n",
       "       'KY', 'ME', 'MS', 'TN', 'PA', 'CT', 'ND'], dtype=object)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['state'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "88b300a4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CA     34\n",
       "IA     44\n",
       "PA     45\n",
       "LA     51\n",
       "AK     52\n",
       "HI     53\n",
       "TN     53\n",
       "GA     54\n",
       "DC     54\n",
       "AR     55\n",
       "NH     56\n",
       "IL     58\n",
       "KY     59\n",
       "SC     60\n",
       "SD     60\n",
       "DE     61\n",
       "OK     61\n",
       "NE     61\n",
       "ME     62\n",
       "NM     62\n",
       "ND     62\n",
       "MO     63\n",
       "FL     63\n",
       "AZ     64\n",
       "MS     65\n",
       "RI     65\n",
       "MA     65\n",
       "NV     66\n",
       "CO     66\n",
       "WA     66\n",
       "MT     68\n",
       "NJ     68\n",
       "NC     68\n",
       "KS     70\n",
       "MD     70\n",
       "IN     71\n",
       "UT     72\n",
       "TX     72\n",
       "MI     73\n",
       "VT     73\n",
       "ID     73\n",
       "CT     74\n",
       "VA     77\n",
       "WY     77\n",
       "WI     78\n",
       "OR     78\n",
       "OH     78\n",
       "AL     80\n",
       "NY     83\n",
       "MN     84\n",
       "WV    106\n",
       "Name: state, dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['state'].value_counts(ascending = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "ce49a501",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,7))\n",
    "sns.countplot(x='state', data=df)\n",
    "\n",
    "plt.xticks(rotation =75, fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "\n",
    "plt.xlabel('State',fontsize=25)\n",
    "plt.ylabel('Count',fontsize=25)\n",
    "\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "fc68bf4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style='whitegrid')\n",
    "\n",
    "plt.figure(figsize=(20,7))\n",
    "\n",
    "sns.countplot(x=df['state'], hue=df['churn'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbd507c9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}