Data_PreProcessing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as  pd\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler, Binarizer\n",
    "from word2number import w2n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Datasets/income.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income($)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rob</td>\n",
       "      <td>27</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Michael</td>\n",
       "      <td>29</td>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mohan</td>\n",
       "      <td>29</td>\n",
       "      <td>61000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ismail</td>\n",
       "      <td>28</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kory</td>\n",
       "      <td>42</td>\n",
       "      <td>150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gautam</td>\n",
       "      <td>39</td>\n",
       "      <td>155000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>David</td>\n",
       "      <td>41</td>\n",
       "      <td>160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Andrea</td>\n",
       "      <td>38</td>\n",
       "      <td>162000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Brad</td>\n",
       "      <td>36</td>\n",
       "      <td>156000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Angelina</td>\n",
       "      <td>35</td>\n",
       "      <td>130000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Donald</td>\n",
       "      <td>37</td>\n",
       "      <td>137000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Tom</td>\n",
       "      <td>26</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Arnold</td>\n",
       "      <td>27</td>\n",
       "      <td>48000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Jared</td>\n",
       "      <td>28</td>\n",
       "      <td>51000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Stark</td>\n",
       "      <td>29</td>\n",
       "      <td>49500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ranbir</td>\n",
       "      <td>32</td>\n",
       "      <td>53000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Dipika</td>\n",
       "      <td>40</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Priyanka</td>\n",
       "      <td>41</td>\n",
       "      <td>63000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Nick</td>\n",
       "      <td>43</td>\n",
       "      <td>64000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Alia</td>\n",
       "      <td>39</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Sid</td>\n",
       "      <td>41</td>\n",
       "      <td>82000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Abdul</td>\n",
       "      <td>39</td>\n",
       "      <td>58000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Name  Age  Income($)\n",
       "0        Rob   27      70000\n",
       "1    Michael   29      90000\n",
       "2      Mohan   29      61000\n",
       "3     Ismail   28      60000\n",
       "4       Kory   42     150000\n",
       "5     Gautam   39     155000\n",
       "6      David   41     160000\n",
       "7     Andrea   38     162000\n",
       "8       Brad   36     156000\n",
       "9   Angelina   35     130000\n",
       "10    Donald   37     137000\n",
       "11       Tom   26      45000\n",
       "12    Arnold   27      48000\n",
       "13     Jared   28      51000\n",
       "14     Stark   29      49500\n",
       "15    Ranbir   32      53000\n",
       "16    Dipika   40      65000\n",
       "17  Priyanka   41      63000\n",
       "18      Nick   43      64000\n",
       "19      Alia   39      80000\n",
       "20       Sid   41      82000\n",
       "21     Abdul   39      58000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## implementing  MinMaxScaler using formula"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "MIN = df['Age'].min()\n",
    "MAX = df['Age'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "diffe = MAX - MIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['NewAge'] = (df['Age'] - MIN)/diffe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income($)</th>\n",
       "      <th>NewAge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rob</td>\n",
       "      <td>27</td>\n",
       "      <td>70000</td>\n",
       "      <td>0.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Michael</td>\n",
       "      <td>29</td>\n",
       "      <td>90000</td>\n",
       "      <td>0.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mohan</td>\n",
       "      <td>29</td>\n",
       "      <td>61000</td>\n",
       "      <td>0.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ismail</td>\n",
       "      <td>28</td>\n",
       "      <td>60000</td>\n",
       "      <td>0.117647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kory</td>\n",
       "      <td>42</td>\n",
       "      <td>150000</td>\n",
       "      <td>0.941176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gautam</td>\n",
       "      <td>39</td>\n",
       "      <td>155000</td>\n",
       "      <td>0.764706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>David</td>\n",
       "      <td>41</td>\n",
       "      <td>160000</td>\n",
       "      <td>0.882353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Andrea</td>\n",
       "      <td>38</td>\n",
       "      <td>162000</td>\n",
       "      <td>0.705882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Brad</td>\n",
       "      <td>36</td>\n",
       "      <td>156000</td>\n",
       "      <td>0.588235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Angelina</td>\n",
       "      <td>35</td>\n",
       "      <td>130000</td>\n",
       "      <td>0.529412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Donald</td>\n",
       "      <td>37</td>\n",
       "      <td>137000</td>\n",
       "      <td>0.647059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Tom</td>\n",
       "      <td>26</td>\n",
       "      <td>45000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Arnold</td>\n",
       "      <td>27</td>\n",
       "      <td>48000</td>\n",
       "      <td>0.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Jared</td>\n",
       "      <td>28</td>\n",
       "      <td>51000</td>\n",
       "      <td>0.117647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Stark</td>\n",
       "      <td>29</td>\n",
       "      <td>49500</td>\n",
       "      <td>0.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ranbir</td>\n",
       "      <td>32</td>\n",
       "      <td>53000</td>\n",
       "      <td>0.352941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Dipika</td>\n",
       "      <td>40</td>\n",
       "      <td>65000</td>\n",
       "      <td>0.823529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Priyanka</td>\n",
       "      <td>41</td>\n",
       "      <td>63000</td>\n",
       "      <td>0.882353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Nick</td>\n",
       "      <td>43</td>\n",
       "      <td>64000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Alia</td>\n",
       "      <td>39</td>\n",
       "      <td>80000</td>\n",
       "      <td>0.764706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Sid</td>\n",
       "      <td>41</td>\n",
       "      <td>82000</td>\n",
       "      <td>0.882353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Abdul</td>\n",
       "      <td>39</td>\n",
       "      <td>58000</td>\n",
       "      <td>0.764706</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Name  Age  Income($)    NewAge\n",
       "0        Rob   27      70000  0.058824\n",
       "1    Michael   29      90000  0.176471\n",
       "2      Mohan   29      61000  0.176471\n",
       "3     Ismail   28      60000  0.117647\n",
       "4       Kory   42     150000  0.941176\n",
       "5     Gautam   39     155000  0.764706\n",
       "6      David   41     160000  0.882353\n",
       "7     Andrea   38     162000  0.705882\n",
       "8       Brad   36     156000  0.588235\n",
       "9   Angelina   35     130000  0.529412\n",
       "10    Donald   37     137000  0.647059\n",
       "11       Tom   26      45000  0.000000\n",
       "12    Arnold   27      48000  0.058824\n",
       "13     Jared   28      51000  0.117647\n",
       "14     Stark   29      49500  0.176471\n",
       "15    Ranbir   32      53000  0.352941\n",
       "16    Dipika   40      65000  0.823529\n",
       "17  Priyanka   41      63000  0.882353\n",
       "18      Nick   43      64000  1.000000\n",
       "19      Alia   39      80000  0.764706\n",
       "20       Sid   41      82000  0.882353\n",
       "21     Abdul   39      58000  0.764706"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MinMaxSclaer using prebuilt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "ages = a.fit_transform(df[['Age']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.05882353],\n",
       "       [0.17647059],\n",
       "       [0.17647059],\n",
       "       [0.11764706],\n",
       "       [0.94117647],\n",
       "       [0.76470588],\n",
       "       [0.88235294],\n",
       "       [0.70588235],\n",
       "       [0.58823529],\n",
       "       [0.52941176],\n",
       "       [0.64705882],\n",
       "       [0.        ],\n",
       "       [0.05882353],\n",
       "       [0.11764706],\n",
       "       [0.17647059],\n",
       "       [0.35294118],\n",
       "       [0.82352941],\n",
       "       [0.88235294],\n",
       "       [1.        ],\n",
       "       [0.76470588],\n",
       "       [0.88235294],\n",
       "       [0.76470588]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standard scaler using formula"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "mm = df['Income($)'].mean()\n",
    "st = df['Income($)'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['NewIncome'] = (df['Income($)'] - mm)/st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income($)</th>\n",
       "      <th>NewAge</th>\n",
       "      <th>NewIncome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rob</td>\n",
       "      <td>27</td>\n",
       "      <td>70000</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>-0.469633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Michael</td>\n",
       "      <td>29</td>\n",
       "      <td>90000</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>-0.009925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mohan</td>\n",
       "      <td>29</td>\n",
       "      <td>61000</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>-0.676501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ismail</td>\n",
       "      <td>28</td>\n",
       "      <td>60000</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>-0.699486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kory</td>\n",
       "      <td>42</td>\n",
       "      <td>150000</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>1.369196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gautam</td>\n",
       "      <td>39</td>\n",
       "      <td>155000</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>1.484123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>David</td>\n",
       "      <td>41</td>\n",
       "      <td>160000</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>1.599049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Andrea</td>\n",
       "      <td>38</td>\n",
       "      <td>162000</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>1.645020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Brad</td>\n",
       "      <td>36</td>\n",
       "      <td>156000</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>1.507108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Angelina</td>\n",
       "      <td>35</td>\n",
       "      <td>130000</td>\n",
       "      <td>0.529412</td>\n",
       "      <td>0.909489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Donald</td>\n",
       "      <td>37</td>\n",
       "      <td>137000</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>1.070386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Tom</td>\n",
       "      <td>26</td>\n",
       "      <td>45000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.044266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Arnold</td>\n",
       "      <td>27</td>\n",
       "      <td>48000</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>-0.975310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Jared</td>\n",
       "      <td>28</td>\n",
       "      <td>51000</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>-0.906354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Stark</td>\n",
       "      <td>29</td>\n",
       "      <td>49500</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>-0.940832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ranbir</td>\n",
       "      <td>32</td>\n",
       "      <td>53000</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>-0.860384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Dipika</td>\n",
       "      <td>40</td>\n",
       "      <td>65000</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>-0.584559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Priyanka</td>\n",
       "      <td>41</td>\n",
       "      <td>63000</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>-0.630530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Nick</td>\n",
       "      <td>43</td>\n",
       "      <td>64000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.607545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Alia</td>\n",
       "      <td>39</td>\n",
       "      <td>80000</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>-0.239779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Sid</td>\n",
       "      <td>41</td>\n",
       "      <td>82000</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>-0.193808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Abdul</td>\n",
       "      <td>39</td>\n",
       "      <td>58000</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>-0.745457</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Name  Age  Income($)    NewAge  NewIncome\n",
       "0        Rob   27      70000  0.058824  -0.469633\n",
       "1    Michael   29      90000  0.176471  -0.009925\n",
       "2      Mohan   29      61000  0.176471  -0.676501\n",
       "3     Ismail   28      60000  0.117647  -0.699486\n",
       "4       Kory   42     150000  0.941176   1.369196\n",
       "5     Gautam   39     155000  0.764706   1.484123\n",
       "6      David   41     160000  0.882353   1.599049\n",
       "7     Andrea   38     162000  0.705882   1.645020\n",
       "8       Brad   36     156000  0.588235   1.507108\n",
       "9   Angelina   35     130000  0.529412   0.909489\n",
       "10    Donald   37     137000  0.647059   1.070386\n",
       "11       Tom   26      45000  0.000000  -1.044266\n",
       "12    Arnold   27      48000  0.058824  -0.975310\n",
       "13     Jared   28      51000  0.117647  -0.906354\n",
       "14     Stark   29      49500  0.176471  -0.940832\n",
       "15    Ranbir   32      53000  0.352941  -0.860384\n",
       "16    Dipika   40      65000  0.823529  -0.584559\n",
       "17  Priyanka   41      63000  0.882353  -0.630530\n",
       "18      Nick   43      64000  1.000000  -0.607545\n",
       "19      Alia   39      80000  0.764706  -0.239779\n",
       "20       Sid   41      82000  0.882353  -0.193808\n",
       "21     Abdul   39      58000  0.764706  -0.745457"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## standardScaler using prebuilt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "std_scaler = StandardScaler()\n",
    "Income = std_scaler.fit_transform(df[['Income($)']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.48068427],\n",
       "       [-0.01015907],\n",
       "       [-0.69242061],\n",
       "       [-0.71594687],\n",
       "       [ 1.40141653],\n",
       "       [ 1.51904783],\n",
       "       [ 1.63667914],\n",
       "       [ 1.68373166],\n",
       "       [ 1.54257409],\n",
       "       [ 0.93089133],\n",
       "       [ 1.09557515],\n",
       "       [-1.06884077],\n",
       "       [-0.99826199],\n",
       "       [-0.92768321],\n",
       "       [-0.9629726 ],\n",
       "       [-0.88063069],\n",
       "       [-0.59831557],\n",
       "       [-0.64536809],\n",
       "       [-0.62184183],\n",
       "       [-0.24542167],\n",
       "       [-0.19836915],\n",
       "       [-0.76299939]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('Datasets/loanf.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Interest.Rate</th>\n",
       "      <th>FICO.Score</th>\n",
       "      <th>Loan.Length</th>\n",
       "      <th>Monthly.Income</th>\n",
       "      <th>Loan.Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15.31</td>\n",
       "      <td>670</td>\n",
       "      <td>36</td>\n",
       "      <td>4891.67</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>19.72</td>\n",
       "      <td>670</td>\n",
       "      <td>36</td>\n",
       "      <td>3575.00</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14.27</td>\n",
       "      <td>665</td>\n",
       "      <td>36</td>\n",
       "      <td>4250.00</td>\n",
       "      <td>10625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>21.67</td>\n",
       "      <td>670</td>\n",
       "      <td>60</td>\n",
       "      <td>14166.67</td>\n",
       "      <td>28000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21.98</td>\n",
       "      <td>665</td>\n",
       "      <td>36</td>\n",
       "      <td>6666.67</td>\n",
       "      <td>22000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Interest.Rate  FICO.Score  Loan.Length  Monthly.Income  Loan.Amount\n",
       "6           15.31         670           36         4891.67         6000\n",
       "11          19.72         670           36         3575.00         2000\n",
       "12          14.27         665           36         4250.00        10625\n",
       "13          21.67         670           60        14166.67        28000\n",
       "21          21.98         665           36         6666.67        22000"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "bini = Binarizer(threshold = 15)\n",
    "data['Loan_prediction'] = bini.fit_transform(data[['Interest.Rate']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Interest.Rate</th>\n",
       "      <th>FICO.Score</th>\n",
       "      <th>Loan.Length</th>\n",
       "      <th>Monthly.Income</th>\n",
       "      <th>Loan.Amount</th>\n",
       "      <th>Loan_prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15.31</td>\n",
       "      <td>670</td>\n",
       "      <td>36</td>\n",
       "      <td>4891.67</td>\n",
       "      <td>6000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>19.72</td>\n",
       "      <td>670</td>\n",
       "      <td>36</td>\n",
       "      <td>3575.00</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14.27</td>\n",
       "      <td>665</td>\n",
       "      <td>36</td>\n",
       "      <td>4250.00</td>\n",
       "      <td>10625</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>21.67</td>\n",
       "      <td>670</td>\n",
       "      <td>60</td>\n",
       "      <td>14166.67</td>\n",
       "      <td>28000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21.98</td>\n",
       "      <td>665</td>\n",
       "      <td>36</td>\n",
       "      <td>6666.67</td>\n",
       "      <td>22000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2472</th>\n",
       "      <td>7.90</td>\n",
       "      <td>770</td>\n",
       "      <td>36</td>\n",
       "      <td>5166.67</td>\n",
       "      <td>19000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2481</th>\n",
       "      <td>6.03</td>\n",
       "      <td>760</td>\n",
       "      <td>36</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2482</th>\n",
       "      <td>6.62</td>\n",
       "      <td>810</td>\n",
       "      <td>36</td>\n",
       "      <td>9250.00</td>\n",
       "      <td>27000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2486</th>\n",
       "      <td>10.75</td>\n",
       "      <td>765</td>\n",
       "      <td>36</td>\n",
       "      <td>7083.33</td>\n",
       "      <td>25000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2497</th>\n",
       "      <td>14.09</td>\n",
       "      <td>740</td>\n",
       "      <td>60</td>\n",
       "      <td>8903.25</td>\n",
       "      <td>16000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2500 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Interest.Rate  FICO.Score  Loan.Length  Monthly.Income  Loan.Amount  \\\n",
       "6             15.31         670           36         4891.67         6000   \n",
       "11            19.72         670           36         3575.00         2000   \n",
       "12            14.27         665           36         4250.00        10625   \n",
       "13            21.67         670           60        14166.67        28000   \n",
       "21            21.98         665           36         6666.67        22000   \n",
       "...             ...         ...          ...             ...          ...   \n",
       "2472           7.90         770           36         5166.67        19000   \n",
       "2481           6.03         760           36         5000.00        10000   \n",
       "2482           6.62         810           36         9250.00        27000   \n",
       "2486          10.75         765           36         7083.33        25000   \n",
       "2497          14.09         740           60         8903.25        16000   \n",
       "\n",
       "      Loan_prediction  \n",
       "6                 1.0  \n",
       "11                1.0  \n",
       "12                0.0  \n",
       "13                1.0  \n",
       "21                1.0  \n",
       "...               ...  \n",
       "2472              0.0  \n",
       "2481              0.0  \n",
       "2482              0.0  \n",
       "2486              0.0  \n",
       "2497              0.0  \n",
       "\n",
       "[2500 rows x 6 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    1727\n",
       "1.0     773\n",
       "Name: Loan_prediction, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Loan_prediction'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = pd.read_csv('Datasets/hiring.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>experience</th>\n",
       "      <th>test_score(out of 10)</th>\n",
       "      <th>interview_score(out of 10)</th>\n",
       "      <th>salary($)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>five</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>seven</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>three</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10</td>\n",
       "      <td>62000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ten</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>eleven</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  experience  test_score(out of 10)  interview_score(out of 10)  salary($)\n",
       "0        NaN                    8.0                           9      50000\n",
       "1        NaN                    8.0                           6      45000\n",
       "2       five                    6.0                           7      60000\n",
       "3        two                   10.0                          10      65000\n",
       "4      seven                    9.0                           6      70000\n",
       "5      three                    7.0                          10      62000\n",
       "6        ten                    NaN                           7      72000\n",
       "7     eleven                    7.0                           8      80000"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2['experience'].fillna('Zero', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "  experience  test_score(out of 10)  interview_score(out of 10)  salary($)\n",
       "0       Zero                    8.0                           9      50000\n",
       "1       Zero                    8.0                           6      45000\n",
       "2       five                    6.0                           7      60000\n",
       "3        two                   10.0                          10      65000\n",
       "4      seven                    9.0                           6      70000\n",
       "5      three                    7.0                          10      62000\n",
       "6        ten                    NaN                           7      72000\n",
       "7     eleven                    7.0                           8      80000"
      ]
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     "execution_count": 68,
     "metadata": {},
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    "data2"
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  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "me = data2['test_score(out of 10)'].mean()\n",
    "data2['test_score(out of 10)'].fillna(me,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "  experience  test_score(out of 10)  interview_score(out of 10)  salary($)\n",
       "0       Zero               8.000000                           9      50000\n",
       "1       Zero               8.000000                           6      45000\n",
       "2       five               6.000000                           7      60000\n",
       "3        two              10.000000                          10      65000\n",
       "4      seven               9.000000                           6      70000\n",
       "5      three               7.000000                          10      62000\n",
       "6        ten               7.857143                           7      72000\n",
       "7     eleven               7.000000                           8      80000"
      ]
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     "execution_count": 71,
     "metadata": {},
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    "data2"
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  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting word2number\n",
      "  Downloading word2number-1.1.zip (9.7 kB)\n",
      "Building wheels for collected packages: word2number\n",
      "  Building wheel for word2number (setup.py): started\n",
      "  Building wheel for word2number (setup.py): finished with status 'done'\n",
      "  Created wheel for word2number: filename=word2number-1.1-py3-none-any.whl size=5595 sha256=bacf0d3a408a6d90c897792eacb2efaa02e14cee5cbbb5facfb4ee9f2b872e0a\n",
      "  Stored in directory: c:\\users\\deebn\\appdata\\local\\pip\\cache\\wheels\\4b\\c3\\77\\a5f48aeb0d3efb7cd5ad61cbd3da30bbf9ffc9662b07c9f879\n",
      "Successfully built word2number\n",
      "Installing collected packages: word2number\n",
      "Successfully installed word2number-1.1\n"
     ]
    }
   ],
   "source": [
    "!pip install word2number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2['experience'] = data2['experience'].apply(w2n.word_to_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
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       "</table>\n",
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      ],
      "text/plain": [
       "   experience  test_score(out of 10)  interview_score(out of 10)  salary($)\n",
       "0           0               8.000000                           9      50000\n",
       "1           0               8.000000                           6      45000\n",
       "2           5               6.000000                           7      60000\n",
       "3           2              10.000000                          10      65000\n",
       "4           7               9.000000                           6      70000\n",
       "5           3               7.000000                          10      62000\n",
       "6          10               7.857143                           7      72000\n",
       "7          11               7.000000                           8      80000"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "data2"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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