Customer Category.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>tenure</th>\n",
       "      <th>age</th>\n",
       "      <th>marital</th>\n",
       "      <th>address</th>\n",
       "      <th>income</th>\n",
       "      <th>ed</th>\n",
       "      <th>employ</th>\n",
       "      <th>retire</th>\n",
       "      <th>gender</th>\n",
       "      <th>reside</th>\n",
       "      <th>custcat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>64.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>136.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>116.0</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   region  tenure  age  marital  address  income  ed  employ  retire  gender  \\\n",
       "0       2      13   44        1        9    64.0   4       5     0.0       0   \n",
       "1       3      11   33        1        7   136.0   5       5     0.0       0   \n",
       "2       3      68   52        1       24   116.0   1      29     0.0       1   \n",
       "3       2      33   33        0       12    33.0   2       0     0.0       1   \n",
       "4       2      23   30        1        9    30.0   1       2     0.0       0   \n",
       "\n",
       "   reside  custcat  \n",
       "0       2        1  \n",
       "1       6        4  \n",
       "2       2        3  \n",
       "3       1        1  \n",
       "4       4        3  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read the dataset\n",
    "df = pd.read_csv('teleCust1000t.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "region     0\n",
       "tenure     0\n",
       "age        0\n",
       "marital    0\n",
       "address    0\n",
       "income     0\n",
       "ed         0\n",
       "employ     0\n",
       "retire     0\n",
       "gender     0\n",
       "reside     0\n",
       "custcat    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#find missing values in dataset\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>region</th>\n",
       "      <th>tenure</th>\n",
       "      <th>age</th>\n",
       "      <th>marital</th>\n",
       "      <th>address</th>\n",
       "      <th>income</th>\n",
       "      <th>ed</th>\n",
       "      <th>employ</th>\n",
       "      <th>retire</th>\n",
       "      <th>gender</th>\n",
       "      <th>reside</th>\n",
       "      <th>custcat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.0000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.0220</td>\n",
       "      <td>35.526000</td>\n",
       "      <td>41.684000</td>\n",
       "      <td>0.495000</td>\n",
       "      <td>11.551000</td>\n",
       "      <td>77.535000</td>\n",
       "      <td>2.671000</td>\n",
       "      <td>10.987000</td>\n",
       "      <td>0.047000</td>\n",
       "      <td>0.517000</td>\n",
       "      <td>2.331000</td>\n",
       "      <td>2.487000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.8162</td>\n",
       "      <td>21.359812</td>\n",
       "      <td>12.558816</td>\n",
       "      <td>0.500225</td>\n",
       "      <td>10.086681</td>\n",
       "      <td>107.044165</td>\n",
       "      <td>1.222397</td>\n",
       "      <td>10.082087</td>\n",
       "      <td>0.211745</td>\n",
       "      <td>0.499961</td>\n",
       "      <td>1.435793</td>\n",
       "      <td>1.120306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.0000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.0000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.0000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>1668.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          region       tenure          age      marital      address  \\\n",
       "count  1000.0000  1000.000000  1000.000000  1000.000000  1000.000000   \n",
       "mean      2.0220    35.526000    41.684000     0.495000    11.551000   \n",
       "std       0.8162    21.359812    12.558816     0.500225    10.086681   \n",
       "min       1.0000     1.000000    18.000000     0.000000     0.000000   \n",
       "25%       1.0000    17.000000    32.000000     0.000000     3.000000   \n",
       "50%       2.0000    34.000000    40.000000     0.000000     9.000000   \n",
       "75%       3.0000    54.000000    51.000000     1.000000    18.000000   \n",
       "max       3.0000    72.000000    77.000000     1.000000    55.000000   \n",
       "\n",
       "            income           ed       employ       retire       gender  \\\n",
       "count  1000.000000  1000.000000  1000.000000  1000.000000  1000.000000   \n",
       "mean     77.535000     2.671000    10.987000     0.047000     0.517000   \n",
       "std     107.044165     1.222397    10.082087     0.211745     0.499961   \n",
       "min       9.000000     1.000000     0.000000     0.000000     0.000000   \n",
       "25%      29.000000     2.000000     3.000000     0.000000     0.000000   \n",
       "50%      47.000000     3.000000     8.000000     0.000000     1.000000   \n",
       "75%      83.000000     4.000000    17.000000     0.000000     1.000000   \n",
       "max    1668.000000     5.000000    47.000000     1.000000     1.000000   \n",
       "\n",
       "            reside      custcat  \n",
       "count  1000.000000  1000.000000  \n",
       "mean      2.331000     2.487000  \n",
       "std       1.435793     1.120306  \n",
       "min       1.000000     1.000000  \n",
       "25%       1.000000     1.000000  \n",
       "50%       2.000000     3.000000  \n",
       "75%       3.000000     3.000000  \n",
       "max       8.000000     4.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    281\n",
       "1    266\n",
       "4    236\n",
       "2    217\n",
       "Name: custcat, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#different class label values\n",
    "df['custcat'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xe0e1030>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Distribution of data\n",
    "fig, axes = plt.subplots(nrows=2,ncols=1)\n",
    "fig.set_size_inches(8,10)\n",
    "sn.boxplot(data=df,orient=\"v\",ax=axes[0])\n",
    "sn.boxplot(data=df,y=\"custcat\",ax=axes[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#distribution of class label\n",
    "#1- Basic Service 2- E-Service 3- Plus Service 4- Total Service\n",
    "sn.countplot(x=\"custcat\", data=df)\n",
    "plt.xlabel(\"Customer Category (1-4)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2., 13., 44., ...,  0.,  0.,  2.],\n",
       "       [ 3., 11., 33., ...,  0.,  0.,  6.],\n",
       "       [ 3., 68., 52., ...,  0.,  1.,  2.],\n",
       "       ...,\n",
       "       [ 3., 67., 59., ...,  0.,  1.,  1.],\n",
       "       [ 3., 70., 49., ...,  0.,  1.,  1.],\n",
       "       [ 3., 50., 36., ...,  0.,  1.,  3.]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To use scikit-learn library, we have to convert the Pandas data frame to a Numpy array:\n",
    "X=df.iloc[:, :-1].values\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4, 3, 1, 3, 3, 2, 4, 3, 2, 1, 3, 1, 4, 1, 2, 3, 3, 1, 4, 2, 1,\n",
       "       4, 1, 3, 3, 4, 3, 1, 2, 2, 4, 3, 2, 4, 2, 4, 3, 3, 3, 3, 3, 1, 4,\n",
       "       3, 2, 2, 4, 1, 3, 1, 2, 4, 1, 1, 1, 2, 1, 3, 2, 3, 4, 1, 4, 4, 1,\n",
       "       3, 3, 1, 1, 1, 2, 1, 2, 3, 1, 3, 1, 3, 4, 4, 1, 4, 3, 2, 1, 2, 4,\n",
       "       4, 3, 2, 1, 2, 4, 1, 4, 3, 2, 3, 2, 4, 3, 3, 2, 2, 2, 1, 4, 1, 2,\n",
       "       2, 2, 1, 2, 3, 1, 3, 3, 3, 1, 1, 1, 1, 2, 1, 2, 4, 2, 2, 4, 3, 1,\n",
       "       4, 2, 3, 1, 1, 3, 3, 4, 4, 3, 4, 2, 3, 1, 4, 2, 2, 2, 1, 3, 4, 4,\n",
       "       4, 3, 1, 1, 2, 3, 3, 3, 1, 3, 2, 2, 4, 1, 1, 2, 4, 1, 4, 1, 2, 1,\n",
       "       3, 4, 4, 1, 4, 1, 2, 2, 2, 1, 1, 2, 3, 3, 1, 4, 2, 3, 4, 1, 1, 3,\n",
       "       1, 4, 1, 4, 2, 2, 4, 1, 3, 4, 3, 3, 1, 1, 1, 2, 2, 1, 3, 4, 4, 1,\n",
       "       3, 4, 1, 3, 1, 4, 4, 1, 3, 3, 3, 3, 2, 1, 4, 2, 4, 4, 1, 4, 1, 4,\n",
       "       3, 2, 1, 3, 2, 1, 1, 2, 1, 4, 2, 2, 4, 3, 2, 1, 4, 4, 4, 1, 3, 2,\n",
       "       1, 1, 3, 3, 3, 1, 3, 2, 4, 1, 3, 2, 2, 3, 3, 1, 4, 4, 3, 2, 3, 3,\n",
       "       4, 3, 3, 2, 4, 3, 2, 3, 4, 4, 3, 1, 4, 2, 3, 3, 2, 2, 1, 4, 2, 3,\n",
       "       4, 1, 1, 2, 2, 4, 4, 1, 3, 1, 4, 1, 4, 3, 2, 4, 1, 3, 1, 3, 4, 1,\n",
       "       1, 1, 1, 4, 2, 1, 3, 1, 1, 3, 1, 4, 1, 4, 3, 1, 2, 3, 4, 4, 4, 3,\n",
       "       1, 3, 2, 3, 2, 2, 3, 3, 3, 3, 4, 2, 3, 1, 4, 3, 1, 4, 4, 3, 2, 4,\n",
       "       1, 1, 2, 2, 4, 1, 1, 2, 3, 2, 2, 3, 1, 4, 3, 2, 1, 4, 4, 4, 3, 4,\n",
       "       3, 2, 3, 3, 2, 3, 2, 3, 1, 3, 4, 2, 1, 2, 4, 1, 2, 2, 3, 2, 1, 3,\n",
       "       2, 1, 2, 1, 3, 4, 2, 2, 4, 1, 3, 2, 1, 2, 1, 2, 4, 4, 1, 4, 3, 4,\n",
       "       4, 3, 4, 1, 3, 2, 3, 1, 4, 3, 3, 1, 1, 2, 4, 1, 4, 2, 3, 4, 1, 4,\n",
       "       3, 2, 3, 4, 3, 1, 2, 1, 2, 3, 2, 1, 4, 2, 4, 3, 2, 1, 1, 1, 2, 1,\n",
       "       3, 1, 1, 3, 2, 3, 1, 1, 1, 1, 3, 4, 2, 4, 3, 3, 2, 2, 2, 3, 1, 1,\n",
       "       2, 4, 2, 2, 4, 4, 2, 2, 4, 4, 1, 4, 4, 3, 4, 2, 3, 1, 2, 2, 3, 1,\n",
       "       4, 1, 1, 1, 3, 2, 3, 3, 4, 3, 4, 4, 3, 3, 4, 1, 3, 4, 1, 3, 1, 1,\n",
       "       2, 4, 2, 3, 1, 1, 3, 3, 2, 4, 2, 4, 1, 1, 3, 2, 1, 2, 1, 3, 2, 4,\n",
       "       3, 4, 1, 1, 4, 1, 4, 3, 2, 3, 3, 1, 1, 3, 1, 1, 1, 1, 3, 4, 3, 1,\n",
       "       3, 4, 3, 3, 4, 1, 1, 2, 1, 3, 2, 2, 3, 1, 4, 1, 3, 2, 3, 3, 3, 1,\n",
       "       1, 3, 2, 4, 3, 1, 2, 2, 4, 1, 4, 3, 1, 1, 3, 1, 3, 4, 1, 4, 2, 1,\n",
       "       3, 3, 4, 3, 3, 1, 1, 2, 4, 4, 3, 1, 3, 1, 3, 1, 1, 3, 3, 4, 2, 1,\n",
       "       3, 2, 1, 4, 4, 1, 4, 2, 2, 4, 2, 2, 3, 1, 2, 3, 4, 3, 4, 2, 2, 3,\n",
       "       3, 1, 3, 3, 2, 1, 4, 2, 2, 3, 2, 1, 4, 2, 4, 3, 4, 2, 2, 2, 3, 3,\n",
       "       1, 2, 2, 2, 3, 4, 4, 1, 4, 4, 4, 1, 4, 3, 3, 4, 1, 1, 1, 4, 2, 3,\n",
       "       2, 3, 1, 1, 1, 1, 4, 1, 2, 2, 1, 1, 4, 3, 2, 4, 3, 3, 1, 4, 2, 2,\n",
       "       2, 1, 4, 4, 2, 3, 2, 1, 3, 2, 1, 2, 4, 4, 3, 1, 2, 4, 4, 3, 2, 1,\n",
       "       2, 3, 4, 3, 3, 2, 1, 4, 4, 4, 1, 1, 3, 4, 1, 1, 1, 3, 2, 2, 2, 1,\n",
       "       2, 3, 1, 3, 2, 4, 4, 4, 3, 4, 3, 3, 3, 3, 3, 4, 3, 2, 4, 1, 2, 3,\n",
       "       3, 3, 3, 3, 4, 3, 1, 3, 4, 4, 1, 4, 1, 3, 2, 2, 3, 2, 1, 1, 3, 1,\n",
       "       2, 3, 3, 4, 1, 3, 1, 2, 1, 1, 1, 4, 4, 4, 2, 3, 2, 2, 3, 3, 1, 3,\n",
       "       4, 1, 2, 3, 4, 4, 2, 4, 1, 4, 3, 2, 4, 3, 3, 2, 1, 3, 3, 3, 4, 2,\n",
       "       4, 4, 3, 2, 3, 3, 4, 4, 2, 4, 4, 2, 3, 4, 2, 1, 2, 2, 4, 4, 4, 4,\n",
       "       1, 1, 1, 1, 1, 2, 3, 1, 3, 4, 2, 1, 3, 3, 2, 3, 3, 4, 1, 4, 2, 3,\n",
       "       3, 3, 4, 1, 3, 3, 4, 4, 4, 3, 4, 1, 1, 3, 1, 3, 1, 2, 4, 3, 3, 4,\n",
       "       3, 2, 4, 2, 1, 4, 3, 4, 4, 1, 1, 3, 3, 3, 1, 1, 2, 1, 4, 4, 4, 3,\n",
       "       1, 3, 1, 1, 3, 3, 3, 2, 3, 1, 1, 3, 2, 1, 3, 1, 3, 4, 4, 4, 2, 3,\n",
       "       3, 2, 3, 4, 4, 1, 1, 4, 3, 2], dtype=int64)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df.iloc[:, 11].values\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting the dataset\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=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaling the dataset\n",
    "sc_X=StandardScaler()\n",
    "X_train=sc_X.fit_transform(X_train)\n",
    "X_test=sc_X.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.02297721, -1.17600045, -0.1336384 , ..., -0.20742414,\n",
       "         0.96076892, -0.92648437],\n",
       "       [-0.02297721, -0.94319822, -0.21330362, ..., -0.20742414,\n",
       "        -1.040833  , -0.92648437],\n",
       "       [ 1.20247394, -0.43103332,  0.26468769, ..., -0.20742414,\n",
       "        -1.040833  , -0.92648437],\n",
       "       ...,\n",
       "       [-0.02297721, -0.38447288, -1.40828191, ..., -0.20742414,\n",
       "         0.96076892,  1.17319692],\n",
       "       [-0.02297721,  0.73297781, -0.45229928, ..., -0.20742414,\n",
       "        -1.040833  , -0.22659061],\n",
       "       [ 1.20247394,  1.66418672,  2.0173225 , ..., -0.20742414,\n",
       "         0.96076892, -0.22659061]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.20247394, -0.43103332,  0.98167466, ..., -0.20742414,\n",
       "         0.96076892, -0.22659061],\n",
       "       [ 1.20247394, -1.31568178, -0.85062537, ..., -0.20742414,\n",
       "         0.96076892,  2.57298444],\n",
       "       [-1.24842835,  0.59329647, -0.77096016, ..., -0.20742414,\n",
       "        -1.040833  , -0.92648437],\n",
       "       ...,\n",
       "       [-0.02297721,  1.24514271,  0.34435291, ..., -0.20742414,\n",
       "         0.96076892, -0.92648437],\n",
       "       [ 1.20247394, -0.52415421,  1.30033553, ..., -0.20742414,\n",
       "        -1.040833  , -0.22659061],\n",
       "       [-0.02297721,  0.68641737, -1.16928625, ..., -0.20742414,\n",
       "         0.96076892,  0.47330316]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (800, 11) (800,)\n",
      "Test set: (200, 11) (200,)\n"
     ]
    }
   ],
   "source": [
    "#Shape of train and test data\n",
    "print ('Train set:', X_train.shape,  y_train.shape)\n",
    "print ('Test set:', X_test.shape,  y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using KNeighborsClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train Model  \n",
    "neigh = KNeighborsClassifier(n_neighbors = 4).fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Prediction\n",
    "prediction = neigh.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 18,
     "metadata": {},
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   ],
   "source": [
    "prediction1=pd.DataFrame(prediction)\n",
    "prediction1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Accuracy\n",
    "from sklearn import metrics\n",
    "metrics.accuracy_score(y_test, prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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=False, random_state=0, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now Using SVM Algorithm \n",
    "from sklearn.svm import SVC\n",
    "classifier=SVC(kernel='rbf',random_state=0)\n",
    "classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predict\n",
    "y_pred=classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
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     "execution_count": 22,
     "metadata": {},
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   "source": [
    "prediction2=pd.DataFrame(y_pred)\n",
    "prediction2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.315"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Accuracy\n",
    "metrics.accuracy_score(y_test, prediction2)"
   ]
  }
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