churn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# User Churn Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, we have a dataset of cell phone service customers. The data contain the information of customers' plans and usage of the service, as well as whether or not they stopped using the service eventually. The goal of this work is to use supervised learning models to predict and prevent user churn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Contents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ul>\n",
    "<li>[Part 1: Data Exploration](#Part-1:-Data-Exploration)\n",
    "<li>[Part 2: Feature Preprocessing and Train/Test Split](#Part-2:-Feature-Preprocessing-and-Train/Test-Split)\n",
    "<li>[Part 3: Model Training and Results Evaluation](#Part-3:-Model-Training-and-Result-Evaluation)\n",
    "<li>[Part 4: Feature Importance](#Part-4:-Feature-Importance)\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1: Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 1.1: Import the Raw Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 21)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "df = pd.read_csv('churn.all')\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state                             object\n",
       "account_length                     int64\n",
       "area_code                          int64\n",
       "phone_number                      object\n",
       "intl_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",
       "number_customer_service_calls      int64\n",
       "churned                           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        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>intl_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>number_customer_service_calls</th>\n",
       "      <th>churned</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>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>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>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>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>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>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>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>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>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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AL</td>\n",
       "      <td>118</td>\n",
       "      <td>510</td>\n",
       "      <td>391-8027</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>223.4</td>\n",
       "      <td>98</td>\n",
       "      <td>37.98</td>\n",
       "      <td>220.6</td>\n",
       "      <td>101</td>\n",
       "      <td>18.75</td>\n",
       "      <td>203.9</td>\n",
       "      <td>118</td>\n",
       "      <td>9.18</td>\n",
       "      <td>6.3</td>\n",
       "      <td>6</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MA</td>\n",
       "      <td>121</td>\n",
       "      <td>510</td>\n",
       "      <td>355-9993</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>24</td>\n",
       "      <td>218.2</td>\n",
       "      <td>88</td>\n",
       "      <td>37.09</td>\n",
       "      <td>348.5</td>\n",
       "      <td>108</td>\n",
       "      <td>29.62</td>\n",
       "      <td>212.6</td>\n",
       "      <td>118</td>\n",
       "      <td>9.57</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7</td>\n",
       "      <td>2.03</td>\n",
       "      <td>3</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MO</td>\n",
       "      <td>147</td>\n",
       "      <td>415</td>\n",
       "      <td>329-9001</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>79</td>\n",
       "      <td>26.69</td>\n",
       "      <td>103.1</td>\n",
       "      <td>94</td>\n",
       "      <td>8.76</td>\n",
       "      <td>211.8</td>\n",
       "      <td>96</td>\n",
       "      <td>9.53</td>\n",
       "      <td>7.1</td>\n",
       "      <td>6</td>\n",
       "      <td>1.92</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>LA</td>\n",
       "      <td>117</td>\n",
       "      <td>408</td>\n",
       "      <td>335-4719</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>184.5</td>\n",
       "      <td>97</td>\n",
       "      <td>31.37</td>\n",
       "      <td>351.6</td>\n",
       "      <td>80</td>\n",
       "      <td>29.89</td>\n",
       "      <td>215.8</td>\n",
       "      <td>90</td>\n",
       "      <td>9.71</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4</td>\n",
       "      <td>2.35</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>WV</td>\n",
       "      <td>141</td>\n",
       "      <td>415</td>\n",
       "      <td>330-8173</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>37</td>\n",
       "      <td>258.6</td>\n",
       "      <td>84</td>\n",
       "      <td>43.96</td>\n",
       "      <td>222.0</td>\n",
       "      <td>111</td>\n",
       "      <td>18.87</td>\n",
       "      <td>326.4</td>\n",
       "      <td>97</td>\n",
       "      <td>14.69</td>\n",
       "      <td>11.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.02</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state  account_length  area_code phone_number intl_plan voice_mail_plan  \\\n",
       "0    KS             128        415     382-4657        no             yes   \n",
       "1    OH             107        415     371-7191        no             yes   \n",
       "2    NJ             137        415     358-1921        no              no   \n",
       "3    OH              84        408     375-9999       yes              no   \n",
       "4    OK              75        415     330-6626       yes              no   \n",
       "5    AL             118        510     391-8027       yes              no   \n",
       "6    MA             121        510     355-9993        no             yes   \n",
       "7    MO             147        415     329-9001       yes              no   \n",
       "8    LA             117        408     335-4719        no              no   \n",
       "9    WV             141        415     330-8173       yes             yes   \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",
       "5                      0              223.4               98   \n",
       "6                     24              218.2               88   \n",
       "7                      0              157.0               79   \n",
       "8                      0              184.5               97   \n",
       "9                     37              258.6               84   \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",
       "5             37.98              220.6              101             18.75   \n",
       "6             37.09              348.5              108             29.62   \n",
       "7             26.69              103.1               94              8.76   \n",
       "8             31.37              351.6               80             29.89   \n",
       "9             43.96              222.0              111             18.87   \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",
       "5                203.9                118                9.18   \n",
       "6                212.6                118                9.57   \n",
       "7                211.8                 96                9.53   \n",
       "8                215.8                 90                9.71   \n",
       "9                326.4                 97               14.69   \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",
       "5                 6.3                 6               1.70   \n",
       "6                 7.5                 7               2.03   \n",
       "7                 7.1                 6               1.92   \n",
       "8                 8.7                 4               2.35   \n",
       "9                11.2                 5               3.02   \n",
       "\n",
       "   number_customer_service_calls  churned  \n",
       "0                              1   False.  \n",
       "1                              1   False.  \n",
       "2                              0   False.  \n",
       "3                              2   False.  \n",
       "4                              3   False.  \n",
       "5                              0   False.  \n",
       "6                              3   False.  \n",
       "7                              0   False.  \n",
       "8                              1   False.  \n",
       "9                              0   False.  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 1.2: Data cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first check if there are NANs in the data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state                            0\n",
       "account_length                   0\n",
       "area_code                        0\n",
       "phone_number                     0\n",
       "intl_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",
       "number_customer_service_calls    0\n",
       "churned                          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum() # there are no NANs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``intl_plan``, ``voice_mail_plan``, and ``churned`` are categorical features with binary variables, so we can just transform them to 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Note that there are white spaces in the strings\n",
    "df.voice_mail_plan = df.voice_mail_plan.map({' no': 0, ' yes':1})\n",
    "df.intl_plan = df.intl_plan.map({' no': 0, ' yes':1})\n",
    "df.churned = df.churned.map({' False.': 0, ' True.':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>intl_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>number_customer_service_calls</th>\n",
       "      <th>churned</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>0</td>\n",
       "      <td>1</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>0</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>0</td>\n",
       "      <td>1</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>0</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>0</td>\n",
       "      <td>0</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>0</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>1</td>\n",
       "      <td>0</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>0</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>1</td>\n",
       "      <td>0</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AL</td>\n",
       "      <td>118</td>\n",
       "      <td>510</td>\n",
       "      <td>391-8027</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>223.4</td>\n",
       "      <td>98</td>\n",
       "      <td>37.98</td>\n",
       "      <td>220.6</td>\n",
       "      <td>101</td>\n",
       "      <td>18.75</td>\n",
       "      <td>203.9</td>\n",
       "      <td>118</td>\n",
       "      <td>9.18</td>\n",
       "      <td>6.3</td>\n",
       "      <td>6</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MA</td>\n",
       "      <td>121</td>\n",
       "      <td>510</td>\n",
       "      <td>355-9993</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>218.2</td>\n",
       "      <td>88</td>\n",
       "      <td>37.09</td>\n",
       "      <td>348.5</td>\n",
       "      <td>108</td>\n",
       "      <td>29.62</td>\n",
       "      <td>212.6</td>\n",
       "      <td>118</td>\n",
       "      <td>9.57</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7</td>\n",
       "      <td>2.03</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MO</td>\n",
       "      <td>147</td>\n",
       "      <td>415</td>\n",
       "      <td>329-9001</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>79</td>\n",
       "      <td>26.69</td>\n",
       "      <td>103.1</td>\n",
       "      <td>94</td>\n",
       "      <td>8.76</td>\n",
       "      <td>211.8</td>\n",
       "      <td>96</td>\n",
       "      <td>9.53</td>\n",
       "      <td>7.1</td>\n",
       "      <td>6</td>\n",
       "      <td>1.92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>LA</td>\n",
       "      <td>117</td>\n",
       "      <td>408</td>\n",
       "      <td>335-4719</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>184.5</td>\n",
       "      <td>97</td>\n",
       "      <td>31.37</td>\n",
       "      <td>351.6</td>\n",
       "      <td>80</td>\n",
       "      <td>29.89</td>\n",
       "      <td>215.8</td>\n",
       "      <td>90</td>\n",
       "      <td>9.71</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4</td>\n",
       "      <td>2.35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>WV</td>\n",
       "      <td>141</td>\n",
       "      <td>415</td>\n",
       "      <td>330-8173</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>258.6</td>\n",
       "      <td>84</td>\n",
       "      <td>43.96</td>\n",
       "      <td>222.0</td>\n",
       "      <td>111</td>\n",
       "      <td>18.87</td>\n",
       "      <td>326.4</td>\n",
       "      <td>97</td>\n",
       "      <td>14.69</td>\n",
       "      <td>11.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state  account_length  area_code phone_number  intl_plan  voice_mail_plan  \\\n",
       "0    KS             128        415     382-4657          0                1   \n",
       "1    OH             107        415     371-7191          0                1   \n",
       "2    NJ             137        415     358-1921          0                0   \n",
       "3    OH              84        408     375-9999          1                0   \n",
       "4    OK              75        415     330-6626          1                0   \n",
       "5    AL             118        510     391-8027          1                0   \n",
       "6    MA             121        510     355-9993          0                1   \n",
       "7    MO             147        415     329-9001          1                0   \n",
       "8    LA             117        408     335-4719          0                0   \n",
       "9    WV             141        415     330-8173          1                1   \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",
       "5                      0              223.4               98   \n",
       "6                     24              218.2               88   \n",
       "7                      0              157.0               79   \n",
       "8                      0              184.5               97   \n",
       "9                     37              258.6               84   \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",
       "5             37.98              220.6              101             18.75   \n",
       "6             37.09              348.5              108             29.62   \n",
       "7             26.69              103.1               94              8.76   \n",
       "8             31.37              351.6               80             29.89   \n",
       "9             43.96              222.0              111             18.87   \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",
       "5                203.9                118                9.18   \n",
       "6                212.6                118                9.57   \n",
       "7                211.8                 96                9.53   \n",
       "8                215.8                 90                9.71   \n",
       "9                326.4                 97               14.69   \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",
       "5                 6.3                 6               1.70   \n",
       "6                 7.5                 7               2.03   \n",
       "7                 7.1                 6               1.92   \n",
       "8                 8.7                 4               2.35   \n",
       "9                11.2                 5               3.02   \n",
       "\n",
       "   number_customer_service_calls  churned  \n",
       "0                              1        0  \n",
       "1                              1        0  \n",
       "2                              0        0  \n",
       "3                              2        0  \n",
       "4                              3        0  \n",
       "5                              0        0  \n",
       "6                              3        0  \n",
       "7                              0        0  \n",
       "8                              1        0  \n",
       "9                              0        0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 1.3:  Data Analysis and Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Statistical Overview\n",
    "Let's check the differences between the users who continue using the service and who do not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.8586\n",
       "1    0.1414\n",
       "Name: churned, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.churned.value_counts() / len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we should note that this is an unbalanced dataset. Only ~ 14% of the users stop using the sercice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead 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>intl_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>number_customer_service_calls</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>churned</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>99.917074</td>\n",
       "      <td>0.063825</td>\n",
       "      <td>0.284416</td>\n",
       "      <td>8.291870</td>\n",
       "      <td>175.746564</td>\n",
       "      <td>99.899604</td>\n",
       "      <td>29.877494</td>\n",
       "      <td>198.805031</td>\n",
       "      <td>100.241556</td>\n",
       "      <td>16.898654</td>\n",
       "      <td>199.455113</td>\n",
       "      <td>99.975775</td>\n",
       "      <td>8.975593</td>\n",
       "      <td>10.190869</td>\n",
       "      <td>4.481947</td>\n",
       "      <td>2.752055</td>\n",
       "      <td>1.457722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>102.332390</td>\n",
       "      <td>0.281471</td>\n",
       "      <td>0.144272</td>\n",
       "      <td>4.496464</td>\n",
       "      <td>207.870580</td>\n",
       "      <td>100.817539</td>\n",
       "      <td>35.338416</td>\n",
       "      <td>211.757850</td>\n",
       "      <td>99.884017</td>\n",
       "      <td>17.999562</td>\n",
       "      <td>206.078218</td>\n",
       "      <td>99.575672</td>\n",
       "      <td>9.273607</td>\n",
       "      <td>10.692362</td>\n",
       "      <td>4.151344</td>\n",
       "      <td>2.887426</td>\n",
       "      <td>2.254597</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         account_length  intl_plan  voice_mail_plan  number_vmail_messages  \\\n",
       "churned                                                                      \n",
       "0             99.917074   0.063825         0.284416               8.291870   \n",
       "1            102.332390   0.281471         0.144272               4.496464   \n",
       "\n",
       "         total_day_minutes  total_day_calls  total_day_charge  \\\n",
       "churned                                                         \n",
       "0               175.746564        99.899604         29.877494   \n",
       "1               207.870580       100.817539         35.338416   \n",
       "\n",
       "         total_eve_minutes  total_eve_calls  total_eve_charge  \\\n",
       "churned                                                         \n",
       "0               198.805031       100.241556         16.898654   \n",
       "1               211.757850        99.884017         17.999562   \n",
       "\n",
       "         total_night_minutes  total_night_calls  total_night_charge  \\\n",
       "churned                                                               \n",
       "0                 199.455113          99.975775            8.975593   \n",
       "1                 206.078218          99.575672            9.273607   \n",
       "\n",
       "         total_intl_minutes  total_intl_calls  total_intl_charge  \\\n",
       "churned                                                            \n",
       "0                 10.190869          4.481947           2.752055   \n",
       "1                 10.692362          4.151344           2.887426   \n",
       "\n",
       "         number_customer_service_calls  \n",
       "churned                                 \n",
       "0                             1.457722  \n",
       "1                             2.254597  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare the mean values of features between the two classes \n",
    "df.groupby('churned').mean().drop(['area_code'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### K-S Tests and Z Tests\n",
    "\n",
    "We can use K-S tests to check whether the distributions of each feature of the two classes are drawn from the same distribution. For ``intl_plan`` and ``voice_mail_plan``, the values are either 0 or 1, so we can use [proportion Z tests](https://onlinecourses.science.psu.edu/stat414/node/268) to check whether the difference in mean values is statistically significant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "account_length:\n",
      "Ks_2sampResult(statistic=0.04158936408765168, pvalue=0.2393621092523914) \n",
      "\n",
      "number_vmail_messages:\n",
      "Ks_2sampResult(statistic=0.13991198460966192, pvalue=7.502131588373171e-11) \n",
      "\n",
      "total_day_minutes:\n",
      "Ks_2sampResult(statistic=0.30453806087407176, pvalue=8.050945996407213e-50) \n",
      "\n",
      "total_day_calls:\n",
      "Ks_2sampResult(statistic=0.0374314160975846, pvalue=0.3566846596189682) \n",
      "\n",
      "total_day_charge:\n",
      "Ks_2sampResult(statistic=0.30453806087407176, pvalue=8.050945996407213e-50) \n",
      "\n",
      "total_eve_minutes:\n",
      "Ks_2sampResult(statistic=0.11320491138661631, pvalue=2.9892973748115123e-07) \n",
      "\n",
      "total_eve_calls:\n",
      "Ks_2sampResult(statistic=0.02443964072957161, pvalue=0.8573431622439269) \n",
      "\n",
      "total_eve_charge:\n",
      "Ks_2sampResult(statistic=0.11320491138661631, pvalue=2.9892973748115123e-07) \n",
      "\n",
      "total_night_minutes:\n",
      "Ks_2sampResult(statistic=0.06274383053759103, pvalue=0.016006448527058535) \n",
      "\n",
      "total_night_calls:\n",
      "Ks_2sampResult(statistic=0.0302110175078604, pvalue=0.6303685394085426) \n",
      "\n",
      "total_night_charge:\n",
      "Ks_2sampResult(statistic=0.062277955857879874, pvalue=0.017191586699967065) \n",
      "\n",
      "total_intl_minutes:\n",
      "Ks_2sampResult(statistic=0.09604596278735389, pvalue=2.442777837021745e-05) \n",
      "\n",
      "total_intl_calls:\n",
      "Ks_2sampResult(statistic=0.09731838712472626, pvalue=1.8065175321591677e-05) \n",
      "\n",
      "total_intl_charge:\n",
      "Ks_2sampResult(statistic=0.09604596278735389, pvalue=2.442777837021745e-05) \n",
      "\n",
      "number_customer_service_calls:\n",
      "Ks_2sampResult(statistic=0.2381782652658797, pvalue=1.2223785561425916e-30) \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ks_2samp\n",
    "\n",
    "def run_KS_test(feature):\n",
    "\n",
    "    dist1 = df.loc[df.churned == 0, feature]\n",
    "    dist2 = df.loc[df.churned == 1, feature]\n",
    "    print feature+':'\n",
    "    print ks_2samp(dist1,dist2),'\\n'\n",
    "\n",
    "run_KS_test('account_length')   \n",
    "run_KS_test('number_vmail_messages')    \n",
    "run_KS_test('total_day_minutes')\n",
    "run_KS_test('total_day_calls')\n",
    "run_KS_test('total_day_charge')\n",
    "run_KS_test('total_eve_minutes')\n",
    "run_KS_test('total_eve_calls')\n",
    "run_KS_test('total_eve_charge')\n",
    "run_KS_test('total_night_minutes')\n",
    "run_KS_test('total_night_calls')\n",
    "run_KS_test('total_night_charge')\n",
    "run_KS_test('total_intl_minutes')\n",
    "run_KS_test('total_intl_calls')\n",
    "run_KS_test('total_intl_charge')\n",
    "run_KS_test('number_customer_service_calls')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the p-values for ``account_length`` as well as the numbers of calls during daytime, evening, and nighttime are pretty high (> 23%), so we cannot reject the null hypothesis that the distributions of the two samples are the same. In contrast, the p-values of the other features are all very low, showing that the two samples are not drawn from the same distribution. The numbers of calls should not be relevant, and what matters should be the total minutes/charge of the calls. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "intl_plan:\n",
      "z-score = -18.3227379767; p-value = 5.45006509691e-75 \n",
      "\n",
      "voice_mail_plan:\n",
      "z-score = 7.8275487721; p-value = 4.97473681535e-15 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.stats.proportion import proportions_ztest\n",
    "\n",
    "def run_proportion_Z_test(feature):\n",
    "\n",
    "    dist1 = df.loc[df.churned == 0, feature]\n",
    "    dist2 = df.loc[df.churned == 1, feature]\n",
    "    n1 = len(dist1)\n",
    "    p1 = dist1.sum()\n",
    "    n2 = len(dist2)\n",
    "    p2 = dist2.sum()\n",
    "    z_score, p_value = proportions_ztest([p1, p2], [n1, n2])\n",
    "    print feature+':'\n",
    "    print 'z-score = {}; p-value = {}'.format(z_score, p_value),'\\n'\n",
    "    \n",
    "run_proportion_Z_test('intl_plan')    \n",
    "run_proportion_Z_test('voice_mail_plan')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Z tests reject the null hypothesis for the cases of these two features, so the difference between the two samples are significant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distribution Plots\n",
    "\n",
    "For the continuous variables (besides ``intl_plan`` and ``voice_mail_plan``), we can also visualize the distributions of the two classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Plot the distributions for the numerical features\n",
    "def DensityPlot(feature):\n",
    "    fig = plt.figure(figsize=(8,4))\n",
    "    sns.kdeplot(df.loc[df.churned == 0,feature], color='b',shade=True, label=\"stay\")\n",
    "    sns.kdeplot(df.loc[df.churned == 1,feature], color='r',shade=True, label=\"churn\")\n",
    "    plt.xlabel(feature)\n",
    "    plt.ylabel('frequency')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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TWLoKlrJV1Y4mgFRzEeIq1qbI8b94ruFhAJYskal2QtxPpoV+xYoVLFq0CLvd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\nCOHBpNELIYQQHuz/AbUvZiFTOPb1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DensityPlot('account_length')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fzrRp09B1nRkzZuBwONr1PqnGDIqplqnbbFBUpMtcdSGESGJxg3p7qarKrFmz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\nRw0F8OYYGX8gHNRt3oZIUA9muCLj8gC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fFA3qLTJ1XVXxZ+U3e350TD0a1HWrzThOOAs2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\nwUGIoC4IgiAIDkIEdUEQBEFwEP8frJxc68DKw8QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DensityPlot('number_vmail_messages')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8AyG/PgZ6PZaXJ+OqUtXb0f7G3rI1jrr1MHy5Bu3OHThatPR2JFGM5Vvok5OT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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DensityPlot('total_day_minutes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uVp6zKqD9j9kARrkNILxICgAhhNekpJzp/nd/+q+3bSOm08c53rEX6HRqRvMJ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\nRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DensityPlot('total_day_calls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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q165NrVq1Mv4RQmTNtWsaNm7UU7y4kxIl7j+MWnLVdEr98Ckp+YpzuMdb2VrQ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\nCWsvMdMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DensityPlot('total_day_charge')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pdwinc4x+76dYbeSPfRCN01m4hr3Xv/9PhFCbFHohqqnjxzXs3Knn6qt91K/v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\nRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DensityPlot('total_eve_minutes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HnTp1bhlTcHAgWq2m6JP1oFt13/gjycd3FWsuc/N/DIxz/IfBXc4QVlkNGIv0JUymQo5n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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DensityPlot('total_eve_calls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XZTQhnJJvUV+8eDHz58/HYrEwcuRIRo8ezebNm7FarcWRT4hSaf16LYqiok0b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\nZ7N161bGjBlDy5Yt8z1x586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MmzYNq9XKM888A0D16tWZOnWquyIKcZMtWzRs3KijXj07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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DensityPlot('total_eve_charge')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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07+9ErbgI27QIpyGQixG3+zqa3xrS5QQAievCAHA3aIgjuifa/fswrF7py2hC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\nRK5CYII=\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DensityPlot('total_night_minutes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qJfCtaSgBAeSP/r9rmlP31ODo2UB6tTlXJef+l0UzsD9nqEfsjvdQzlStxZGE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\nrkJggg==\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DensityPlot('total_night_calls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5y5X72+M0eqkdzyN0b3IOp6Jh2abKrgcMBjIfHoTGkom3zA0QHkyaACGKuKVL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\nIYQQNZQUAUIIIUQN9f9MQxJZwJ3hgQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DensityPlot('total_night_charge')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+AWQ+PBqN2YxpykRXRBOiRBRY1N9++20GDRrEZ599lmOediGE6506pbB6tfNa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\nSUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DensityPlot('total_intl_minutes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mM6tWrWLGjBksWLCgRPq2bdt46KGHyMy8cRJaQkICvXr14pNPPmH+/PnMnTsX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\ngg==\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DensityPlot('total_intl_calls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Np8MyfZrc9y2KlVw/EcaOHUvVqlUBZ2+7RYsWjBs3zuXBhPAEc+YYsdkUeve2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\nFkIIIQLM/wEqbCFm86f5PAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DensityPlot('total_intl_charge')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Rjnm3d+b96qtWPvjAgtls55ln6trlnEKIo0ur87zvv/9+Nm7cyJQpU9i4cWPD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\n8RZCCCFMzP8Bi/ovh8Zeo10AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DensityPlot('number_customer_service_calls')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation Matrix & Heatmap\n",
    "Let's see the correlations between different numerical features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>account_length</th>\n",
       "      <th>intl_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>number_customer_service_calls</th>\n",
       "      <th>churned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>account_length</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.014681</td>\n",
       "      <td>-0.006650</td>\n",
       "      <td>-0.014575</td>\n",
       "      <td>-0.001017</td>\n",
       "      <td>0.028240</td>\n",
       "      <td>-0.001019</td>\n",
       "      <td>-0.009591</td>\n",
       "      <td>0.009143</td>\n",
       "      <td>-0.009587</td>\n",
       "      <td>0.000668</td>\n",
       "      <td>-0.007825</td>\n",
       "      <td>0.000656</td>\n",
       "      <td>0.001291</td>\n",
       "      <td>0.014277</td>\n",
       "      <td>0.001292</td>\n",
       "      <td>-0.001445</td>\n",
       "      <td>0.021203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intl_plan</th>\n",
       "      <td>0.014681</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.009054</td>\n",
       "      <td>0.011947</td>\n",
       "      <td>0.028138</td>\n",
       "      <td>0.008102</td>\n",
       "      <td>0.028141</td>\n",
       "      <td>0.021235</td>\n",
       "      <td>0.002505</td>\n",
       "      <td>0.021234</td>\n",
       "      <td>-0.026508</td>\n",
       "      <td>0.005623</td>\n",
       "      <td>-0.026510</td>\n",
       "      <td>0.031799</td>\n",
       "      <td>0.004493</td>\n",
       "      <td>0.031751</td>\n",
       "      <td>-0.012974</td>\n",
       "      <td>0.259123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voice_mail_plan</th>\n",
       "      <td>-0.006650</td>\n",
       "      <td>0.009054</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.954508</td>\n",
       "      <td>0.002059</td>\n",
       "      <td>-0.001735</td>\n",
       "      <td>0.002056</td>\n",
       "      <td>0.023328</td>\n",
       "      <td>-0.005916</td>\n",
       "      <td>0.023332</td>\n",
       "      <td>0.005524</td>\n",
       "      <td>0.009176</td>\n",
       "      <td>0.005522</td>\n",
       "      <td>0.004116</td>\n",
       "      <td>-0.007525</td>\n",
       "      <td>0.004165</td>\n",
       "      <td>-0.014453</td>\n",
       "      <td>-0.110698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_vmail_messages</th>\n",
       "      <td>-0.014575</td>\n",
       "      <td>0.011947</td>\n",
       "      <td>0.954508</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005381</td>\n",
       "      <td>0.000883</td>\n",
       "      <td>0.005377</td>\n",
       "      <td>0.019490</td>\n",
       "      <td>-0.003954</td>\n",
       "      <td>0.019496</td>\n",
       "      <td>0.005541</td>\n",
       "      <td>0.002676</td>\n",
       "      <td>0.005535</td>\n",
       "      <td>0.002463</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.002505</td>\n",
       "      <td>-0.007086</td>\n",
       "      <td>-0.097633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_day_minutes</th>\n",
       "      <td>-0.001017</td>\n",
       "      <td>0.028138</td>\n",
       "      <td>0.002059</td>\n",
       "      <td>0.005381</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001935</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010750</td>\n",
       "      <td>0.008128</td>\n",
       "      <td>-0.010760</td>\n",
       "      <td>0.011799</td>\n",
       "      <td>0.004236</td>\n",
       "      <td>0.011783</td>\n",
       "      <td>-0.019486</td>\n",
       "      <td>-0.001303</td>\n",
       "      <td>-0.019415</td>\n",
       "      <td>0.002733</td>\n",
       "      <td>0.207705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_day_calls</th>\n",
       "      <td>0.028240</td>\n",
       "      <td>0.008102</td>\n",
       "      <td>-0.001735</td>\n",
       "      <td>0.000883</td>\n",
       "      <td>0.001935</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001936</td>\n",
       "      <td>-0.000699</td>\n",
       "      <td>0.003754</td>\n",
       "      <td>-0.000695</td>\n",
       "      <td>0.002804</td>\n",
       "      <td>-0.008308</td>\n",
       "      <td>0.002802</td>\n",
       "      <td>0.013097</td>\n",
       "      <td>0.010893</td>\n",
       "      <td>0.013161</td>\n",
       "      <td>-0.010739</td>\n",
       "      <td>0.016130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_day_charge</th>\n",
       "      <td>-0.001019</td>\n",
       "      <td>0.028141</td>\n",
       "      <td>0.002056</td>\n",
       "      <td>0.005377</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001936</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010747</td>\n",
       "      <td>0.008129</td>\n",
       "      <td>-0.010757</td>\n",
       "      <td>0.011801</td>\n",
       "      <td>0.004235</td>\n",
       "      <td>0.011785</td>\n",
       "      <td>-0.019490</td>\n",
       "      <td>-0.001307</td>\n",
       "      <td>-0.019419</td>\n",
       "      <td>0.002726</td>\n",
       "      <td>0.207700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_eve_minutes</th>\n",
       "      <td>-0.009591</td>\n",
       "      <td>0.021235</td>\n",
       "      <td>0.023328</td>\n",
       "      <td>0.019490</td>\n",
       "      <td>-0.010750</td>\n",
       "      <td>-0.000699</td>\n",
       "      <td>-0.010747</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002763</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.016639</td>\n",
       "      <td>0.013420</td>\n",
       "      <td>-0.016642</td>\n",
       "      <td>0.000137</td>\n",
       "      <td>0.008388</td>\n",
       "      <td>0.000159</td>\n",
       "      <td>-0.013823</td>\n",
       "      <td>0.089288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_eve_calls</th>\n",
       "      <td>0.009143</td>\n",
       "      <td>0.002505</td>\n",
       "      <td>-0.005916</td>\n",
       "      <td>-0.003954</td>\n",
       "      <td>0.008128</td>\n",
       "      <td>0.003754</td>\n",
       "      <td>0.008129</td>\n",
       "      <td>0.002763</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002778</td>\n",
       "      <td>0.001781</td>\n",
       "      <td>-0.013682</td>\n",
       "      <td>0.001799</td>\n",
       "      <td>-0.007458</td>\n",
       "      <td>0.005574</td>\n",
       "      <td>-0.007507</td>\n",
       "      <td>0.006235</td>\n",
       "      <td>-0.006284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_eve_charge</th>\n",
       "      <td>-0.009587</td>\n",
       "      <td>0.021234</td>\n",
       "      <td>0.023332</td>\n",
       "      <td>0.019496</td>\n",
       "      <td>-0.010760</td>\n",
       "      <td>-0.000695</td>\n",
       "      <td>-0.010757</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002778</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.016649</td>\n",
       "      <td>0.013422</td>\n",
       "      <td>-0.016652</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.008393</td>\n",
       "      <td>0.000155</td>\n",
       "      <td>-0.013836</td>\n",
       "      <td>0.089282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_night_minutes</th>\n",
       "      <td>0.000668</td>\n",
       "      <td>-0.026508</td>\n",
       "      <td>0.005524</td>\n",
       "      <td>0.005541</td>\n",
       "      <td>0.011799</td>\n",
       "      <td>0.002804</td>\n",
       "      <td>0.011801</td>\n",
       "      <td>-0.016639</td>\n",
       "      <td>0.001781</td>\n",
       "      <td>-0.016649</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.026972</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>-0.006721</td>\n",
       "      <td>-0.017214</td>\n",
       "      <td>-0.006655</td>\n",
       "      <td>-0.008533</td>\n",
       "      <td>0.045677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_night_calls</th>\n",
       "      <td>-0.007825</td>\n",
       "      <td>0.005623</td>\n",
       "      <td>0.009176</td>\n",
       "      <td>0.002676</td>\n",
       "      <td>0.004236</td>\n",
       "      <td>-0.008308</td>\n",
       "      <td>0.004235</td>\n",
       "      <td>0.013420</td>\n",
       "      <td>-0.013682</td>\n",
       "      <td>0.013422</td>\n",
       "      <td>0.026972</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.026949</td>\n",
       "      <td>0.000391</td>\n",
       "      <td>-0.000156</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>-0.008283</td>\n",
       "      <td>-0.006986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_night_charge</th>\n",
       "      <td>0.000656</td>\n",
       "      <td>-0.026510</td>\n",
       "      <td>0.005522</td>\n",
       "      <td>0.005535</td>\n",
       "      <td>0.011783</td>\n",
       "      <td>0.002802</td>\n",
       "      <td>0.011785</td>\n",
       "      <td>-0.016642</td>\n",
       "      <td>0.001799</td>\n",
       "      <td>-0.016652</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.026949</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.006717</td>\n",
       "      <td>-0.017182</td>\n",
       "      <td>-0.006650</td>\n",
       "      <td>-0.008530</td>\n",
       "      <td>0.045673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_intl_minutes</th>\n",
       "      <td>0.001291</td>\n",
       "      <td>0.031799</td>\n",
       "      <td>0.004116</td>\n",
       "      <td>0.002463</td>\n",
       "      <td>-0.019486</td>\n",
       "      <td>0.013097</td>\n",
       "      <td>-0.019490</td>\n",
       "      <td>0.000137</td>\n",
       "      <td>-0.007458</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>-0.006721</td>\n",
       "      <td>0.000391</td>\n",
       "      <td>-0.006717</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.016791</td>\n",
       "      <td>0.999993</td>\n",
       "      <td>-0.012122</td>\n",
       "      <td>0.063285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_intl_calls</th>\n",
       "      <td>0.014277</td>\n",
       "      <td>0.004493</td>\n",
       "      <td>-0.007525</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>-0.001303</td>\n",
       "      <td>0.010893</td>\n",
       "      <td>-0.001307</td>\n",
       "      <td>0.008388</td>\n",
       "      <td>0.005574</td>\n",
       "      <td>0.008393</td>\n",
       "      <td>-0.017214</td>\n",
       "      <td>-0.000156</td>\n",
       "      <td>-0.017182</td>\n",
       "      <td>0.016791</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.016900</td>\n",
       "      <td>-0.019147</td>\n",
       "      <td>-0.046893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_intl_charge</th>\n",
       "      <td>0.001292</td>\n",
       "      <td>0.031751</td>\n",
       "      <td>0.004165</td>\n",
       "      <td>0.002505</td>\n",
       "      <td>-0.019415</td>\n",
       "      <td>0.013161</td>\n",
       "      <td>-0.019419</td>\n",
       "      <td>0.000159</td>\n",
       "      <td>-0.007507</td>\n",
       "      <td>0.000155</td>\n",
       "      <td>-0.006655</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>-0.006650</td>\n",
       "      <td>0.999993</td>\n",
       "      <td>0.016900</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.012180</td>\n",
       "      <td>0.063275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_customer_service_calls</th>\n",
       "      <td>-0.001445</td>\n",
       "      <td>-0.012974</td>\n",
       "      <td>-0.014453</td>\n",
       "      <td>-0.007086</td>\n",
       "      <td>0.002733</td>\n",
       "      <td>-0.010739</td>\n",
       "      <td>0.002726</td>\n",
       "      <td>-0.013823</td>\n",
       "      <td>0.006235</td>\n",
       "      <td>-0.013836</td>\n",
       "      <td>-0.008533</td>\n",
       "      <td>-0.008283</td>\n",
       "      <td>-0.008530</td>\n",
       "      <td>-0.012122</td>\n",
       "      <td>-0.019147</td>\n",
       "      <td>-0.012180</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.212564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>churned</th>\n",
       "      <td>0.021203</td>\n",
       "      <td>0.259123</td>\n",
       "      <td>-0.110698</td>\n",
       "      <td>-0.097633</td>\n",
       "      <td>0.207705</td>\n",
       "      <td>0.016130</td>\n",
       "      <td>0.207700</td>\n",
       "      <td>0.089288</td>\n",
       "      <td>-0.006284</td>\n",
       "      <td>0.089282</td>\n",
       "      <td>0.045677</td>\n",
       "      <td>-0.006986</td>\n",
       "      <td>0.045673</td>\n",
       "      <td>0.063285</td>\n",
       "      <td>-0.046893</td>\n",
       "      <td>0.063275</td>\n",
       "      <td>0.212564</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               account_length  intl_plan  voice_mail_plan  \\\n",
       "account_length                       1.000000   0.014681        -0.006650   \n",
       "intl_plan                            0.014681   1.000000         0.009054   \n",
       "voice_mail_plan                     -0.006650   0.009054         1.000000   \n",
       "number_vmail_messages               -0.014575   0.011947         0.954508   \n",
       "total_day_minutes                   -0.001017   0.028138         0.002059   \n",
       "total_day_calls                      0.028240   0.008102        -0.001735   \n",
       "total_day_charge                    -0.001019   0.028141         0.002056   \n",
       "total_eve_minutes                   -0.009591   0.021235         0.023328   \n",
       "total_eve_calls                      0.009143   0.002505        -0.005916   \n",
       "total_eve_charge                    -0.009587   0.021234         0.023332   \n",
       "total_night_minutes                  0.000668  -0.026508         0.005524   \n",
       "total_night_calls                   -0.007825   0.005623         0.009176   \n",
       "total_night_charge                   0.000656  -0.026510         0.005522   \n",
       "total_intl_minutes                   0.001291   0.031799         0.004116   \n",
       "total_intl_calls                     0.014277   0.004493        -0.007525   \n",
       "total_intl_charge                    0.001292   0.031751         0.004165   \n",
       "number_customer_service_calls       -0.001445  -0.012974        -0.014453   \n",
       "churned                              0.021203   0.259123        -0.110698   \n",
       "\n",
       "                               number_vmail_messages  total_day_minutes  \\\n",
       "account_length                             -0.014575          -0.001017   \n",
       "intl_plan                                   0.011947           0.028138   \n",
       "voice_mail_plan                             0.954508           0.002059   \n",
       "number_vmail_messages                       1.000000           0.005381   \n",
       "total_day_minutes                           0.005381           1.000000   \n",
       "total_day_calls                             0.000883           0.001935   \n",
       "total_day_charge                            0.005377           1.000000   \n",
       "total_eve_minutes                           0.019490          -0.010750   \n",
       "total_eve_calls                            -0.003954           0.008128   \n",
       "total_eve_charge                            0.019496          -0.010760   \n",
       "total_night_minutes                         0.005541           0.011799   \n",
       "total_night_calls                           0.002676           0.004236   \n",
       "total_night_charge                          0.005535           0.011783   \n",
       "total_intl_minutes                          0.002463          -0.019486   \n",
       "total_intl_calls                            0.000124          -0.001303   \n",
       "total_intl_charge                           0.002505          -0.019415   \n",
       "number_customer_service_calls              -0.007086           0.002733   \n",
       "churned                                    -0.097633           0.207705   \n",
       "\n",
       "                               total_day_calls  total_day_charge  \\\n",
       "account_length                        0.028240         -0.001019   \n",
       "intl_plan                             0.008102          0.028141   \n",
       "voice_mail_plan                      -0.001735          0.002056   \n",
       "number_vmail_messages                 0.000883          0.005377   \n",
       "total_day_minutes                     0.001935          1.000000   \n",
       "total_day_calls                       1.000000          0.001936   \n",
       "total_day_charge                      0.001936          1.000000   \n",
       "total_eve_minutes                    -0.000699         -0.010747   \n",
       "total_eve_calls                       0.003754          0.008129   \n",
       "total_eve_charge                     -0.000695         -0.010757   \n",
       "total_night_minutes                   0.002804          0.011801   \n",
       "total_night_calls                    -0.008308          0.004235   \n",
       "total_night_charge                    0.002802          0.011785   \n",
       "total_intl_minutes                    0.013097         -0.019490   \n",
       "total_intl_calls                      0.010893         -0.001307   \n",
       "total_intl_charge                     0.013161         -0.019419   \n",
       "number_customer_service_calls        -0.010739          0.002726   \n",
       "churned                               0.016130          0.207700   \n",
       "\n",
       "                               total_eve_minutes  total_eve_calls  \\\n",
       "account_length                         -0.009591         0.009143   \n",
       "intl_plan                               0.021235         0.002505   \n",
       "voice_mail_plan                         0.023328        -0.005916   \n",
       "number_vmail_messages                   0.019490        -0.003954   \n",
       "total_day_minutes                      -0.010750         0.008128   \n",
       "total_day_calls                        -0.000699         0.003754   \n",
       "total_day_charge                       -0.010747         0.008129   \n",
       "total_eve_minutes                       1.000000         0.002763   \n",
       "total_eve_calls                         0.002763         1.000000   \n",
       "total_eve_charge                        1.000000         0.002778   \n",
       "total_night_minutes                    -0.016639         0.001781   \n",
       "total_night_calls                       0.013420        -0.013682   \n",
       "total_night_charge                     -0.016642         0.001799   \n",
       "total_intl_minutes                      0.000137        -0.007458   \n",
       "total_intl_calls                        0.008388         0.005574   \n",
       "total_intl_charge                       0.000159        -0.007507   \n",
       "number_customer_service_calls          -0.013823         0.006235   \n",
       "churned                                 0.089288        -0.006284   \n",
       "\n",
       "                               total_eve_charge  total_night_minutes  \\\n",
       "account_length                        -0.009587             0.000668   \n",
       "intl_plan                              0.021234            -0.026508   \n",
       "voice_mail_plan                        0.023332             0.005524   \n",
       "number_vmail_messages                  0.019496             0.005541   \n",
       "total_day_minutes                     -0.010760             0.011799   \n",
       "total_day_calls                       -0.000695             0.002804   \n",
       "total_day_charge                      -0.010757             0.011801   \n",
       "total_eve_minutes                      1.000000            -0.016639   \n",
       "total_eve_calls                        0.002778             0.001781   \n",
       "total_eve_charge                       1.000000            -0.016649   \n",
       "total_night_minutes                   -0.016649             1.000000   \n",
       "total_night_calls                      0.013422             0.026972   \n",
       "total_night_charge                    -0.016652             0.999999   \n",
       "total_intl_minutes                     0.000132            -0.006721   \n",
       "total_intl_calls                       0.008393            -0.017214   \n",
       "total_intl_charge                      0.000155            -0.006655   \n",
       "number_customer_service_calls         -0.013836            -0.008533   \n",
       "churned                                0.089282             0.045677   \n",
       "\n",
       "                               total_night_calls  total_night_charge  \\\n",
       "account_length                         -0.007825            0.000656   \n",
       "intl_plan                               0.005623           -0.026510   \n",
       "voice_mail_plan                         0.009176            0.005522   \n",
       "number_vmail_messages                   0.002676            0.005535   \n",
       "total_day_minutes                       0.004236            0.011783   \n",
       "total_day_calls                        -0.008308            0.002802   \n",
       "total_day_charge                        0.004235            0.011785   \n",
       "total_eve_minutes                       0.013420           -0.016642   \n",
       "total_eve_calls                        -0.013682            0.001799   \n",
       "total_eve_charge                        0.013422           -0.016652   \n",
       "total_night_minutes                     0.026972            0.999999   \n",
       "total_night_calls                       1.000000            0.026949   \n",
       "total_night_charge                      0.026949            1.000000   \n",
       "total_intl_minutes                      0.000391           -0.006717   \n",
       "total_intl_calls                       -0.000156           -0.017182   \n",
       "total_intl_charge                       0.000368           -0.006650   \n",
       "number_customer_service_calls          -0.008283           -0.008530   \n",
       "churned                                -0.006986            0.045673   \n",
       "\n",
       "                               total_intl_minutes  total_intl_calls  \\\n",
       "account_length                           0.001291          0.014277   \n",
       "intl_plan                                0.031799          0.004493   \n",
       "voice_mail_plan                          0.004116         -0.007525   \n",
       "number_vmail_messages                    0.002463          0.000124   \n",
       "total_day_minutes                       -0.019486         -0.001303   \n",
       "total_day_calls                          0.013097          0.010893   \n",
       "total_day_charge                        -0.019490         -0.001307   \n",
       "total_eve_minutes                        0.000137          0.008388   \n",
       "total_eve_calls                         -0.007458          0.005574   \n",
       "total_eve_charge                         0.000132          0.008393   \n",
       "total_night_minutes                     -0.006721         -0.017214   \n",
       "total_night_calls                        0.000391         -0.000156   \n",
       "total_night_charge                      -0.006717         -0.017182   \n",
       "total_intl_minutes                       1.000000          0.016791   \n",
       "total_intl_calls                         0.016791          1.000000   \n",
       "total_intl_charge                        0.999993          0.016900   \n",
       "number_customer_service_calls           -0.012122         -0.019147   \n",
       "churned                                  0.063285         -0.046893   \n",
       "\n",
       "                               total_intl_charge  \\\n",
       "account_length                          0.001292   \n",
       "intl_plan                               0.031751   \n",
       "voice_mail_plan                         0.004165   \n",
       "number_vmail_messages                   0.002505   \n",
       "total_day_minutes                      -0.019415   \n",
       "total_day_calls                         0.013161   \n",
       "total_day_charge                       -0.019419   \n",
       "total_eve_minutes                       0.000159   \n",
       "total_eve_calls                        -0.007507   \n",
       "total_eve_charge                        0.000155   \n",
       "total_night_minutes                    -0.006655   \n",
       "total_night_calls                       0.000368   \n",
       "total_night_charge                     -0.006650   \n",
       "total_intl_minutes                      0.999993   \n",
       "total_intl_calls                        0.016900   \n",
       "total_intl_charge                       1.000000   \n",
       "number_customer_service_calls          -0.012180   \n",
       "churned                                 0.063275   \n",
       "\n",
       "                               number_customer_service_calls   churned  \n",
       "account_length                                     -0.001445  0.021203  \n",
       "intl_plan                                          -0.012974  0.259123  \n",
       "voice_mail_plan                                    -0.014453 -0.110698  \n",
       "number_vmail_messages                              -0.007086 -0.097633  \n",
       "total_day_minutes                                   0.002733  0.207705  \n",
       "total_day_calls                                    -0.010739  0.016130  \n",
       "total_day_charge                                    0.002726  0.207700  \n",
       "total_eve_minutes                                  -0.013823  0.089288  \n",
       "total_eve_calls                                     0.006235 -0.006284  \n",
       "total_eve_charge                                   -0.013836  0.089282  \n",
       "total_night_minutes                                -0.008533  0.045677  \n",
       "total_night_calls                                  -0.008283 -0.006986  \n",
       "total_night_charge                                 -0.008530  0.045673  \n",
       "total_intl_minutes                                 -0.012122  0.063285  \n",
       "total_intl_calls                                   -0.019147 -0.046893  \n",
       "total_intl_charge                                  -0.012180  0.063275  \n",
       "number_customer_service_calls                       1.000000  0.212564  \n",
       "churned                                             0.212564  1.000000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = df.drop(['area_code'], axis=1).corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ERARvvfUWH3/8MQDLly/HxcWFqKgo0tLSqKqqIjIy0uy29OzZE0dHR9NYJE9PT4qLi1sV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\nTFVoBwcHUlJSUBSFCxcuGC+3sSn6krj3tt7e3rRu3ZqIiAg2bdpE9+7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FJp9NUpiXc/36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\nJSIiIqI+/B9kBTXF8obQTwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the minutes and charge of calls perfectly correlate with each other, which means they have repeated information. Keeping both the minutes and charge of calls would be redundant. We will therefore remove all the minutes. Also, ``voice_mail_plan`` and ``number_vmail_messages`` are highly correlated. We can remove ``voice_mail_plan``, since ``number_vmail_messages`` = 0 essentially means ``voice_mail_plan`` = 0. All the numbers of calls and ``account_length`` are irrelevant since they have little correlation with ``churned``."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2: Feature Preprocessing and Train/Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Get the labels\n",
    "y = df['churned'].values\n",
    "\n",
    "# Drop the useless columns: 1. irrelevant featues 2. featuers with repeated info 3. the target variable\n",
    "to_drop = ['state','account_length','area_code','phone_number','voice_mail_plan',\n",
    "           'total_day_calls','total_day_minutes','total_eve_calls','total_eve_minutes',\n",
    "           'total_night_calls','total_night_minutes','total_intl_calls',\n",
    "           'total_intl_minutes','churned']\n",
    "\n",
    "X = df.drop(to_drop, axis=1)\n",
    "\n",
    "# Train/Test split\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test =  train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>intl_plan</th>\n",
       "      <th>number_vmail_messages</th>\n",
       "      <th>total_day_charge</th>\n",
       "      <th>total_eve_charge</th>\n",
       "      <th>total_night_charge</th>\n",
       "      <th>total_intl_charge</th>\n",
       "      <th>number_customer_service_calls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>45.07</td>\n",
       "      <td>16.78</td>\n",
       "      <td>11.01</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>27.47</td>\n",
       "      <td>16.62</td>\n",
       "      <td>11.45</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41.38</td>\n",
       "      <td>10.30</td>\n",
       "      <td>7.32</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>50.90</td>\n",
       "      <td>5.26</td>\n",
       "      <td>8.86</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28.34</td>\n",
       "      <td>12.61</td>\n",
       "      <td>8.41</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>37.98</td>\n",
       "      <td>18.75</td>\n",
       "      <td>9.18</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>37.09</td>\n",
       "      <td>29.62</td>\n",
       "      <td>9.57</td>\n",
       "      <td>2.03</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26.69</td>\n",
       "      <td>8.76</td>\n",
       "      <td>9.53</td>\n",
       "      <td>1.92</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.37</td>\n",
       "      <td>29.89</td>\n",
       "      <td>9.71</td>\n",
       "      <td>2.35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>43.96</td>\n",
       "      <td>18.87</td>\n",
       "      <td>14.69</td>\n",
       "      <td>3.02</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   intl_plan  number_vmail_messages  total_day_charge  total_eve_charge  \\\n",
       "0          0                     25             45.07             16.78   \n",
       "1          0                     26             27.47             16.62   \n",
       "2          0                      0             41.38             10.30   \n",
       "3          1                      0             50.90              5.26   \n",
       "4          1                      0             28.34             12.61   \n",
       "5          1                      0             37.98             18.75   \n",
       "6          0                     24             37.09             29.62   \n",
       "7          1                      0             26.69              8.76   \n",
       "8          0                      0             31.37             29.89   \n",
       "9          1                     37             43.96             18.87   \n",
       "\n",
       "   total_night_charge  total_intl_charge  number_customer_service_calls  \n",
       "0               11.01               2.70                              1  \n",
       "1               11.45               3.70                              1  \n",
       "2                7.32               3.29                              0  \n",
       "3                8.86               1.78                              2  \n",
       "4                8.41               2.73                              3  \n",
       "5                9.18               1.70                              0  \n",
       "6                9.57               2.03                              3  \n",
       "7                9.53               1.92                              0  \n",
       "8                9.71               2.35                              1  \n",
       "9               14.69               3.02                              0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Scale the data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# fit the scaler to the training set and tranform both the training and test sets\n",
    "X_train = pd.DataFrame(scaler.fit_transform(X_train.values), columns=X.columns)\n",
    "X_test = pd.DataFrame(scaler.transform(X_test.values), columns=X.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 3: Model Training and Result Evaluation\n",
    "\n",
    "Let's assume that we want to provide the customers who will likely to stop the service with a special offer, in order to prevent their drops. We want to target most of these customers, but we also don't want to falsely target too many people who actually won't stop using the service. Thus, we want to consider both precision and recall of the result when choosing the optimal hyperparameters of our models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 3.1: Train the Models with Grid Search\n",
    "\n",
    "Here we use Area Under the Receiver Operating Characteristic Curve (ROC AUC) as the evaluation metric for grid search, in order to consider both true positive rates and false positive rates. We will try Logistic Regression, KNN, Random Forest, Support Vector Machine, and XGBoost Classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix, roc_curve, roc_auc_score, accuracy_score, precision_score, recall_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'penalty': ['l1', 'l2'], 'C': array([0.001  , 0.00107, ..., 0.93325, 1.     ])},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LogisticRegression()\n",
    "C_grid = 0.001*10**(np.arange(0,1.01,0.01)*3)\n",
    "parameters = {\n",
    "    'penalty': ['l1', 'l2'], \n",
    "    'C': C_grid\n",
    "}\n",
    "\n",
    "Grid_LR = GridSearchCV(lr, parameters, scoring='roc_auc')\n",
    "Grid_LR.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score:  0.8281228796364782\n",
      "Best parameters set: \n",
      "{'penalty': 'l2', 'C': 0.001}\n"
     ]
    }
   ],
   "source": [
    "lr = Grid_LR.best_estimator_\n",
    "print 'Best score: ', Grid_LR.best_score_\n",
    "print 'Best parameters set: \\n', Grid_LR.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_lr = lr.predict(X_test)\n",
    "y_prob_lr = lr.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform'),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'n_neighbors': [4, 8, 16], 'weights': ['uniform', 'distance']},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier()\n",
    "parameters = {\n",
    "    'n_neighbors':[4,8,16],\n",
    "    'weights': ['uniform','distance']\n",
    "}\n",
    "\n",
    "Grid_KNN = GridSearchCV(knn, parameters, scoring='roc_auc')\n",
    "Grid_KNN.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score:  0.9003129964579348\n",
      "Best parameters set: \n",
      "{'n_neighbors': 16, 'weights': 'distance'}\n"
     ]
    }
   ],
   "source": [
    "knn = Grid_KNN.best_estimator_\n",
    "print 'Best score: ', Grid_KNN.best_score_\n",
    "print 'Best parameters set: \\n', Grid_KNN.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_knn = knn.predict(X_test)\n",
    "y_prob_knn = knn.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=20, n_jobs=1,\n",
       "            oob_score=False, random_state=0, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'max_features': [2, 3, 4, 5, 6], 'min_samples_split': [4, 5, 6, 7, 8, 9], 'min_samples_leaf': [1, 2, 3, 4, 5]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=20, criterion=\"entropy\", random_state=0)\n",
    "parameters = {\n",
    "    \"max_features\": range(2,7),\n",
    "    \"min_samples_split\": range(4,10),\n",
    "    \"min_samples_leaf\": range(1,6),\n",
    "}\n",
    "\n",
    "Grid_RF = GridSearchCV(rf, parameters, scoring='roc_auc', n_jobs=-1)\n",
    "Grid_RF.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score:  0.9128126753010477\n",
      "Best parameters set: \n",
      "{'max_features': 6, 'min_samples_split': 7, 'min_samples_leaf': 3}\n"
     ]
    }
   ],
   "source": [
    "rf = Grid_RF.best_estimator_\n",
    "print 'Best score: ', Grid_RF.best_score_\n",
    "print 'Best parameters set: \\n', Grid_RF.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_rf = rf.predict(X_test)\n",
    "y_prob_rf = rf.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'C': array([  1.     ,   1.04713, ...,  95.49926, 100.     ])},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm = SVC(probability=True)\n",
    "C_grid = 10**(np.arange(0,1.01,0.01)*2)\n",
    "parameters = {'C': C_grid}\n",
    "\n",
    "Grid_SVM = GridSearchCV(svm, parameters, scoring='roc_auc', n_jobs=-1)\n",
    "Grid_SVM.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score:  0.9005154695158768\n",
      "Best parameters set: \n",
      "{'C': 2.6302679918953817}\n"
     ]
    }
   ],
   "source": [
    "svm = Grid_SVM.best_estimator_\n",
    "print 'Best score: ', Grid_SVM.best_score_\n",
    "print 'Best parameters set: \\n', Grid_SVM.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_svm = svm.predict(X_test)\n",
    "y_prob_svm = svm.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
       "       n_jobs=1, nthread=1, objective='binary:logistic', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=0, silent=True,\n",
       "       subsample=1),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'subsample': [0.25, 0.5, 1.0], 'learning_rate': [0.025, 0.05, 0.1], 'reg_lambda': [1, 2, 3], 'reg_alpha': [0, 1, 2], 'max_depth': [3, 4, 5]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb = XGBClassifier(random_state=0)\n",
    "parameters = {\n",
    "    'learning_rate': [0.025,0.05,0.1], \n",
    "    'max_depth': [3,4,5],\n",
    "    'subsample': [0.25,0.5,1.0],\n",
    "    'reg_lambda': [1,2,3], \n",
    "    'reg_alpha': [0,1,2]\n",
    "}\n",
    "\n",
    "warnings.filterwarnings('ignore', category=DeprecationWarning) \n",
    "Grid_XGB = GridSearchCV(xgb, parameters, scoring='roc_auc', n_jobs=-1)\n",
    "Grid_XGB.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score:  0.9115561648140047\n",
      "Best parameters set: \n",
      "{'reg_lambda': 2, 'learning_rate': 0.1, 'reg_alpha': 2, 'max_depth': 5, 'subsample': 1.0}\n"
     ]
    }
   ],
   "source": [
    "xgb = Grid_XGB.best_estimator_\n",
    "print 'Best score: ', Grid_XGB.best_score_\n",
    "print 'Best parameters set: \\n', Grid_XGB.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_xgb = xgb.predict(X_test)\n",
    "y_prob_xgb = xgb.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 3.2: Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def cal_evaluation(classifier, cm, auc):\n",
    "    tn = cm[0][0]\n",
    "    fp = cm[0][1]\n",
    "    fn = cm[1][0]\n",
    "    tp = cm[1][1]\n",
    "    accuracy  = (tp + tn) / (tp + fp + fn + tn + 0.0)\n",
    "    precision = tp / (tp + fp + 0.0)\n",
    "    recall = tp / (tp + fn + 0.0)\n",
    "    f1 = 2 * precision * recall / (precision + recall)\n",
    "    print classifier\n",
    "    print \"Accuracy is \" + str(accuracy)\n",
    "    print \"Precision is \" + str(precision)\n",
    "    print \"Recall is \" + str(recall)\n",
    "    print \"F1 score is \" + str(f1)\n",
    "    print \"ROC AUC is \" + str(auc)\n",
    "\n",
    "def draw_confusion_matrices(confusion_matricies):\n",
    "    class_names = ['Not','Churn']\n",
    "    for x in confusion_matrices:\n",
    "        classifier, cm, auc = x[0], x[1], x[2]\n",
    "        cal_evaluation(classifier, cm, auc)\n",
    "        fig = plt.figure()\n",
    "        ax = fig.add_subplot(111)\n",
    "        cax = ax.matshow(cm, interpolation='nearest',cmap=plt.get_cmap('Reds'))\n",
    "        plt.title('Confusion matrix for {}'.format(classifier))\n",
    "        fig.colorbar(cax)\n",
    "        ax.set_xticklabels([''] + class_names)\n",
    "        ax.set_yticklabels([''] + class_names)\n",
    "        plt.xlabel('Predicted')\n",
    "        plt.ylabel('True')\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logisitic Regression\n",
      "Accuracy is 0.8686666666666667\n",
      "Precision is 0.5757575757575758\n",
      "Recall is 0.09405940594059406\n",
      "F1 score is 0.16170212765957448\n",
      "ROC AUC is 0.8270225327617508\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Nearest-Neighbors\n",
      "Accuracy is 0.9173333333333333\n",
      "Precision is 0.90625\n",
      "Recall is 0.4306930693069307\n",
      "F1 score is 0.5838926174496644\n",
      "ROC AUC is 0.9084501670506034\n"
     ]
    },
    {
     "data": {
      "image/png": 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oKDszIqb2sfxaEfG5lVZBM2uqrjpuBdwfEZPy/d2AOcBcYJeI6IqIDYEuSQv7WLbfereL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\nADUzK8gB1MysoP8PfB162Nd+hQsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest\n",
      "Accuracy is 0.9453333333333334\n",
      "Precision is 0.910958904109589\n",
      "Recall is 0.6584158415841584\n",
      "F1 score is 0.764367816091954\n",
      "ROC AUC is 0.9019321423667791\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Machine\n",
      "Accuracy is 0.946\n",
      "Precision is 0.9290780141843972\n",
      "Recall is 0.6485148514851485\n",
      "F1 score is 0.7638483965014577\n",
      "ROC AUC is 0.9056221300096111\n"
     ]
    },
    {
     "data": {
      "image/png": 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CXC6pKuk+YFxEbDDIuoNyADWz0lVa+DccSRcBz9dvXlI1318GTAbWA56sW6dWPtC6g3If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\nrkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost Classifier\n",
      "Accuracy is 0.9486666666666667\n",
      "Precision is 0.9310344827586207\n",
      "Recall is 0.6683168316831684\n",
      "F1 score is 0.7780979827089337\n",
      "ROC AUC is 0.9143503333384185\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVAAAAEOCAYAAADfbE9eAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nK8gB1MysIAdQM7OCHEDNzAr6f/08et4B8/iXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "confusion_matrices = [\n",
    "    (\"Logisitic Regression\", confusion_matrix(y_test, y_pred_lr), roc_auc_score(y_test, y_prob_lr)),\n",
    "    (\"K-Nearest-Neighbors\", confusion_matrix(y_test, y_pred_knn), roc_auc_score(y_test, y_prob_knn)),\n",
    "    (\"Random Forest\", confusion_matrix(y_test, y_pred_rf), roc_auc_score(y_test, y_prob_rf)),\n",
    "    (\"Support Vector Machine\", confusion_matrix(y_test, y_pred_svm), roc_auc_score(y_test, y_prob_svm)),\n",
    "    (\"XGBoost Classifier\", confusion_matrix(y_test, y_pred_xgb), roc_auc_score(y_test, y_prob_xgb))\n",
    "]\n",
    "\n",
    "draw_confusion_matrices(confusion_matrices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random Forest, SVM, and XGBoost perform better than the other two models. Let's just average their predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stacked Model\n",
      "Accuracy is 0.952\n",
      "Precision is 0.9333333333333333\n",
      "Recall is 0.693069306930693\n",
      "F1 score is 0.7954545454545454\n",
      "ROC AUC is 0.9166081862423531\n"
     ]
    },
    {
     "data": {
      "image/png": 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g4AhgBTApIkbksdHdgcdIM2Vt13YzK6arVPujnbRrEJoJvE5ah/VDUja6AHgK+HfSQtkJ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\nCvpvhvvd+3EOouUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# average the predicted probabilities to make predictions\n",
    "y_prob = y_prob_rf/3.0 + y_prob_svm/3.0 + y_prob_xgb/3.0\n",
    "y_avg = y_prob >= 0.5\n",
    "\n",
    "confusion_matrices = [\n",
    "    (\"Stacked Model\", confusion_matrix(y_test, y_avg), roc_auc_score(y_test, y_prob))\n",
    "]\n",
    "\n",
    "draw_confusion_matrices(confusion_matrices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 3.3: Fine-tune the Threshold\n",
    "In the following, we will try to determine the optimal threshold for our stacked model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HxphvgSsAPQlWYcPrhUWLYmjduhoPPODi77+t92Nj0cZfhbVAEsBzQALWmcB9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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use the predicted probability to make the ROC curve\n",
    "fpr, tpr, thresh = roc_curve(y_test, y_prob, pos_label=1)\n",
    "roc_auc = roc_auc_score(y_test, y_prob)\n",
    "\n",
    "# These are the points at threshold = 0.1~0.5\n",
    "x1 = fpr[(thresh <= 0.5) & (thresh >= 0.1)] \n",
    "x2 = tpr[(thresh <= 0.5) & (thresh >= 0.1)]\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.plot(fpr, tpr, color='r', lw=2, label='ROC curve (area = {:.2f})'.format(roc_auc))\n",
    "plt.plot([0, 1], [0, 1], color='b', lw=2, linestyle='--')\n",
    "plt.plot(x1, x2, color='k', lw=3, label='threshold = 0.1 ~ 0.5')\n",
    "plt.xlim([-0.05, 1.05])\n",
    "plt.ylim([-0.05, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve for Stacked Model')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that true positive rate and recall are actually equal, so we can use a lower threshold (< 0.5) to acheive higher recall. Let's first see how different evaluation metrics change with the threshold we choose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_grid = np.zeros(thresh.shape)\n",
    "prec_grid = np.zeros(thresh.shape)\n",
    "recall_grid = np.zeros(thresh.shape)\n",
    "TP_grid = np.zeros(thresh.shape)\n",
    "FP_grid = np.zeros(thresh.shape)\n",
    "FN_grid = np.zeros(thresh.shape)\n",
    "\n",
    "for i in range(thresh.shape[0]):\n",
    "    cm = confusion_matrix(y_test, y_prob >= thresh[i])\n",
    "    acc_grid[i] = accuracy_score(y_test, y_prob >= thresh[i])\n",
    "    prec_grid[i] = precision_score(y_test, y_prob >= thresh[i])\n",
    "    recall_grid[i] = recall_score(y_test, y_prob >= thresh[i])\n",
    "    TP_grid[i] = cm[1][1]\n",
    "    FP_grid[i] = cm[0][1]\n",
    "    FN_grid[i] = cm[1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1inlqxNLk0iWJp582k5oq0b27g1dftfs6pJInSWA2k977aeJ2HyJuxz5S33gT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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.plot(thresh, acc_grid, color='k', lw=2, label='Accuracy')\n",
    "plt.plot(thresh, prec_grid, color='b', lw=2, label='Precision')\n",
    "plt.plot(thresh, recall_grid, color='r', lw=2, label='Recall')\n",
    "plt.xlim([-0.05, 1.05])\n",
    "plt.ylim([-0.05, 1.05])\n",
    "plt.xlabel('Threshold')\n",
    "plt.ylabel('Score')\n",
    "plt.legend(loc=\"lower center\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because there is a trade-off between precision and recall, we need to set a goal and fine-tune the threshold of our model. In this problem, our goal is to minimize the loss of our profitability. As mentioned earlier, we want to provide the customers who will likely to stop the service with a special offer, in order to prevent their drops. However, we would make additional cost if we falsely target a customer who actually won't stop using the service. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following, we use a very simple method to estimate the loss of our profitability:\n",
    "- If a customer stop the service, we lose 1 point\n",
    "- If a customer takes the special offer we provide and continue using the service, we lose 0.1 point\n",
    "- If we do nothing and a customer continue using the service, we lose nothing and get 0 point\n",
    "- We assume that half of the customers who were going to drop will take the offer, and all the the customers who were not going to drop will take the offer.\n",
    "\n",
    "\n",
    "Note that these are just my assumptions for a case study. In the reality, the company would have to carefully estimate these numbers.\n",
    "<br>\n",
    "<br>\n",
    "We can inspect how the loss changes with the threshold we use as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gpJMCNG4c72iUSjwOB/TpE+CTT/J58cUCRAJs2ODkttsacOqpacyZ4yao4zoShiaICPrk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\nYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "P = 0.5 # The chance a customer who was going to drop would take the special offer\n",
    "loss = TP_grid*P*(-0.1) + TP_grid*(1-P)*(-1) + FN_grid*(-1) + FP_grid*(-0.1)\n",
    "thresh_best = thresh[loss == max(loss)][0]\n",
    "    \n",
    "fig = plt.figure()\n",
    "plt.plot(thresh, loss, color='b', lw=2)\n",
    "plt.scatter([thresh_best], [max(loss)], color='r', s=50, label='Threshold = {:.2f}'.format(thresh_best))\n",
    "plt.xlim([-0.05, 1.05])\n",
    "plt.xlabel('Threshold')\n",
    "plt.ylabel('Loss of Profitability')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the loss is minimized at threshold = 0.28. Now let's take a look at the confusion matrix using this threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stacked Model\n",
      "Accuracy is 0.9386666666666666\n",
      "Precision is 0.7644230769230769\n",
      "Recall is 0.7871287128712872\n",
      "F1 score is 0.775609756097561\n",
      "ROC AUC is 0.9166081862423531\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_new = y_prob > thresh_best\n",
    "\n",
    "confusion_matrices = [\n",
    "    (\"Stacked Model\", confusion_matrix(y_test, y_pred_new), roc_auc_score(y_test, y_prob))\n",
    "]\n",
    "\n",
    "draw_confusion_matrices(confusion_matrices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 4: Feature Importance\n",
    "Lastly, let's check the relative importance of individual features based on the Random Forest and the XGBoost."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature importance ranking by Random Forest Model:\n",
      "total_day_charge: 0.3208\n",
      "total_eve_charge: 0.1841\n",
      "number_customer_service_calls: 0.129\n",
      "total_night_charge: 0.1112\n",
      "total_intl_charge: 0.101\n",
      "intl_plan: 0.0899\n",
      "number_vmail_messages: 0.064\n"
     ]
    }
   ],
   "source": [
    "importances = rf.feature_importances_\n",
    "\n",
    "# Print the feature ranking\n",
    "print(\"Feature importance ranking by Random Forest Model:\")\n",
    "for k,v in sorted(zip(map(lambda x: round(x, 4), importances), X.columns), reverse=True):\n",
    "    print v + \": \" + str(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature importance ranking by Random Forest Model:\n",
      "total_day_charge: 0.2506\n",
      "total_eve_charge: 0.2136\n",
      "total_night_charge: 0.1996\n",
      "total_intl_charge: 0.1326\n",
      "number_customer_service_calls: 0.0775\n",
      "number_vmail_messages: 0.072\n",
      "intl_plan: 0.054\n"
     ]
    }
   ],
   "source": [
    "importances = xgb.feature_importances_\n",
    "\n",
    "# Print the feature ranking\n",
    "print(\"Feature importance ranking by Random Forest Model:\")\n",
    "for k,v in sorted(zip(map(lambda x: round(x, 4), importances), X.columns), reverse=True):\n",
    "    print v + \": \" + str(k)"
   ]
  },
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   },
   "source": [
    "Overall, the more important features are the charge of the calls, which is as expected. This is because the customers who spent more money are more likely to drop and switch to other cell phone service providers. For the Random Forest, the number of customer service calls is also quite important and is ranked at the third. This makes sense since the users who called the customer service more tend to have more problems and are likely to drop as well."
   ]
  }
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