data_preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "46198e3d",
   "metadata": {},
   "source": [
    "# Data Preprocessing or Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "08d74142",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'F:\\\\Machine-learning\\\\Assignments'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e2095cf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('F:\\\\Machine-learning\\\\Assignments\\\\data_set')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2960e935",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'F:\\\\Machine-learning\\\\Assignments\\\\data_set'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c87a9065",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('supershops.csv')\n",
    "df2 = pd.read_csv('supershops.csv')\n",
    "df3 = pd.read_csv('supershops.csv')\n",
    "df4 = pd.read_csv('supershops.csv')\n",
    "df5 = pd.read_csv('supershops.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "07d52d35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Marketing Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Transport</th>\n",
       "      <th>Area</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>114523.61</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>Ctg</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Rangpur</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Rangpur</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Marketing Spend  Administration  Transport     Area     Profit\n",
       "0        114523.61       136897.80  471784.10    Dhaka  192261.83\n",
       "1        162597.70       151377.59  443898.53      Ctg  191792.06\n",
       "2        153441.51       101145.55  407934.54  Rangpur  191050.39\n",
       "3        144372.41       118671.85  383199.62    Dhaka  182901.99\n",
       "4        142107.34        91391.77  366168.42  Rangpur  166187.94"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e7dea0cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 5)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "abf68e3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Marketing Spend', 'Administration', 'Transport', 'Area', 'Profit'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dd26f55e",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop('Profit', axis=1)\n",
    "y = df['Profit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ddbb9de6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Transport</th>\n",
       "      <th>Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>114523.61</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>Dhaka</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>Ctg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Rangpur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>Dhaka</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Rangpur</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Marketing Spend  Administration  Transport     Area\n",
       "0        114523.61       136897.80  471784.10    Dhaka\n",
       "1        162597.70       151377.59  443898.53      Ctg\n",
       "2        153441.51       101145.55  407934.54  Rangpur\n",
       "3        144372.41       118671.85  383199.62    Dhaka\n",
       "4        142107.34        91391.77  366168.42  Rangpur"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d6dd4e37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    192261.83\n",
       "1    191792.06\n",
       "2    191050.39\n",
       "3    182901.99\n",
       "4    166187.94\n",
       "Name: Profit, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e0bb9d8",
   "metadata": {},
   "source": [
    "# 1. Normalization Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cab55e07",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform features by scaling each feature to a given range\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "normal = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f52a82a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "m_fit = normal.fit(df[['Marketing Spend']])    # fit, just training model according to data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "83e4e18d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinMaxScaler()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d1473309",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Marketing Spend'] = normal.transform(df[['Marketing Spend']])  # fransform, finally change data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f17efc99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.692617\n",
       "1    0.983359\n",
       "2    0.927985\n",
       "3    0.873136\n",
       "4    0.859438\n",
       "Name: Marketing Spend, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Marketing Spend'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9cbe0b0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Transport</th>\n",
       "      <th>Area</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.692617</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.983359</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>Ctg</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.927985</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Rangpur</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.873136</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.859438</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Rangpur</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Marketing Spend  Administration  Transport     Area     Profit\n",
       "0         0.692617       136897.80  471784.10    Dhaka  192261.83\n",
       "1         0.983359       151377.59  443898.53      Ctg  191792.06\n",
       "2         0.927985       101145.55  407934.54  Rangpur  191050.39\n",
       "3         0.873136       118671.85  383199.62    Dhaka  182901.99\n",
       "4         0.859438        91391.77  366168.42  Rangpur  166187.94"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a45aafb",
   "metadata": {},
   "source": [
    "### shortcut way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cdcb1f41",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Administration'] = normal.fit_transform( df[['Administration']] )   # fit & training at a same time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "45d0cba7",
   "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>Marketing Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Transport</th>\n",
       "      <th>Area</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.692617</td>\n",
       "      <td>0.651744</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.983359</td>\n",
       "      <td>0.761972</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>Ctg</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.927985</td>\n",
       "      <td>0.379579</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Rangpur</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Marketing Spend  Administration  Transport     Area     Profit\n",
       "0         0.692617        0.651744  471784.10    Dhaka  192261.83\n",
       "1         0.983359        0.761972  443898.53      Ctg  191792.06\n",
       "2         0.927985        0.379579  407934.54  Rangpur  191050.39"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45c2c0e7",
   "metadata": {},
   "source": [
    "### we can change range using, feature_range=(from, to)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "47ba19e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "normal = MinMaxScaler(feature_range=(3,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "95902582",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Transport = normal.fit_transform( df[['Transport']] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fad1813e",
   "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>Marketing Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Transport</th>\n",
       "      <th>Area</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.692617</td>\n",
       "      <td>0.651744</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.983359</td>\n",
       "      <td>0.761972</td>\n",
       "      <td>4.881787</td>\n",
       "      <td>Ctg</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.927985</td>\n",
       "      <td>0.379579</td>\n",
       "      <td>4.729327</td>\n",
       "      <td>Rangpur</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.873136</td>\n",
       "      <td>0.512998</td>\n",
       "      <td>4.624470</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.859438</td>\n",
       "      <td>0.305328</td>\n",
       "      <td>4.552271</td>\n",
       "      <td>Rangpur</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Marketing Spend  Administration  Transport     Area     Profit\n",
       "0         0.692617        0.651744   5.000000    Dhaka  192261.83\n",
       "1         0.983359        0.761972   4.881787      Ctg  191792.06\n",
       "2         0.927985        0.379579   4.729327  Rangpur  191050.39\n",
       "3         0.873136        0.512998   4.624470    Dhaka  182901.99\n",
       "4         0.859438        0.305328   4.552271  Rangpur  166187.94"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5a41664",
   "metadata": {},
   "source": [
    "# 2. Standardization feature Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2d1c67ed",
   "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>Marketing Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Transport</th>\n",
       "      <th>Area</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>114523.61</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>Ctg</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Rangpur</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Rangpur</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Marketing Spend  Administration  Transport     Area     Profit\n",
       "0        114523.61       136897.80  471784.10    Dhaka  192261.83\n",
       "1        162597.70       151377.59  443898.53      Ctg  191792.06\n",
       "2        153441.51       101145.55  407934.54  Rangpur  191050.39\n",
       "3        144372.41       118671.85  383199.62    Dhaka  182901.99\n",
       "4        142107.34        91391.77  366168.42  Rangpur  166187.94"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Standardize features by removing the mean and scaling to unit variance\n",
    "\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0a05789b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "standard = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f1aff6ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['Marketing Spend'] = standard.fit_transform( df2[['Marketing Spend']] )\n",
    "df2['Administration'] = standard.fit_transform( df2[['Administration']] )\n",
    "df2['Transport'] = standard.fit_transform( df2[['Transport']] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a9e93c11",
   "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",
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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>Marketing Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Transport</th>\n",
       "      <th>Area</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.897913</td>\n",
       "      <td>0.560753</td>\n",
       "      <td>2.165287</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.955860</td>\n",
       "      <td>1.082807</td>\n",
       "      <td>1.929843</td>\n",
       "      <td>Ctg</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.754364</td>\n",
       "      <td>-0.728257</td>\n",
       "      <td>1.626191</td>\n",
       "      <td>Rangpur</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.554784</td>\n",
       "      <td>-0.096365</td>\n",
       "      <td>1.417348</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.504937</td>\n",
       "      <td>-1.079919</td>\n",
       "      <td>1.273550</td>\n",
       "      <td>Rangpur</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Marketing Spend  Administration  Transport     Area     Profit\n",
       "0         0.897913        0.560753   2.165287    Dhaka  192261.83\n",
       "1         1.955860        1.082807   1.929843      Ctg  191792.06\n",
       "2         1.754364       -0.728257   1.626191  Rangpur  191050.39\n",
       "3         1.554784       -0.096365   1.417348    Dhaka  182901.99\n",
       "4         1.504937       -1.079919   1.273550  Rangpur  166187.94"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f558e517",
   "metadata": {},
   "source": [
    "# 3. Max absolute Value Feature Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0aca9ef9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>Marketing Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Transport</th>\n",
       "      <th>Area</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>114523.61</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>Ctg</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Rangpur</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Rangpur</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Marketing Spend  Administration  Transport     Area     Profit\n",
       "0        114523.61       136897.80  471784.10    Dhaka  192261.83\n",
       "1        162597.70       151377.59  443898.53      Ctg  191792.06\n",
       "2        153441.51       101145.55  407934.54  Rangpur  191050.39\n",
       "3        144372.41       118671.85  383199.62    Dhaka  182901.99\n",
       "4        142107.34        91391.77  366168.42  Rangpur  166187.94"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scale each feature by its maximum absolute value\n",
    "\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "45f113df",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MaxAbsScaler\n",
    "maxabs = MaxAbsScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dd3cb069",
   "metadata": {},
   "outputs": [],
   "source": [
    "df3['Marketing Spend'] = maxabs.fit_transform( df3[['Marketing Spend']] )\n",
    "df3['Administration'] = maxabs.fit_transform( df3[['Administration']] )\n",
    "df3['Transport'] = maxabs.fit_transform( df3[['Transport']] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4e9a9778",
   "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>Marketing Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Transport</th>\n",
       "      <th>Area</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.006049</td>\n",
       "      <td>0.679748</td>\n",
       "      <td>0.004036</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>64926.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.007956</td>\n",
       "      <td>0.634104</td>\n",
       "      <td>0.629768</td>\n",
       "      <td>Rangpur</td>\n",
       "      <td>49490.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.741474</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Ctg</td>\n",
       "      <td>42559.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.003278</td>\n",
       "      <td>0.283298</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>35673.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.640496</td>\n",
       "      <td>0.095749</td>\n",
       "      <td>Ctg</td>\n",
       "      <td>14681.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Marketing Spend  Administration  Transport     Area    Profit\n",
       "45         0.006049        0.679748   0.004036    Dhaka  64926.08\n",
       "46         0.007956        0.634104   0.629768  Rangpur  49490.75\n",
       "47         0.000000        0.741474   0.000000      Ctg  42559.73\n",
       "48         0.003278        0.283298   0.000000    Dhaka  35673.41\n",
       "49         0.000000        0.640496   0.095749      Ctg  14681.40"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7530b0b",
   "metadata": {},
   "source": [
    "# 4. Robust Scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "72babccf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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>Marketing Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Transport</th>\n",
       "      <th>Area</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>114523.61</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>Ctg</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Rangpur</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Rangpur</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Marketing Spend  Administration  Transport     Area     Profit\n",
       "0        114523.61       136897.80  471784.10    Dhaka  192261.83\n",
       "1        162597.70       151377.59  443898.53      Ctg  191792.06\n",
       "2        153441.51       101145.55  407934.54  Rangpur  191050.39\n",
       "3        144372.41       118671.85  383199.62    Dhaka  182901.99\n",
       "4        142107.34        91391.77  366168.42  Rangpur  166187.94"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Robust scaler features using statistics that are robust to outliers\n",
    "\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0df0e620",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import RobustScaler\n",
    "robust = RobustScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "54af904a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df4['Marketing Spend'] = robust.fit_transform( df4[['Marketing Spend']] )\n",
    "df4['Administration'] = robust.fit_transform( df4[['Administration']] )\n",
    "df4['Transport'] = robust.fit_transform( df4[['Transport']] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7d8aaba3",
   "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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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Transport</th>\n",
       "      <th>Area</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.672530</td>\n",
       "      <td>0.345355</td>\n",
       "      <td>1.552016</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.452113</td>\n",
       "      <td>0.697565</td>\n",
       "      <td>1.383714</td>\n",
       "      <td>Ctg</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.303634</td>\n",
       "      <td>-0.524290</td>\n",
       "      <td>1.166654</td>\n",
       "      <td>Rangpur</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.156567</td>\n",
       "      <td>-0.097977</td>\n",
       "      <td>1.017368</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.119836</td>\n",
       "      <td>-0.761543</td>\n",
       "      <td>0.914576</td>\n",
       "      <td>Rangpur</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Marketing Spend  Administration  Transport     Area     Profit\n",
       "0         0.672530        0.345355   1.552016    Dhaka  192261.83\n",
       "1         1.452113        0.697565   1.383714      Ctg  191792.06\n",
       "2         1.303634       -0.524290   1.166654  Rangpur  191050.39\n",
       "3         1.156567       -0.097977   1.017368    Dhaka  182901.99\n",
       "4         1.119836       -0.761543   0.914576  Rangpur  166187.94"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c94ac9b",
   "metadata": {},
   "source": [
    "# Another simple method, divided by max value of that column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "350baed8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Transport</th>\n",
       "      <th>Area</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>114523.61</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>Ctg</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Rangpur</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Rangpur</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Marketing Spend  Administration  Transport     Area     Profit\n",
       "0        114523.61       136897.80  471784.10    Dhaka  192261.83\n",
       "1        162597.70       151377.59  443898.53      Ctg  191792.06\n",
       "2        153441.51       101145.55  407934.54  Rangpur  191050.39\n",
       "3        144372.41       118671.85  383199.62    Dhaka  182901.99\n",
       "4        142107.34        91391.77  366168.42  Rangpur  166187.94"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1d1fc313",
   "metadata": {},
   "outputs": [],
   "source": [
    "df4['Marketing Spend'] = ( df4['Marketing Spend'] ) / max( df4['Marketing Spend'] )\n",
    "df4['Administration'] = ( df4['Administration'] ) / max( df4['Administration'] )\n",
    "df4['Transport'] = ( df4['Transport'] ) / max( df4['Transport'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "caa73e7c",
   "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>Marketing Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Transport</th>\n",
       "      <th>Area</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.449332</td>\n",
       "      <td>0.236848</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.970189</td>\n",
       "      <td>0.478396</td>\n",
       "      <td>0.891559</td>\n",
       "      <td>Ctg</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.870987</td>\n",
       "      <td>-0.359562</td>\n",
       "      <td>0.751702</td>\n",
       "      <td>Rangpur</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.772728</td>\n",
       "      <td>-0.067193</td>\n",
       "      <td>0.655513</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.748187</td>\n",
       "      <td>-0.522273</td>\n",
       "      <td>0.589283</td>\n",
       "      <td>Rangpur</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Marketing Spend  Administration  Transport     Area     Profit\n",
       "0         0.449332        0.236848   1.000000    Dhaka  192261.83\n",
       "1         0.970189        0.478396   0.891559      Ctg  191792.06\n",
       "2         0.870987       -0.359562   0.751702  Rangpur  191050.39\n",
       "3         0.772728       -0.067193   0.655513    Dhaka  182901.99\n",
       "4         0.748187       -0.522273   0.589283  Rangpur  166187.94"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05132afc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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