Churn Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2><center><b>Customer Churn</b></center></h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pandas\n",
      "  Downloading pandas-1.1.3-cp38-cp38-win_amd64.whl (8.9 MB)\n",
      "Collecting pytz>=2017.2\n",
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      "Installing collected packages: pytz, pandas\n",
      "Successfully installed pandas-1.1.3 pytz-2020.1\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
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     "output_type": "stream",
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      "Installing collected packages: kiwisolver, cycler, pillow, matplotlib\n",
      "Successfully installed cycler-0.10.0 kiwisolver-1.2.0 matplotlib-3.3.2 pillow-8.0.0\n"
     ]
    }
   ],
   "source": [
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting seaborn\n",
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      "Installing collected packages: scipy, seaborn\n",
      "Successfully installed scipy-1.5.3 seaborn-0.11.0\n"
     ]
    }
   ],
   "source": [
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: sklearn in c:\\users\\samee\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (0.0)\n",
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      "Requirement already satisfied: scipy>=0.19.1 in c:\\users\\samee\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from scikit-learn->sklearn) (1.5.3)\n",
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     ]
    }
   ],
   "source": [
    "!pip install sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from pylab import rcParams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "# Loading the CSV with pandas\n",
    "data = pd.read_csv('..data science projects/Customer Churn/Telecom_cust_churn.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3333, 21)\n"
     ]
    }
   ],
   "source": [
    "print (data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>account length</th>\n",
       "      <th>area code</th>\n",
       "      <th>phone number</th>\n",
       "      <th>international plan</th>\n",
       "      <th>voice mail plan</th>\n",
       "      <th>number vmail messages</th>\n",
       "      <th>total day minutes</th>\n",
       "      <th>total day calls</th>\n",
       "      <th>total day charge</th>\n",
       "      <th>...</th>\n",
       "      <th>total eve calls</th>\n",
       "      <th>total eve charge</th>\n",
       "      <th>total night minutes</th>\n",
       "      <th>total night calls</th>\n",
       "      <th>total night charge</th>\n",
       "      <th>total intl minutes</th>\n",
       "      <th>total intl calls</th>\n",
       "      <th>total intl charge</th>\n",
       "      <th>customer service calls</th>\n",
       "      <th>churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KS</td>\n",
       "      <td>128</td>\n",
       "      <td>415</td>\n",
       "      <td>382-4657</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>25</td>\n",
       "      <td>265.1</td>\n",
       "      <td>110</td>\n",
       "      <td>45.07</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>16.78</td>\n",
       "      <td>244.7</td>\n",
       "      <td>91</td>\n",
       "      <td>11.01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OH</td>\n",
       "      <td>107</td>\n",
       "      <td>415</td>\n",
       "      <td>371-7191</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>26</td>\n",
       "      <td>161.6</td>\n",
       "      <td>123</td>\n",
       "      <td>27.47</td>\n",
       "      <td>...</td>\n",
       "      <td>103</td>\n",
       "      <td>16.62</td>\n",
       "      <td>254.4</td>\n",
       "      <td>103</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NJ</td>\n",
       "      <td>137</td>\n",
       "      <td>415</td>\n",
       "      <td>358-1921</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>243.4</td>\n",
       "      <td>114</td>\n",
       "      <td>41.38</td>\n",
       "      <td>...</td>\n",
       "      <td>110</td>\n",
       "      <td>10.30</td>\n",
       "      <td>162.6</td>\n",
       "      <td>104</td>\n",
       "      <td>7.32</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  state  account length  area code phone number international plan  \\\n",
       "0    KS             128        415     382-4657                 no   \n",
       "1    OH             107        415     371-7191                 no   \n",
       "2    NJ             137        415     358-1921                 no   \n",
       "\n",
       "  voice mail plan  number vmail messages  total day minutes  total day calls  \\\n",
       "0             yes                     25              265.1              110   \n",
       "1             yes                     26              161.6              123   \n",
       "2              no                      0              243.4              114   \n",
       "\n",
       "   total day charge  ...  total eve calls  total eve charge  \\\n",
       "0             45.07  ...               99             16.78   \n",
       "1             27.47  ...              103             16.62   \n",
       "2             41.38  ...              110             10.30   \n",
       "\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",
       "\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",
       "\n",
       "   customer service calls  churn  \n",
       "0                       1  False  \n",
       "1                       1  False  \n",
       "2                       0  False  \n",
       "\n",
       "[3 rows x 21 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = data[\"churn\"].value_counts()\n",
    "#print (y)\n",
    "sns.barplot(y.index, y.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Churn Percentage = 14.491449144914492\n"
     ]
    }
   ],
   "source": [
    "y_True = data[\"churn\"][data[\"churn\"] == True]\n",
    "print (\"Churn Percentage = \"+str( (y_True.shape[0] / data[\"churn\"].shape[0]) * 100 ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data to plot\n",
    "sizes = data['churn'].value_counts(sort = True)\n",
    "colors = [\"blue\",\"orange\"] \n",
    "labels='no','yes'\n",
    "explode=(0.1, 0.1)\n",
    "rcParams['figure.figsize'] = 5,5\n",
    "# Plot\n",
    "plt.pie(sizes, explode=explode,labels=labels, colors=colors,\n",
    "        autopct='%1.1f%%', shadow=True, startangle=270,)\n",
    "plt.title('Percentage of Churn in Dataset')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <h2>Descriptive Analysis</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>area code</th>\n",
       "      <th>number vmail messages</th>\n",
       "      <th>total day minutes</th>\n",
       "      <th>total day calls</th>\n",
       "      <th>total day charge</th>\n",
       "      <th>total eve minutes</th>\n",
       "      <th>total eve calls</th>\n",
       "      <th>total eve charge</th>\n",
       "      <th>total night minutes</th>\n",
       "      <th>total night calls</th>\n",
       "      <th>total night charge</th>\n",
       "      <th>total intl minutes</th>\n",
       "      <th>total intl calls</th>\n",
       "      <th>total intl charge</th>\n",
       "      <th>customer service calls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>101.064806</td>\n",
       "      <td>437.182418</td>\n",
       "      <td>8.099010</td>\n",
       "      <td>179.775098</td>\n",
       "      <td>100.435644</td>\n",
       "      <td>30.562307</td>\n",
       "      <td>200.980348</td>\n",
       "      <td>100.114311</td>\n",
       "      <td>17.083540</td>\n",
       "      <td>200.872037</td>\n",
       "      <td>100.107711</td>\n",
       "      <td>9.039325</td>\n",
       "      <td>10.237294</td>\n",
       "      <td>4.479448</td>\n",
       "      <td>2.764581</td>\n",
       "      <td>1.562856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>39.822106</td>\n",
       "      <td>42.371290</td>\n",
       "      <td>13.688365</td>\n",
       "      <td>54.467389</td>\n",
       "      <td>20.069084</td>\n",
       "      <td>9.259435</td>\n",
       "      <td>50.713844</td>\n",
       "      <td>19.922625</td>\n",
       "      <td>4.310668</td>\n",
       "      <td>50.573847</td>\n",
       "      <td>19.568609</td>\n",
       "      <td>2.275873</td>\n",
       "      <td>2.791840</td>\n",
       "      <td>2.461214</td>\n",
       "      <td>0.753773</td>\n",
       "      <td>1.315491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.200000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>74.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>143.700000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>24.430000</td>\n",
       "      <td>166.600000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>14.160000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>7.520000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>101.000000</td>\n",
       "      <td>415.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>179.400000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>201.400000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>17.120000</td>\n",
       "      <td>201.200000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>9.050000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.780000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>127.000000</td>\n",
       "      <td>510.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>216.400000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>36.790000</td>\n",
       "      <td>235.300000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>235.300000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>10.590000</td>\n",
       "      <td>12.100000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.270000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>243.000000</td>\n",
       "      <td>510.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>350.800000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>59.640000</td>\n",
       "      <td>363.700000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>30.910000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>17.770000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       account length    area code  number vmail messages  total day minutes  \\\n",
       "count     3333.000000  3333.000000            3333.000000        3333.000000   \n",
       "mean       101.064806   437.182418               8.099010         179.775098   \n",
       "std         39.822106    42.371290              13.688365          54.467389   \n",
       "min          1.000000   408.000000               0.000000           0.000000   \n",
       "25%         74.000000   408.000000               0.000000         143.700000   \n",
       "50%        101.000000   415.000000               0.000000         179.400000   \n",
       "75%        127.000000   510.000000              20.000000         216.400000   \n",
       "max        243.000000   510.000000              51.000000         350.800000   \n",
       "\n",
       "       total day calls  total day charge  total eve minutes  total eve calls  \\\n",
       "count      3333.000000       3333.000000        3333.000000      3333.000000   \n",
       "mean        100.435644         30.562307         200.980348       100.114311   \n",
       "std          20.069084          9.259435          50.713844        19.922625   \n",
       "min           0.000000          0.000000           0.000000         0.000000   \n",
       "25%          87.000000         24.430000         166.600000        87.000000   \n",
       "50%         101.000000         30.500000         201.400000       100.000000   \n",
       "75%         114.000000         36.790000         235.300000       114.000000   \n",
       "max         165.000000         59.640000         363.700000       170.000000   \n",
       "\n",
       "       total eve charge  total night minutes  total night calls  \\\n",
       "count       3333.000000          3333.000000        3333.000000   \n",
       "mean          17.083540           200.872037         100.107711   \n",
       "std            4.310668            50.573847          19.568609   \n",
       "min            0.000000            23.200000          33.000000   \n",
       "25%           14.160000           167.000000          87.000000   \n",
       "50%           17.120000           201.200000         100.000000   \n",
       "75%           20.000000           235.300000         113.000000   \n",
       "max           30.910000           395.000000         175.000000   \n",
       "\n",
       "       total night charge  total intl minutes  total intl calls  \\\n",
       "count         3333.000000         3333.000000       3333.000000   \n",
       "mean             9.039325           10.237294          4.479448   \n",
       "std              2.275873            2.791840          2.461214   \n",
       "min              1.040000            0.000000          0.000000   \n",
       "25%              7.520000            8.500000          3.000000   \n",
       "50%              9.050000           10.300000          4.000000   \n",
       "75%             10.590000           12.100000          6.000000   \n",
       "max             17.770000           20.000000         20.000000   \n",
       "\n",
       "       total intl charge  customer service calls  \n",
       "count        3333.000000             3333.000000  \n",
       "mean            2.764581                1.562856  \n",
       "std             0.753773                1.315491  \n",
       "min             0.000000                0.000000  \n",
       "25%             2.300000                1.000000  \n",
       "50%             2.780000                1.000000  \n",
       "75%             3.270000                2.000000  \n",
       "max             5.400000                9.000000  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Churn by state</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='state'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby([\"state\", \"churn\"]).size().unstack().plot(kind='bar', stacked=True, figsize=(30,10)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Churn by Area code</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='area code'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby([\"area code\", \"churn\"]).size().unstack().plot(kind='bar', stacked=True, figsize=(5,5)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Data Preparation and Feature Engineering</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold, KFold\n",
    "from sklearn import tree\n",
    "from sklearn import svm\n",
    "from sklearn import ensemble\n",
    "from sklearn import neighbors\n",
    "from sklearn import linear_model\n",
    "from sklearn import metrics\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encoder = preprocessing.LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state                       int32\n",
      "account length              int64\n",
      "area code                   int64\n",
      "phone number               object\n",
      "international plan          int32\n",
      "voice mail plan             int32\n",
      "number vmail messages       int64\n",
      "total day minutes         float64\n",
      "total day calls             int64\n",
      "total day charge          float64\n",
      "total eve minutes         float64\n",
      "total eve calls             int64\n",
      "total eve charge          float64\n",
      "total night minutes       float64\n",
      "total night calls           int64\n",
      "total night charge        float64\n",
      "total intl minutes        float64\n",
      "total intl calls            int64\n",
      "total intl charge         float64\n",
      "customer service calls      int64\n",
      "churn                        bool\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "data['state'] = label_encoder.fit_transform(data['state'])\n",
    "data['international plan'] = label_encoder.fit_transform(data['international plan'])\n",
    "data['voice mail plan'] = label_encoder.fit_transform(data['voice mail plan'])\n",
    "\n",
    "#print (df['Voice mail plan'][:4])\n",
    "print (data.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3333, 21)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of       state  account length  area code phone number  international plan  \\\n",
       "0        16             128        415     382-4657                   0   \n",
       "1        35             107        415     371-7191                   0   \n",
       "2        31             137        415     358-1921                   0   \n",
       "3        35              84        408     375-9999                   1   \n",
       "4        36              75        415     330-6626                   1   \n",
       "...     ...             ...        ...          ...                 ...   \n",
       "3328      3             192        415     414-4276                   0   \n",
       "3329     49              68        415     370-3271                   0   \n",
       "3330     39              28        510     328-8230                   0   \n",
       "3331      6             184        510     364-6381                   1   \n",
       "3332     42              74        415     400-4344                   0   \n",
       "\n",
       "      voice mail plan  number vmail messages  total day minutes  \\\n",
       "0                   1                     25              265.1   \n",
       "1                   1                     26              161.6   \n",
       "2                   0                      0              243.4   \n",
       "3                   0                      0              299.4   \n",
       "4                   0                      0              166.7   \n",
       "...               ...                    ...                ...   \n",
       "3328                1                     36              156.2   \n",
       "3329                0                      0              231.1   \n",
       "3330                0                      0              180.8   \n",
       "3331                0                      0              213.8   \n",
       "3332                1                     25              234.4   \n",
       "\n",
       "      total day calls  total day charge  ...  total eve calls  \\\n",
       "0                 110             45.07  ...               99   \n",
       "1                 123             27.47  ...              103   \n",
       "2                 114             41.38  ...              110   \n",
       "3                  71             50.90  ...               88   \n",
       "4                 113             28.34  ...              122   \n",
       "...               ...               ...  ...              ...   \n",
       "3328               77             26.55  ...              126   \n",
       "3329               57             39.29  ...               55   \n",
       "3330              109             30.74  ...               58   \n",
       "3331              105             36.35  ...               84   \n",
       "3332              113             39.85  ...               82   \n",
       "\n",
       "      total eve charge  total night minutes  total night calls  \\\n",
       "0                16.78                244.7                 91   \n",
       "1                16.62                254.4                103   \n",
       "2                10.30                162.6                104   \n",
       "3                 5.26                196.9                 89   \n",
       "4                12.61                186.9                121   \n",
       "...                ...                  ...                ...   \n",
       "3328             18.32                279.1                 83   \n",
       "3329             13.04                191.3                123   \n",
       "3330             24.55                191.9                 91   \n",
       "3331             13.57                139.2                137   \n",
       "3332             22.60                241.4                 77   \n",
       "\n",
       "      total night charge  total intl minutes  total intl calls  \\\n",
       "0                  11.01                10.0                 3   \n",
       "1                  11.45                13.7                 3   \n",
       "2                   7.32                12.2                 5   \n",
       "3                   8.86                 6.6                 7   \n",
       "4                   8.41                10.1                 3   \n",
       "...                  ...                 ...               ...   \n",
       "3328               12.56                 9.9                 6   \n",
       "3329                8.61                 9.6                 4   \n",
       "3330                8.64                14.1                 6   \n",
       "3331                6.26                 5.0                10   \n",
       "3332               10.86                13.7                 4   \n",
       "\n",
       "      total intl charge  customer service calls  churn  \n",
       "0                  2.70                       1  False  \n",
       "1                  3.70                       1  False  \n",
       "2                  3.29                       0  False  \n",
       "3                  1.78                       2  False  \n",
       "4                  2.73                       3  False  \n",
       "...                 ...                     ...    ...  \n",
       "3328               2.67                       2  False  \n",
       "3329               2.59                       3  False  \n",
       "3330               3.81                       2  False  \n",
       "3331               1.35                       2  False  \n",
       "3332               3.70                       0  False  \n",
       "\n",
       "[3333 rows x 21 columns]>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4> Strip off reponse values</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3333"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data[['churn']].to_numpy()\n",
    "y.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4> Strip of redundant columns</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop([\"phone number\",\"churn\"], axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>account length</th>\n",
       "      <th>area code</th>\n",
       "      <th>international plan</th>\n",
       "      <th>voice mail plan</th>\n",
       "      <th>number vmail messages</th>\n",
       "      <th>total day minutes</th>\n",
       "      <th>total day calls</th>\n",
       "      <th>total day charge</th>\n",
       "      <th>total eve minutes</th>\n",
       "      <th>total eve calls</th>\n",
       "      <th>total eve charge</th>\n",
       "      <th>total night minutes</th>\n",
       "      <th>total night calls</th>\n",
       "      <th>total night charge</th>\n",
       "      <th>total intl minutes</th>\n",
       "      <th>total intl calls</th>\n",
       "      <th>total intl charge</th>\n",
       "      <th>customer service calls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16</td>\n",
       "      <td>128</td>\n",
       "      <td>415</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35</td>\n",
       "      <td>107</td>\n",
       "      <td>415</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31</td>\n",
       "      <td>137</td>\n",
       "      <td>415</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   state  account length  area code  international plan  voice mail plan  \\\n",
       "0     16             128        415                   0                1   \n",
       "1     35             107        415                   0                1   \n",
       "2     31             137        415                   0                0   \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",
       "\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",
       "\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",
       "\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",
       "\n",
       "   customer service calls  \n",
       "0                       1  \n",
       "1                       1  \n",
       "2                       0  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Build feature matrix</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 16.  , 128.  , 415.  , ...,   3.  ,   2.7 ,   1.  ],\n",
       "       [ 35.  , 107.  , 415.  , ...,   3.  ,   3.7 ,   1.  ],\n",
       "       [ 31.  , 137.  , 415.  , ...,   5.  ,   3.29,   0.  ],\n",
       "       ...,\n",
       "       [ 39.  ,  28.  , 510.  , ...,   6.  ,   3.81,   2.  ],\n",
       "       [  6.  , 184.  , 510.  , ...,  10.  ,   1.35,   2.  ],\n",
       "       [ 42.  ,  74.  , 415.  , ...,   4.  ,   3.7 ,   0.  ]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3333, 19)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4> feature scaling (standardizing values)</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = preprocessing.StandardScaler()\n",
    "X = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.6786493 ,  0.67648946, -0.52360328, ..., -0.60119509,\n",
       "        -0.0856905 , -0.42793202],\n",
       "       [ 0.6031696 ,  0.14906505, -0.52360328, ..., -0.60119509,\n",
       "         1.2411686 , -0.42793202],\n",
       "       [ 0.33331299,  0.9025285 , -0.52360328, ...,  0.21153386,\n",
       "         0.69715637, -1.1882185 ],\n",
       "       ...,\n",
       "       [ 0.87302621, -1.83505538,  1.71881732, ...,  0.61789834,\n",
       "         1.3871231 ,  0.33235445],\n",
       "       [-1.35329082,  2.08295458,  1.71881732, ...,  2.24335625,\n",
       "        -1.87695028,  0.33235445],\n",
       "       [ 1.07541867, -0.67974475, -0.52360328, ..., -0.19483061,\n",
       "         1.2411686 , -1.1882185 ]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Stratified Cross Validation - Since the Response values are not balanced</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stratified_cv(X, y, clf_class, shuffle=True, n_folds=10, **kwargs):\n",
    "    stratified_k_fold = StratifiedKFold(n_splits=n_folds, shuffle=shuffle)\n",
    "    y_pred = y.copy()\n",
    "    # ii -> train\n",
    "    # jj -> test indices\n",
    "    for ii, jj in stratified_k_fold.split(X, y): \n",
    "        X_train, X_test = X[ii], X[jj]\n",
    "        y_train = y[ii]\n",
    "        clf = clf_class(**kwargs)\n",
    "        clf.fit(X_train,y_train)\n",
    "        y_pred[jj] = clf.predict(X_test).reshape(X_test.shape[0],1)\n",
    "    return y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Build Models and Train</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosting Classifier:  0.95\n",
      "Support vector machine(SVM):   0.92\n",
      "Random Forest Classifier:      0.95\n",
      "K Nearest Neighbor Classifier: 0.89\n",
      "Logistic Regression:           0.86\n"
     ]
    }
   ],
   "source": [
    "print('Gradient Boosting Classifier:  {:.2f}'.format(metrics.accuracy_score(y, stratified_cv(X, y, ensemble.GradientBoostingClassifier))))\n",
    "print('Support vector machine(SVM):   {:.2f}'.format(metrics.accuracy_score(y, stratified_cv(X, y, svm.SVC))))\n",
    "print('Random Forest Classifier:      {:.2f}'.format(metrics.accuracy_score(y, stratified_cv(X, y, ensemble.RandomForestClassifier))))\n",
    "print('K Nearest Neighbor Classifier: {:.2f}'.format(metrics.accuracy_score(y, stratified_cv(X, y, neighbors.KNeighborsClassifier))))\n",
    "print('Logistic Regression:           {:.2f}'.format(metrics.accuracy_score(y, stratified_cv(X, y, linear_model.LogisticRegression))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Confusion Matrices for various models</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grad_ens_conf_matrix      = metrics.confusion_matrix(y, stratified_cv(X, y, ensemble.GradientBoostingClassifier))\n",
    "sns.heatmap(grad_ens_conf_matrix, annot=True,  fmt='');\n",
    "title = 'Gradient Boosting'\n",
    "plt.title(title);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "svm_svc_conf_matrix       = metrics.confusion_matrix(y, stratified_cv(X, y, svm.SVC))\n",
    "sns.heatmap(svm_svc_conf_matrix, annot=True,  fmt='');\n",
    "title = 'SVM'\n",
    "plt.title(title);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "random_forest_conf_matrix = metrics.confusion_matrix(y, stratified_cv(X, y, ensemble.RandomForestClassifier))\n",
    "sns.heatmap(random_forest_conf_matrix, annot=True,  fmt='');\n",
    "title = 'Random Forest'\n",
    "plt.title(title);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OoJNU4tBVUva8jk5S9ryOTlL2PEcnKXsOXSVlz8kISdlz6Copew5dJWXPoauk7Bl0krKXHLpKyp0VnaTsGXSSspfj5SU+YVhS9gw6SSVNUd3SnoiYFBGLImJ2RVvviJgSEXOLn72K9oiIyyKiISJmRcSuFfuMLrafGxGjV+UzGXSSSpqqXFbBdcDwldrGA1NTSgOBqcXvACOAgcUyFrgKmoMROAPYC9gTOGN5OLbFoJNUUqugSyk9ALyxUvMo4Pri9fXAoRXtN6Rm04GNI6IfcCAwJaX0RkrpTWAKHw/Pj3EyQlJJB09G9E0pLShevwr0LV73B16u2G5+0dZae5us6CSVVHuOLiLGRsTjFcvY1TluSilRo5y1opNUUu11dCmlicDE1dxtYUT0SyktKIami4r2RmBAxXZbFG2NwNCV2u9r7yBWdJJKUpVLlSYDy2dORwO3V7QfV8y+7g28VQxx7wEOiIhexSTEAUVbm6zoJJU01egsXUTcTHM11ici5tM8e3o+cGtEjAFeBI4qNr8TGAk0AEuB4wFSSm9ExNnAjGK7s1JKK09wfIxBJ6mkVreApZS+0sqqYS1sm4BxrbzPJGDS6hzboJNUkuMtYAadpBJv6peUPR+lLil7tZqMqCeDTlJJfjFn0ElaiefoJGUvx6Grd0ZIyp4VnaSS/Oo5g07SSjxHJyl7OZ6jM+gkleQXcwadpJU4dJWUvZRhTWfQSSqxopOUvRwnI7xguA3rrrcuv77neiZPu5k7H7yVk793IgBfHXMU9z72n8x97Ql69d54xfaHHDGC3913C3fc/yt+9ftJfOazA+vUcwH8+5XnMWfedB5+9Pcr2q697lLuf3gy9z88mZmzp3H/w5MB6NV7Y27//Y28tGAmP5vw43p1uVPo4EepdwgrujZ88P4HHHf4v7D03ffo0aMHt9xxLQ9MfZgnH/sz0/7wIDf9Z/l7QF5+qZFjRp3A228tYd9hgznnoh9y5PBV+iJx1cAvf3EbV//8Rq6aeOGKtjFfO3XF67N/Op6333oHgPf/+31+es6l7Ljj9uw4qGv/A5VjRWfQtWPpu+8B0GOdHvRYpwcpwTNPzWlx2z/NmLXi9czHn6Lv5pt1SB/VskcensGAT7f+lZ+HHjaSUQcfC8DSpe/x6CNPsM02W3ZU9zqtHM/RVT10jYjj12ZHOqtu3boxedovmf7sFB6+bzp/fnL2Ku335WMO5YGpf6xx71Stz++zB4sWLWbeX1+sd1c6nVTlf53ZmpyjO3Ot9aITa2pq4pD9/idDPjeCz+36jwz8zLbt7rPXPrvz5WNGceFZl3VAD1WNI448mNt+fUe9u9EpNVW5dGZtDl0jYlZrq4C+bew3FhgLsOmGn+bve/apuoOdxZK33+HRhx5n3y8MZu5zf211ux0GbcdPL/kRY47+Fv/15lsd2EOtqu7du3PwIQfwhSGH1bsrnVJnr86q0d45ur7AgcCbK7UH0Oq4rPIbuwduutsn9v9a70025sMPl7Hk7XdYr+d6DB66F1dfdn2r2/fr/w9ccd0EvjvuR7ww76UO7KlWx9D9BjP3L/N45ZVX692VTqmzV2fVaC/o7gA2TCnNXHlFRNxXiw51Jpv27cMFl59Jt27d6dYtuOv2e5k25UGOO+FoTjjpOPpstgm/u/8W7r/3YU7/9tmc9N0T2LjX33PmBeMBWLbsIw7f/9g6f4qu6+pJl7DPkD3ZZJNezH7uQc7/6b9x0w2/5rAjD+Y3//fjw9aZs6ex0UYbss6663DQwftzxKjjmTOnoQ49r6+m9ImtTVoVqcYf6pNc0XV1r//32/XugtbAG0vmVvV9XsdueXhVf2dvfPG2Tvv9YV5eIqkkx8rEoJNU4gXDkrLXFWddJXUxXXHWVVIX49BVUvYcukrKnkNXSdmr9bW19WDQSSrxHJ2k7Dl0lZQ9JyMkZc+hq6TsORkhKXueo5OUvRzP0fm9rpJKmkhVLasiIl6IiKciYmZEPF609Y6IKRExt/jZq2iPiLgsIhoiYlZE7FrtZzLoJHW0/VJKO6eUdi9+Hw9MTSkNBKYWvwOMAAYWy1jgqmoPaNBJKkkpVbWsgVHA8i9juR44tKL9htRsOrBxRPSr5gAGnaSSWg5daX6A8R8i4oni2wIB+qaUFhSvX+Vv3zDYH3i5Yt/5RdtqczJCUkm1kxGVX3NamFh8I2Clf04pNUbEZsCUiHiudOyUUkSs9dkQg05SSbXfAlb5NadtbNNY/FwUEb8F9gQWRkS/lNKCYmi6qNi8ERhQsfsWRdtqc+gqqSRVubQnIjaIiI2WvwYOAGYDk4HRxWajgduL15OB44rZ172BtyqGuKvFik5SSQ1vAesL/DYioDl7fplSujsiZgC3RsQY4EXgqGL7O4GRQAOwFDi+2gMbdJJKahV0KaV5wE4ttL8ODGuhPQHj1saxDTpJJd7rKil7Pr1EUvZyvNfVoJNU4tBVUvYcukrKnhWdpOxZ0UnKnpMRkrJX7b2unZn3ukrKnhWdpBKHrpKyl+PQ1aCTVGJFJyl7VnSSsmdFJyl7VnSSsmdFJyl7KTXVuwtrnUEnqcR7XSVlz6eXSMqeFZ2k7FnRScqel5dIyp6Xl0jKnkNXSdlzMkJS9nKs6HzCsKTsWdFJKnHWVVL2chy6GnSSSpyMkJQ9KzpJ2fMcnaTseWeEpOxZ0UnKnufoJGXPoauk7FnRScqeQScpe/nFHESO6d2RImJsSmlivfuh6vjn1zX49JI1N7beHdAa8c+vCzDoJGXPoJOUPYNuzXl+55PNP78uwMkISdmzopOUPYNuDUTE8IiYExENETG+3v3RqouISRGxKCJm17svqj2DrkoR0R24AhgBDAK+EhGD6tsrrYbrgOH17oQ6hkFXvT2BhpTSvJTSB8AtwKg690mrKKX0APBGvfuhjmHQVa8/8HLF7/OLNkmdjEEnKXsGXfUagQEVv29RtEnqZAy66s0ABkbE1hGxLnA0MLnOfZLUAoOuSimlZcBJwD3As8CtKaWn69srraqIuBl4BNghIuZHxJh690m1450RkrJnRScpewadpOwZdJKyZ9BJyp5BJyl7Bp2k7Bl0krJn0EnK3v8H/dAnUdoUZC0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "k_neighbors_conf_matrix   = metrics.confusion_matrix(y, stratified_cv(X, y, neighbors.KNeighborsClassifier))\n",
    "sns.heatmap(k_neighbors_conf_matrix, annot=True,  fmt='');\n",
    "title = 'KNN'\n",
    "plt.title(title);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAToAAAE/CAYAAADWjw/vAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAb+klEQVR4nO3deZxWdd3/8debRURABkQQEFfQwkzc0FRyIRW5VTLNMEVCCyu8c7tz3yIqK1Pz5xYahmTww6VbNDfELSoUXFLRTMIFJhYREBBUhvncf1wHug7ODHDBcA1f3s8e58G5Pmf7nhnn0+d7vuecSxGBmVnKGpW7AWZm9c2JzsyS50RnZslzojOz5DnRmVnynOjMLHlOdA2MpNskXVHCdjtIWiKpcX20q6GS9IikgeVuhzVs8n10pZP0DvDtiHhiUz22pG8BvwWWAdXA28BlEfHQ+rbRrKFwRWcAf4uIlkAFcAswRlLFhj7I5lZtWsPhRFcPJDWTdIOkf2fTDZKaFS2/UNKsbNm3JYWkrtmy30kals23k/SQpIWS5kv6s6RGkkYBOwAPZt3VCyXtlO2nSbZtW0l3ZsdYIOl/19TuiKgGRgEtgG5F53KtpPckzcm61s3X4VxulfSwpI+AwyV1knSfpPclvS3pB0X76ilpiqRF2bGuy+JbSvq9pA+yn8VkSR2yZU9L+nY230jS5ZLelTRX0l2SWmfLVv58BmbnMk/SZSX/km2T4kRXPy4DDgR6AHsBPYHLAST1Ac4HvgJ0BQ6rYz8XADOBbYEOwKVARMQA4D3guIhoGRG/qGHbUcBWwB5Ae+D6NTU6q7gGAcuBd7PwNcBu2bl0BToDV67DuXwT+AnQCvgr8CDw92w/vYFzJR2drftr4NcRsTWwKzA2iw8EWgNdgG2A71Loaq/uW9l0OLAL0BK4abV1DgF2z459paTP1/4TsVQ40dWPU4GhETE3It4HfgQMyJadDNwZEVMjYilwdR37WQ50BHaMiOUR8edYi4uqkjoCxwDfjYgF2bbP1LHJgZIWAh8D1wKnRcRcSQIGA+dFxPyIWAz8FOi/DufyQET8JasW9wS2jYihEfFpREwHbi/a33Kgq6R2EbEkIiYVxbcBukbEioh4ISIW1XCsU4HrImJ6RCwBLgH6r6xyMz+KiGUR8XcKCXevOn4ulggnuvrRif9URGTznYqWzShaVjy/ul8C04DHJU2XdPFaHr8LMD8iFqzl+pMiogJoA4wDemXxbSlUhS9kXcaFwKNZHNbuXIpjOwKdVu4r29+lFKpVgDMpVI//yLqnx2bxUcBjFK4d/lvSLyQ1reFYNf3cmxTtH2B20fxSClWfJc6Jrn78m8If9Uo7ZDGAWcD2Rcu61LaTiFgcERdExC7A8cD5knqvXFzH8WcAbdd1QCGrgr4HDJC0NzCPQhdxj4ioyKbW2cDF2p5LcTtnAG8X7asiIlpFRN/s+G9FxCkUuto/B+6V1CKrSH8UEd2Bg4BjgdNrOFZNP/cqYM66/BwsPU50669pdrF85dQEGA1cLmlbSe0oXNP6fbb+WGCQpM9L2gqo9Z45ScdK6pp1IT8EVlC4BQQKf7y71LRdRMwCHgFukdRGUlNJX16bk4mI+cAdwJVZd/N24HpJ7bM2dS66prbW55J5Hlgs6SJJzSU1lvQFSftn+z5N0rbZcRdm21RLOlzSntk1xEUUurLVNex/NHCepJ0ltaTQzf7/EVG1Nudu6XKiW38PU6h6Vk5XA8OAKcArwKvAi1mMiHgEuBF4ikK3dOV1qE9q2Hc34AlgCfA34JaIeCpb9jMKyXShpP+pYdsBFBLCP4C5wLnrcE43AH0lfRG4aGU7JS3K2rN7CedCRKygUI31oHC/3jwKSbV1tkofYKqkJRQGJvpHxDJgO+BeCknuDeAZCt3Z1Y3I4s9m+/8Y+O91OG9LlG8YLrNs1O81oNmmXnmkdC6WFld0ZSDphOz+tDYUrkU9uKkmhpTOxdLlRFceZ1HoTv6LwnW375W3OeslpXOxRLnrambJc0VnZslzojOz5DVZ8yrrZ/m86e4bb6Kad+q15pWswar6tFKlbFfq32zTdruUdLyNwRWdmSWv3is6M9vEVK8odws2OCc6M8uLmp6u27Q50ZlZXrUTnZklLlzRmVnyXNGZWfJc0ZlZ8jzqambJc0VnZsnzNTozS51HXc0sfa7ozCx5rujMLHkedTWz5LmiM7Pk+RqdmSUvwYrOL940s+S5ojOzPHddzSx1ER51NbPUJXiNzonOzPIS7Lp6MMLM8qK6tGkNJHWR9JSk1yVNlXROFr9aUqWkl7Opb9E2l0iaJulNSUcXxftksWmSLl7TsV3RmVle/T0ZUQVcEBEvSmoFvCBpfLbs+oi4tnhlSd2B/sAeQCfgCUm7ZYtvBo4EZgKTJY2LiNdrO7ATnZnl1dM1uoiYBczK5hdLegPoXMcm/YAxEfEJ8LakaUDPbNm0iJgOIGlMtm6tic5dVzPLq64ubVoHknYC9gaey0JnS3pF0ghJbbJYZ2BG0WYzs1ht8Vo50ZlZXonX6CQNljSlaBpc0+4ltQTuA86NiEXArcCuQA8KFd+vNvQpuetqZnkljrpGxHBgeF3rSGpKIcndHRH3Z9vNKVp+O/BQ9rES6FK0+fZZjDriNXJFZ2Z59dR1lSTgt8AbEXFdUbxj0WonAK9l8+OA/pKaSdoZ6AY8D0wGuknaWdIWFAYsxtV1bFd0ZpZTj09GHAwMAF6V9HIWuxQ4RVIPIIB3gLMK7YipksZSGGSoAoZE1jhJZwOPAY2BERExta4DKyI29MnkLJ83vX4PYPWmeade5W6CrYeqTytVynbLnh5R0t9s88POKOl4G4MrOjPL8yNgZpa8BB8Bc6Izs7wEKzqPuppZ8lzRmVmeu65mlrwEu65OdGaW54rOzJLnRGdmyXPX1cyS54rOzJLnis7MkueKzsyS54rOzJLnis7MkudEZ2bJq+d3VJaDE52Z5bmiM7PkOdGZWfI86mpmyUuwovOLN80sea7ozCzPo65mlrwEu65OdGaW50RnZsnzqKuZpS6qfY3OzFLnrquZJc9dVzNLnruuZpY8d13NLHlOdOmbNed9Lv3xtXywYAFCnNTvGAac/FUuuOJnvPPeTAAWL1lCq5YtuW/kzQC8Oe1thv7iRpZ8tJRGjRox5o5f06zZFixfvpyfXHcLk196lUYSPxg8kCMPP6Scp7dZO+cH3+GMM04hInjttX9w5rfP55NPPgHg+uuGMuhb/alou1uZW9kA+MmI9DVp3Jgf/vd36L57Vz76aCknn/kDDtp/b37140tWrfPL/3c7LVtsBUBV1QouHvoLfnbFD/lct11Y+OEimjRpDMBvRo6hbZsK/jTmDqqrq/lw0eKynJNBp07bcfaQM9hzr8P5+OOPGf2H2/jGyf24a9RY9t3ni7RpU1HuJjYcCVZ0a3yoX9LnJF0k6cZsukjS5zdG48ph23Zt6b57VwBatNiKXXbswpz3P1i1PCJ49Mln6XvkYQD89fkX2G3Xnflct10AqGi9NY0bFxLdH//0ON8e8A0AGjVqRJuK1hvxTGx1TZo0oXnzLWncuDFbNW/OrFmzadSoET+/5gouvmRYuZvXcFRHaVMDVmeik3QRMAYQ8Hw2CRgt6eL6b155Vc6awxtv/Ysv7rH7qtgLf3+Nbdq0YccunQF4d0Ylkhh83mV8fdDZjLj7HgAWLV4CwE2338XXB53N+Zf/hHnzF2z8kzAA/v3v2Vx3/W28/a/nmfneS3y4aBHjn3iWId8fxIMPPc7s2XPL3cSGI6pLmxqwNVV0ZwL7R8Q1EfH7bLoG6JktS9bSpcs477JhXPSDs2jZosWq+MPjn6bvkYeu+ly1YgUvvTKVn191IXfdei0Tnvkrk6a8xIoVK5gzdx499vw899x5E3t94fNce9Md5TgVAyoqWnP8cUfTdbcD6bLjPrRosRWnnXYSJ514LDfdPKLczWtYNreKDqgGOtUQ75gtq5GkwZKmSJpyx12j16d9ZbG8qopzLxvGfx11OEcedvCqeFXVCp545q/06f3lVbEO7dux715foE1Fa5pvuSW9vrQ/r7/5Lypab03zLZvxlUML2x91eC/eeHPaRj8XK+jduxdvv/Me8+bNp6qqij/+7yNcdcUF7LrrTrz5xl+Y9s9JbLVVc/7x+sRyN7Xsorq6pKkhW9NgxLnABElvATOy2A5AV+Ds2jaKiOHAcIDl86Y37FS/mojgyp/dwC47dmFg/6/llk2a8hK77Lg927XfdlXs4J77cufd97Ls449p2qQpU15+lQHfOAFJHHrwAUx+6RUO2LcHz015mV133mFjn45lZrxXyQEH7EPz5luybNnHHHH4Idzw6+HcfMudq9ZZOP+ffK67R8VTVGeii4hHJe1GoavaOQtXApMjYkV9N64cXnplKg8+OoFuu+7EiQOHAHDOWQP58kE9eeSJZzjmK4fl1m+9dStO7/81+p95DpLo9aX9OfSgngCc//0zuGTotVzz69/QtqI1wy49f2OfjmWen/wS99//JyY//xhVVVW8/PJUbr/j7nI3q2Fq4N3QUijq+Z6ZTa2is/9o3qlXuZtg66Hq00qVst1Hw04r6W+2xeW/L+l4G4PvozOzvAQrOic6M8tr4AMLpfC3gJlZXj3dXiKpi6SnJL0uaaqkc7J4W0njJb2V/dsmiyt7SGGapFck7VO0r4HZ+m9JGrimYzvRmVle/d0wXAVcEBHdgQOBIZK6AxcDEyKiGzAh+wxwDNAtmwYDt0IhMQJXAQdQGCi9amVyrI0TnZnl1VNFFxGzIuLFbH4x8AaFuzn6ASOz1UYCX83m+wF3RcEkoEJSR+BoYHxEzI+IBcB4oE9dx/Y1OjPL2Rg3/0raCdgbeA7oEBGzskWzgQ7ZfGf+c/8uwMwsVlu8Vq7ozCyvxIqu+ImobBpc0+4ltQTuA86NiEXFy6Jwv9sGH/Z1RWdmeSXeXlL8RFRtJDWlkOTujoj7s/AcSR0jYlbWNV35hoVKoEvR5ttnsUrgsNXiT9d1XFd0ZpZXT4MRkgT8FngjIq4rWjQOWDlyOhB4oCh+ejb6eiDwYdbFfQw4SlKbbBDiqCxWK1d0ZpZXfzcMHwwMAF6V9HIWuxS4Bhgr6UzgXeDkbNnDQF9gGrAUGAQQEfMl/RiYnK03NCLm13VgJzozy6mvL7COiIkU3mdZk941rB/AkFr2NQJY6/drOdGZWZ4fATOz5CX4CJgTnZnluaIzs+QlmOh8e4mZJc8VnZnl1PfLeMvBic7M8hLsujrRmVmeE52Zpa6+bhguJyc6M8tzojOz5KV3v7ATnZnluetqZulzojOz5Lnramapc9fVzNLnis7MUueKzszS54rOzFK3Ft9zs8lxojOzPCc6M0tdihWdX7xpZslzRWdmeQlWdE50ZpaTYtfVic7McpzozCx5TnRmlr5QuVuwwTnRmVmOKzozS15Uu6Izs8S5ojOz5IWv0ZlZ6lzRmVnyfI3OzJIX6b1304nOzPJc0ZlZ8pzozCx57rqaWfJSrOj84k0zS54rOjPL8Q3DZpa8FG8YdtfVzHKqQyVNayJphKS5kl4ril0tqVLSy9nUt2jZJZKmSXpT0tFF8T5ZbJqki9fmnJzozCwnQiVNa+F3QJ8a4tdHRI9sehhAUnegP7BHts0tkhpLagzcDBwDdAdOydatk7uuZpZTX6OuEfGspJ3WcvV+wJiI+AR4W9I0oGe2bFpETAeQNCZb9/W6duaKzsxyIkqb1sPZkl7JurZtslhnYEbROjOzWG3xOjnRmVlOVKukSdJgSVOKpsFrcbhbgV2BHsAs4Ff1cU7uuppZztoMLNQkIoYDw9dxmzkr5yXdDjyUfawEuhStun0Wo454rVzRmVlOPQ5GfIakjkUfTwBWjsiOA/pLaiZpZ6Ab8DwwGegmaWdJW1AYsBi3puO4ojOznPp61lXSaOAwoJ2kmcBVwGGSegABvAOcVWhDTJU0lsIgQxUwJCJWZPs5G3gMaAyMiIipazx21PMTvMvnTU/wEeHNQ/NOvcrdBFsPVZ9WllRmvbzj8SX9zfZ4d1yDfaTCFZ2Z5fgRMDNLnl/TVIIee5xS34ewepLe/6/b2ih11LUhc0VnZjnuuppZ8lKs6HwfnZklzxWdmeUkOBbhRGdmeSl2XZ3ozCzHgxFmlrwE36TuRGdmeZHgHZROdGaWU53gaIQTnZnlVLuiM7PUuetqZsnzYISZJc8VnZklzxWdmSXPic7Mkueuq5klrzq9POdEZ2Z5vo/OzJKX4IMRfvGmmaXPFZ2Z5XjU1cySVy1fozOzxKV4jc6Jzsxy3HU1s+T5PjozS57vozOz5PkanZklz11XM0ueByPMLHnuuppZ8tx1NbPkuetqZslzojOz5IW7rmaWOld0ZpY8JzozS16Kt5f4DcNmljxXdGaWk+J9dK7ozCynusRpTSSNkDRX0mtFsbaSxkt6K/u3TRaXpBslTZP0iqR9irYZmK3/lqSBa3NOTnRmllNfiQ74HdBntdjFwISI6AZMyD4DHAN0y6bBwK1QSIzAVcABQE/gqpXJsS5OdGaWEyVOa9xvxLPA/NXC/YCR2fxI4KtF8buiYBJQIakjcDQwPiLmR8QCYDyfTZ6f4Wt0Zpazka/RdYiIWdn8bKBDNt8ZmFG03swsVlu8Tq7ozCyn1K6rpMGSphRNg9fluBGxtsXhOnNFZ2Y5pWaaiBgODF/HzeZI6hgRs7Ku6dwsXgl0KVpv+yxWCRy2WvzpNR3EFZ2Z5VQTJU0lGgesHDkdCDxQFD89G309EPgw6+I+BhwlqU02CHFUFquTKzozy6mvR8AkjaZQjbWTNJPC6Ok1wFhJZwLvAidnqz8M9AWmAUuBQQARMV/Sj4HJ2XpDI2L1AY7PcKIzs5z6egQsIk6pZVHvGtYNYEgt+xkBjFiXYzvRmVmOH+o3s+Sl+AiYE52Z5azHwEKD5URnZjnppTknOjNbja/RmVnyUuy6+oZhM0ueKzozy0mvnnOiM7PV+BqdmSUvxWt0TnRmlpNemnOiM7PVuOtqZsmLBGs6Jzozy3FFZ2bJS3EwwjcM12GLZlsw5tER3P/k73ngmdEM+eF3ADig137cM34k900Yxahxw9lhp+0BaLpFU64dPoxHJt3L6Ed+S6cuHcvZ/M3e7cN/ReXMv/PSSxNWxdq0qeCRh0fz+tSJPPLwaCoqWgNw3HFH8eIL45ky+XEm/e1hDj5o/3I1u+zq61vAysmJrg6ffvIpZ3xtCF874jRO7H0ahxxxIF/c9wtc+fOLuOj7V3Fi7wH86f7HOOu8QQCc+M3jWbRwMccceBJ3/WYM519R43sDbSMZeddYjj321FzswguH8ORTE+m+xyE8+dRELryw8Dt68smJ7LPvkey3/1F8Z/AF3Paba8vR5AZhI79KfaNwoluDpUuXAdCkaROaNGlCRBARtGjVAoCWW7dk7px5ABzR58s8MPZPADz+4JMceMjmWxU0BBMnPsf8BQtzseOOO5pRo+4BYNSoezj++MJXgn700dJV67TYaisKL7jdPNXjF1iXTcnX6CQNiog7N2RjGqJGjRpxz/iR7LDz9owecS+vvjiVK8//KbfdfT0ff/wJHy3+iFP6nglA+47bMruy8CVGK1asYPHiJVS0bc3C+R+W8xSsSIf27Zg9u/A7mj17Lh3at1u1rF+/Pgwbdgntt92Gfv0G1raL5KU46ro+Fd2PNlgrGrDq6mpO7D2AI3ocx5777EHXz+3C6Wf157unnkfvvY/jj2Me4sKh55S7mVai4srtgQceZc89D+XEk87k6qt/WMZWlVeKFV2diU7SK7VMr/Kfb9SuabtVX2S7YNnc2lbbpCxetITnJ75AryMOYvc9uvHqi1MBePSB8ey93xcBmDvrfbbr3B6Axo0b06pVS1dzDcycufPYbrvC72i77doz9/0PPrPOxInPsfPOO7DNNm02dvMahCjxfw3Zmiq6DsDpwHE1TJ/9LyQTEcMjYr+I2K9N8/Ybqq0bXZttKmi1dUsAmm3ZjC8d2pPpb71Nq1Yt2XGXwnfrFmLvAPDUY3+m38n/BcBRxx3BcxOnlKXdVruHHnycAQO+DsCAAV/nwQcLXwm66647rVpn7x5foFmzLfjggwXlaGLZpVjRreka3UNAy4h4efUFkp6ujwY1JNt2aMdPb7ySRo0b0ahRIx57YALPjP8LV13wU24YcQ1RHXy4cBFXnDcMgPv+MI5rbrqaRybdy4cLF/E/Z11e5jPYvI0adTOHfvlLtGvXlrenT2Ho0Gv5xS9vZvQfbmPQt07hvfdmcso3vwvACSf05bTTTqJqeRXLln3Mqad+r8ytL5/qBAdiVN+jS3t0OCC9n9pm4p8LZpa7CbYeln9aWdL3eQ3Y8Wsl/c2Oevf+Bvv9YX4ywsxyUqxMnOjMLKeh3/xbCic6M8tp6COopXCiM7Ochj6CWgonOjPLcdfVzJLnrquZJc9dVzNLXopvbnGiM7McX6Mzs+S562pmyfNghJklz11XM0ueByPMLHm+RmdmyfM1OjNLXorX6Px1h2aWPFd0ZpaT4mCEKzozy6kmSprWhqR3JL0q6WVJU7JYW0njJb2V/dsmi0vSjZKmZd8+uE+p5+REZ2Y5G+HrDg+PiB4RsV/2+WJgQkR0AyZknwGOAbpl02Dg1lLPyYnOzHKqI0qa1kM/YGQ2PxL4alH8riiYBFRI6ljKAZzozCwnSpzWYfePS3pB0uAs1iEiZmXzsyl8nzRAZ2BG0bYzs9g682CEmeWUentJlrgGF4WGR8Tw1VY7JCIqJbUHxkv6R/HCiAhJG3w0xInOzHJKTXRZUls9sa2+TmX271xJfwR6AnMkdYyIWVnXdG62eiXQpWjz7bPYOnPX1cxyIqKkaU0ktZDUauU8cBTwGjAOGJitNhB4IJsfB5yejb4eCHxY1MVdJ67ozCynHp+M6AD8URIUcs8fIuJRSZOBsZLOBN4FTs7WfxjoC0wDlgKDSj2wE52Z5dTXs64RMR3Yq4b4B0DvGuIBDNkQx3aiM7OcFJ+McKIzs5wUH+p3ojOzHFd0ZpY8V3Rmljy/eNPMkreez602SL5h2MyS54rOzHLcdTWz5KXYdXWiM7McV3RmljxXdGaWPFd0ZpY8V3RmljxXdGaWvIjqcjdhg3OiM7McP+tqZsnz20vMLHmu6Mwsea7ozCx5vr3EzJLn20vMLHnuuppZ8jwYYWbJS7Gi8xuGzSx5rujMLMejrmaWvBS7rk50ZpbjwQgzS54rOjNLnq/RmVny/GSEmSXPFZ2ZJc/X6Mwsee66mlnyXNGZWfKc6MwseemlOVCK2XtjkjQ4IoaXux1WGv/+Ng9+e8n6G1zuBth68e9vM+BEZ2bJc6Izs+Q50a0/X9/ZtPn3txnwYISZJc8VnZklz4luPUjqI+lNSdMkXVzu9tjakzRC0lxJr5W7LVb/nOhKJKkxcDNwDNAdOEVS9/K2ytbB74A+5W6EbRxOdKXrCUyLiOkR8SkwBuhX5jbZWoqIZ4H55W6HbRxOdKXrDMwo+jwzi5lZA+NEZ2bJc6IrXSXQpejz9lnMzBoYJ7rSTQa6SdpZ0hZAf2BcmdtkZjVwoitRRFQBZwOPAW8AYyNianlbZWtL0mjgb8DukmZKOrPcbbL64ycjzCx5rujMLHlOdGaWPCc6M0ueE52ZJc+JzsyS50RnZslzojOz5DnRmVny/g+jB3LEOAVQUgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "logistic_reg_conf_matrix  = metrics.confusion_matrix(y, stratified_cv(X, y, linear_model.LogisticRegression))\n",
    "sns.heatmap(logistic_reg_conf_matrix, annot=True,  fmt='');\n",
    "title = 'Logistic Regression'\n",
    "plt.title(title);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosting Classifier:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "       False       0.96      0.99      0.97      2850\n",
      "        True       0.91      0.73      0.81       483\n",
      "\n",
      "    accuracy                           0.95      3333\n",
      "   macro avg       0.93      0.86      0.89      3333\n",
      "weighted avg       0.95      0.95      0.95      3333\n",
      "\n",
      "\n",
      "Support vector machine(SVM):\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "       False       0.92      0.99      0.95      2850\n",
      "        True       0.88      0.51      0.65       483\n",
      "\n",
      "    accuracy                           0.92      3333\n",
      "   macro avg       0.90      0.75      0.80      3333\n",
      "weighted avg       0.92      0.92      0.91      3333\n",
      "\n",
      "\n",
      "Random Forest Classifier:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "       False       0.95      0.99      0.97      2850\n",
      "        True       0.94      0.71      0.81       483\n",
      "\n",
      "    accuracy                           0.95      3333\n",
      "   macro avg       0.94      0.85      0.89      3333\n",
      "weighted avg       0.95      0.95      0.95      3333\n",
      "\n",
      "\n",
      "K Nearest Neighbor Classifier:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "       False       0.90      0.99      0.94      2850\n",
      "        True       0.80      0.34      0.47       483\n",
      "\n",
      "    accuracy                           0.89      3333\n",
      "   macro avg       0.85      0.66      0.71      3333\n",
      "weighted avg       0.88      0.89      0.87      3333\n",
      "\n",
      "\n",
      "Logistic Regression:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "       False       0.88      0.97      0.92      2850\n",
      "        True       0.55      0.21      0.30       483\n",
      "\n",
      "    accuracy                           0.86      3333\n",
      "   macro avg       0.72      0.59      0.61      3333\n",
      "weighted avg       0.83      0.86      0.83      3333\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Gradient Boosting Classifier:\\n {}\\n'.format(metrics.classification_report(y, stratified_cv(X, y, ensemble.GradientBoostingClassifier))))\n",
    "print('Support vector machine(SVM):\\n {}\\n'.format(metrics.classification_report(y, stratified_cv(X, y, svm.SVC))))\n",
    "print('Random Forest Classifier:\\n {}\\n'.format(metrics.classification_report(y, stratified_cv(X, y, ensemble.RandomForestClassifier))))\n",
    "print('K Nearest Neighbor Classifier:\\n {}\\n'.format(metrics.classification_report(y, stratified_cv(X, y, neighbors.KNeighborsClassifier))))\n",
    "print('Logistic Regression:\\n {}\\n'.format(metrics.classification_report(y, stratified_cv(X, y, linear_model.LogisticRegression))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4> final model selection </h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier()"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbc = ensemble.GradientBoostingClassifier()\n",
    "gbc.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00189273 0.00352162 0.0003414  0.09486837 0.02218814 0.06105097\n",
      " 0.15973489 0.0046965  0.13090728 0.07779427 0.00135536 0.06504675\n",
      " 0.01072002 0.00068683 0.0073353  0.03318342 0.11087915 0.05025976\n",
      " 0.16353726]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get Feature Importance from the classifier\n",
    "feature_importance = gbc.feature_importances_\n",
    "print (gbc.feature_importances_)\n",
    "feat_importances = pd.Series(gbc.feature_importances_, index=data.columns)\n",
    "feat_importances = feat_importances.nlargest(19)\n",
    "feat_importances.plot(kind='barh' , figsize=(10,10)) "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}