Model1_DataWrangling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>ACCT_LEN</th>\n",
       "      <th>AREA_CD</th>\n",
       "      <th>PHONE</th>\n",
       "      <th>INTL_PLN</th>\n",
       "      <th>VM_PLN</th>\n",
       "      <th>VM_CNT</th>\n",
       "      <th>DAY_MINS</th>\n",
       "      <th>DAY_CALLS</th>\n",
       "      <th>DAY_CHG</th>\n",
       "      <th>...</th>\n",
       "      <th>EVE_CALLS</th>\n",
       "      <th>EVE_CHG</th>\n",
       "      <th>NITE_MINS</th>\n",
       "      <th>NITE_CALLS</th>\n",
       "      <th>NITE_CHG</th>\n",
       "      <th>INTL_MINS</th>\n",
       "      <th>INTL_CALLS</th>\n",
       "      <th>INTL_CHG</th>\n",
       "      <th>CSRV_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  ACCT_LEN  AREA_CD     PHONE INTL_PLN VM_PLN  VM_CNT  DAY_MINS  \\\n",
       "0    KS       128      415  382-4657       no    yes      25     265.1   \n",
       "1    OH       107      415  371-7191       no    yes      26     161.6   \n",
       "2    NJ       137      415  358-1921       no     no       0     243.4   \n",
       "3    OH        84      408  375-9999      yes     no       0     299.4   \n",
       "4    OK        75      415  330-6626      yes     no       0     166.7   \n",
       "\n",
       "   DAY_CALLS  DAY_CHG   ...    EVE_CALLS  EVE_CHG  NITE_MINS  NITE_CALLS  \\\n",
       "0        110    45.07   ...           99    16.78      244.7          91   \n",
       "1        123    27.47   ...          103    16.62      254.4         103   \n",
       "2        114    41.38   ...          110    10.30      162.6         104   \n",
       "3         71    50.90   ...           88     5.26      196.9          89   \n",
       "4        113    28.34   ...          122    12.61      186.9         121   \n",
       "\n",
       "   NITE_CHG  INTL_MINS  INTL_CALLS  INTL_CHG  CSRV_CALLS   CHURN  \n",
       "0     11.01       10.0           3      2.70           1  False.  \n",
       "1     11.45       13.7           3      3.70           1  False.  \n",
       "2      7.32       12.2           5      3.29           0  False.  \n",
       "3      8.86        6.6           7      1.78           2  False.  \n",
       "4      8.41       10.1           3      2.73           3  False.  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_names = ['STATE','ACCT_LEN','AREA_CD','PHONE','INTL_PLN','VM_PLN','VM_CNT','DAY_MINS','DAY_CALLS','DAY_CHG','EVE_MINS','EVE_CALLS','EVE_CHG','NITE_MINS','NITE_CALLS','NITE_CHG','INTL_MINS','INTL_CALLS','INTL_CHG','CSRV_CALLS','CHURN']\n",
    "data=pd.read_csv(\"C:/Users/Bhaskar22/Desktop/MINIPROJECT/data/Mobile_churn_UCI.csv\", names=col_names, header=0)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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",
      "STATE         3333 non-null object\n",
      "ACCT_LEN      3333 non-null int64\n",
      "AREA_CD       3333 non-null int64\n",
      "PHONE         3333 non-null object\n",
      "INTL_PLN      3333 non-null object\n",
      "VM_PLN        3333 non-null object\n",
      "VM_CNT        3333 non-null int64\n",
      "DAY_MINS      3333 non-null float64\n",
      "DAY_CALLS     3333 non-null int64\n",
      "DAY_CHG       3333 non-null float64\n",
      "EVE_MINS      3333 non-null float64\n",
      "EVE_CALLS     3333 non-null int64\n",
      "EVE_CHG       3333 non-null float64\n",
      "NITE_MINS     3333 non-null float64\n",
      "NITE_CALLS    3333 non-null int64\n",
      "NITE_CHG      3333 non-null float64\n",
      "INTL_MINS     3333 non-null float64\n",
      "INTL_CALLS    3333 non-null int64\n",
      "INTL_CHG      3333 non-null float64\n",
      "CSRV_CALLS    3333 non-null int64\n",
      "CHURN         3333 non-null object\n",
      "dtypes: float64(8), int64(8), object(5)\n",
      "memory usage: 546.9+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(3333, 21)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.info()\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all(data.CHURN.notnull())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 3333 rows and 21 columns. All columns are populated, there are no null or missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['STATE'] = data['STATE'].astype('category')\n",
    "data['CHURN'] = data['CHURN'].apply(lambda x: 0 if x=='False.' else 1)   #---> True=1; False=0\n",
    "data['INTL_PLN'] = data['INTL_PLN'].apply(lambda x: 0 if x=='no' else 1) #---> No=0; Yes=1 \n",
    "data['VM_PLN'] = data['VM_PLN'].apply(lambda x: 0 if x=='no' else 1)     #---> No=0; Yes=1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation: 0.9999999521903994\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features=[\"DAY_CHG\",\"DAY_MINS\",\"DAY_CALLS\"]\n",
    "data[features].hist(figsize=(15,8))\n",
    "print(\"Correlation:\",data['DAY_CHG'].corr(data['DAY_MINS']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation: 0.9999997760198502\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features=[\"EVE_CHG\",\"EVE_MINS\",\"EVE_CALLS\"]\n",
    "data[features].hist(figsize=(15,8))\n",
    "print(\"Correlation:\",data['EVE_MINS'].corr(data['EVE_CHG']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation: 0.9999927417510324\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features=[\"INTL_MINS\",\"INTL_CHG\",\"INTL_CALLS\"]\n",
    "data[features].hist(figsize=(15,8))\n",
    "print(\"Correlation:\",data['INTL_MINS'].corr(data['INTL_CHG']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation: 0.9569266420697519\n"
     ]
    }
   ],
   "source": [
    "print(\"Correlation:\",data['VM_CNT'].corr(data['VM_PLN']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3333 entries, 0 to 3332\n",
      "Data columns (total 14 columns):\n",
      "ACCT_LEN      3333 non-null int64\n",
      "INTL_PLN      3333 non-null int64\n",
      "VM_PLN        3333 non-null int64\n",
      "VM_CNT        3333 non-null int64\n",
      "DAY_MINS      3333 non-null float64\n",
      "DAY_CALLS     3333 non-null int64\n",
      "EVE_MINS      3333 non-null float64\n",
      "EVE_CALLS     3333 non-null int64\n",
      "NITE_MINS     3333 non-null float64\n",
      "NITE_CALLS    3333 non-null int64\n",
      "INTL_MINS     3333 non-null float64\n",
      "INTL_CALLS    3333 non-null int64\n",
      "CSRV_CALLS    3333 non-null int64\n",
      "CHURN         3333 non-null int64\n",
      "dtypes: float64(4), int64(10)\n",
      "memory usage: 364.6 KB\n"
     ]
    },
    {
     "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>ACCT_LEN</th>\n",
       "      <th>INTL_PLN</th>\n",
       "      <th>VM_PLN</th>\n",
       "      <th>VM_CNT</th>\n",
       "      <th>DAY_MINS</th>\n",
       "      <th>DAY_CALLS</th>\n",
       "      <th>EVE_MINS</th>\n",
       "      <th>EVE_CALLS</th>\n",
       "      <th>NITE_MINS</th>\n",
       "      <th>NITE_CALLS</th>\n",
       "      <th>INTL_MINS</th>\n",
       "      <th>INTL_CALLS</th>\n",
       "      <th>CSRV_CALLS</th>\n",
       "      <th>CHURN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>265.1</td>\n",
       "      <td>110</td>\n",
       "      <td>197.4</td>\n",
       "      <td>99</td>\n",
       "      <td>244.7</td>\n",
       "      <td>91</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>107</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>161.6</td>\n",
       "      <td>123</td>\n",
       "      <td>195.5</td>\n",
       "      <td>103</td>\n",
       "      <td>254.4</td>\n",
       "      <td>103</td>\n",
       "      <td>13.7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>243.4</td>\n",
       "      <td>114</td>\n",
       "      <td>121.2</td>\n",
       "      <td>110</td>\n",
       "      <td>162.6</td>\n",
       "      <td>104</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>71</td>\n",
       "      <td>61.9</td>\n",
       "      <td>88</td>\n",
       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>113</td>\n",
       "      <td>148.3</td>\n",
       "      <td>122</td>\n",
       "      <td>186.9</td>\n",
       "      <td>121</td>\n",
       "      <td>10.1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ACCT_LEN  INTL_PLN  VM_PLN  VM_CNT  DAY_MINS  DAY_CALLS  EVE_MINS  \\\n",
       "0       128         0       1      25     265.1        110     197.4   \n",
       "1       107         0       1      26     161.6        123     195.5   \n",
       "2       137         0       0       0     243.4        114     121.2   \n",
       "3        84         1       0       0     299.4         71      61.9   \n",
       "4        75         1       0       0     166.7        113     148.3   \n",
       "\n",
       "   EVE_CALLS  NITE_MINS  NITE_CALLS  INTL_MINS  INTL_CALLS  CSRV_CALLS  CHURN  \n",
       "0         99      244.7          91       10.0           3           1      0  \n",
       "1        103      254.4         103       13.7           3           1      0  \n",
       "2        110      162.6         104       12.2           5           0      0  \n",
       "3         88      196.9          89        6.6           7           2      0  \n",
       "4        122      186.9         121       10.1           3           3      0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(['PHONE'],axis=1,inplace=True)\n",
    "data.drop(['DAY_CHG'],axis=1,inplace=True)\n",
    "data.drop(['EVE_CHG'],axis=1,inplace=True)\n",
    "data.drop(['INTL_CHG'],axis=1,inplace=True)\n",
    "data.drop(['NITE_CHG'],axis=1,inplace=True)\n",
    "#data.drop(['VM_CNT'],axis=1,inplace=True)\n",
    "\n",
    "data.drop(['STATE'],axis=1,inplace=True)\n",
    "data.drop(['AREA_CD'],axis=1,inplace=True)\n",
    "\n",
    "data.info()\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv('C:/Users/Bhaskar22/Desktop/MINIPROJECT/data/Mobile_churn_UCI_Cleaned.csv')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}