day26-ordinal-encoding/day26.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('customer.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>review</th>\n",
       "      <th>education</th>\n",
       "      <th>purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>89</td>\n",
       "      <td>Female</td>\n",
       "      <td>Good</td>\n",
       "      <td>PG</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>98</td>\n",
       "      <td>Female</td>\n",
       "      <td>Good</td>\n",
       "      <td>UG</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>74</td>\n",
       "      <td>Male</td>\n",
       "      <td>Good</td>\n",
       "      <td>UG</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>39</td>\n",
       "      <td>Female</td>\n",
       "      <td>Good</td>\n",
       "      <td>UG</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>73</td>\n",
       "      <td>Male</td>\n",
       "      <td>Average</td>\n",
       "      <td>UG</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  gender   review education purchased\n",
       "33   89  Female     Good        PG       Yes\n",
       "10   98  Female     Good        UG       Yes\n",
       "9    74    Male     Good        UG       Yes\n",
       "48   39  Female     Good        UG       Yes\n",
       "30   73    Male  Average        UG        No"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.iloc[:,2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review</th>\n",
       "      <th>education</th>\n",
       "      <th>purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Average</td>\n",
       "      <td>School</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Poor</td>\n",
       "      <td>UG</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Good</td>\n",
       "      <td>PG</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Good</td>\n",
       "      <td>PG</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Average</td>\n",
       "      <td>UG</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    review education purchased\n",
       "0  Average    School        No\n",
       "1     Poor        UG        No\n",
       "2     Good        PG        No\n",
       "3     Good        PG        No\n",
       "4  Average        UG        No"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>review</th>\n",
       "      <th>education</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Poor</td>\n",
       "      <td>UG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Average</td>\n",
       "      <td>UG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Average</td>\n",
       "      <td>School</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Good</td>\n",
       "      <td>UG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Good</td>\n",
       "      <td>PG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Good</td>\n",
       "      <td>School</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Poor</td>\n",
       "      <td>UG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Average</td>\n",
       "      <td>UG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Average</td>\n",
       "      <td>School</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Average</td>\n",
       "      <td>PG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Good</td>\n",
       "      <td>PG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Average</td>\n",
       "      <td>UG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Average</td>\n",
       "      <td>PG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Poor</td>\n",
       "      <td>PG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Poor</td>\n",
       "      <td>UG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Good</td>\n",
       "      <td>PG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Average</td>\n",
       "      <td>UG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Average</td>\n",
       "      <td>PG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Good</td>\n",
       "      <td>UG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Average</td>\n",
       "      <td>School</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Good</td>\n",
       "      <td>School</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Poor</td>\n",
       "      <td>School</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Good</td>\n",
       "      <td>PG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Poor</td>\n",
       "      <td>PG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Good</td>\n",
       "      <td>PG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Average</td>\n",
       "      <td>UG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Poor</td>\n",
       "      <td>School</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Poor</td>\n",
       "      <td>UG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Good</td>\n",
       "      <td>UG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Poor</td>\n",
       "      <td>PG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Poor</td>\n",
       "      <td>PG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Good</td>\n",
       "      <td>School</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Poor</td>\n",
       "      <td>PG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Average</td>\n",
       "      <td>UG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Good</td>\n",
       "      <td>UG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Poor</td>\n",
       "      <td>School</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Poor</td>\n",
       "      <td>PG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Poor</td>\n",
       "      <td>PG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Good</td>\n",
       "      <td>UG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Good</td>\n",
       "      <td>UG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     review education\n",
       "16     Poor        UG\n",
       "44  Average        UG\n",
       "13  Average    School\n",
       "48     Good        UG\n",
       "42     Good        PG\n",
       "38     Good    School\n",
       "17     Poor        UG\n",
       "4   Average        UG\n",
       "5   Average    School\n",
       "24  Average        PG\n",
       "47     Good        PG\n",
       "32  Average        UG\n",
       "37  Average        PG\n",
       "26     Poor        PG\n",
       "15     Poor        UG\n",
       "41     Good        PG\n",
       "30  Average        UG\n",
       "21  Average        PG\n",
       "9      Good        UG\n",
       "20  Average    School\n",
       "23     Good    School\n",
       "12     Poor    School\n",
       "33     Good        PG\n",
       "14     Poor        PG\n",
       "2      Good        PG\n",
       "8   Average        UG\n",
       "7      Poor    School\n",
       "1      Poor        UG\n",
       "36     Good        UG\n",
       "46     Poor        PG\n",
       "19     Poor        PG\n",
       "18     Good    School\n",
       "22     Poor        PG\n",
       "29  Average        UG\n",
       "49     Good        UG\n",
       "28     Poor    School\n",
       "43     Poor        PG\n",
       "27     Poor        PG\n",
       "10     Good        UG\n",
       "11     Good        UG"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "oe = OrdinalEncoder(categories=[['Poor','Average','Good'],['School','UG','PG']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrdinalEncoder(categories=[['Poor', 'Average', 'Good'], ['School', 'UG', 'PG']])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oe.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = oe.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1.],\n",
       "       [1., 1.],\n",
       "       [1., 0.],\n",
       "       [2., 1.],\n",
       "       [2., 2.],\n",
       "       [2., 0.],\n",
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       "       [2., 2.],\n",
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       "       [1., 0.],\n",
       "       [2., 0.],\n",
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       "       [2., 2.],\n",
       "       [0., 2.],\n",
       "       [2., 2.],\n",
       "       [1., 1.],\n",
       "       [0., 0.],\n",
       "       [0., 1.],\n",
       "       [2., 1.],\n",
       "       [0., 2.],\n",
       "       [0., 2.],\n",
       "       [2., 0.],\n",
       "       [0., 2.],\n",
       "       [1., 1.],\n",
       "       [2., 1.],\n",
       "       [0., 0.],\n",
       "       [0., 2.],\n",
       "       [0., 2.],\n",
       "       [2., 1.],\n",
       "       [2., 1.]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['Poor', 'Average', 'Good'], dtype=object),\n",
       " array(['School', 'UG', 'PG'], dtype=object)]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oe.categories_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 2.],\n",
       "       [1., 0.],\n",
       "       [0., 1.],\n",
       "       [2., 0.],\n",
       "       [2., 1.],\n",
       "       [1., 1.],\n",
       "       [2., 1.],\n",
       "       [0., 0.],\n",
       "       [2., 1.],\n",
       "       [0., 2.],\n",
       "       [1., 1.],\n",
       "       [0., 2.],\n",
       "       [2., 0.],\n",
       "       [2., 2.],\n",
       "       [0., 0.],\n",
       "       [2., 2.],\n",
       "       [2., 1.],\n",
       "       [0., 2.],\n",
       "       [0., 1.],\n",
       "       [2., 0.],\n",
       "       [2., 2.],\n",
       "       [0., 2.],\n",
       "       [0., 0.],\n",
       "       [2., 0.],\n",
       "       [2., 0.],\n",
       "       [1., 1.],\n",
       "       [2., 2.],\n",
       "       [0., 1.],\n",
       "       [2., 2.],\n",
       "       [1., 1.],\n",
       "       [2., 2.],\n",
       "       [0., 1.],\n",
       "       [1., 0.],\n",
       "       [0., 0.],\n",
       "       [0., 2.],\n",
       "       [0., 2.],\n",
       "       [1., 2.],\n",
       "       [2., 1.],\n",
       "       [1., 0.],\n",
       "       [2., 0.]])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelEncoder()"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le.fit(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No', 'Yes'], dtype=object)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = le.transform(y_train)\n",
    "y_test = le.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0,\n",
       "       1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1])"
      ]
     },
     "execution_count": 100,
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