feature-engineering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "74d85345",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df = pd.read_csv('supershop.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "233c7745",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dd258fa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "02fd90f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50 entries, 0 to 49\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Marketing Spend  50 non-null     float64\n",
      " 1   Administration   50 non-null     float64\n",
      " 2   Transport        49 non-null     float64\n",
      " 3   Area             50 non-null     object \n",
      " 4   Profit           50 non-null     float64\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 2.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()              # data frame contains -> object (string), float64, int64, bool, datatime64, category datatypies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1517592",
   "metadata": {},
   "source": [
    "### check null value -> binary checking -> True if yes, else False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6eaddd40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Marketing Spend    0\n",
       "Administration     0\n",
       "Transport          1\n",
       "Area               0\n",
       "Profit             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()      "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3005f06",
   "metadata": {},
   "source": [
    "### Handle NaN value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "db9ff37e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "215331.7324489796"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.isnull()\n",
    "\n",
    "df.Transport.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a3b26192",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "214634.81"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Transport.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c43faa95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "471784.1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Transport.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "da951cc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Transport.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4875da26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here I want to replase by mean, so\n",
    "\n",
    "transport_mean = df.Transport.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "abeec3dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "215331.7324489796"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transport_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "887fd6c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Transport = df.Transport.fillna(transport_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cd9923aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Marketing Spend    0\n",
       "Administration     0\n",
       "Transport          0\n",
       "Area               0\n",
       "Profit             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1836d6bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "215331.7324489796"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Transport[19]  # before value was NaN and now replaced by mean() of Transport column :)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0632dbf",
   "metadata": {},
   "source": [
    "# Now, Without Encoding Algorithm Technique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7060752d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50 entries, 0 to 49\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Marketing Spend  50 non-null     float64\n",
      " 1   Administration   50 non-null     float64\n",
      " 2   Transport        50 non-null     float64\n",
      " 3   Area             50 non-null     object \n",
      " 4   Profit           50 non-null     float64\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 2.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fbdd9c5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Dhaka', 'Ctg', 'Rangpur'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Area.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0e306964",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Area = df.Area.replace(['Dhaka', 'Ctg', 'Rangpur'], [3,2,1])  # python replace function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8b9b6d35",
   "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>114523.61</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>3</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>2</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>1</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>3</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>1</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     3  192261.83\n",
       "1        162597.70       151377.59  443898.53     2  191792.06\n",
       "2        153441.51       101145.55  407934.54     1  191050.39\n",
       "3        144372.41       118671.85  383199.62     3  182901.99\n",
       "4        142107.34        91391.77  366168.42     1  166187.94"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "63e17e4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50 entries, 0 to 49\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Marketing Spend  50 non-null     float64\n",
      " 1   Administration   50 non-null     float64\n",
      " 2   Transport        50 non-null     float64\n",
      " 3   Area             50 non-null     int64  \n",
      " 4   Profit           50 non-null     float64\n",
      "dtypes: float64(4), int64(1)\n",
      "memory usage: 2.1 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ae72abc",
   "metadata": {},
   "source": [
    "# Label Encoding Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "eca75a18",
   "metadata": {},
   "outputs": [
    {
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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>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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('supershop.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ddc9b156",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "label = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "23f11ee2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Area'] = label.fit_transform(df['Area'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3b74f7d1",
   "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>114523.61</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>1</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>0</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>2</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>1</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>2</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     1  192261.83\n",
       "1        162597.70       151377.59  443898.53     0  191792.06\n",
       "2        153441.51       101145.55  407934.54     2  191050.39\n",
       "3        144372.41       118671.85  383199.62     1  182901.99\n",
       "4        142107.34        91391.77  366168.42     2  166187.94"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fa3e2b0",
   "metadata": {},
   "source": [
    "## If more than one columns are categorical, so we run a loop\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e76201cb",
   "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>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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('supershop.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cf727513",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64\n"
     ]
    }
   ],
   "source": [
    "print( df.Transport.dtype )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "06edbee0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "object\n"
     ]
    }
   ],
   "source": [
    "print( df.Area.dtype )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "38aa530b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yes  Area  Column number :  4\n"
     ]
    }
   ],
   "source": [
    "i = 0  # just loop count\n",
    "for column in df.columns:\n",
    "    i += 1\n",
    "    if df[column].dtype != 'object':\n",
    "        continue\n",
    "    else:\n",
    "        df[column] = label.fit_transform(df[column])   # here we fit & transform our model\n",
    "        print('Yes ', column, ' Column number : ', i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7813ad64",
   "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>1</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>0</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>2</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>1</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>2</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     1  192261.83\n",
       "1        162597.70       151377.59  443898.53     0  191792.06\n",
       "2        153441.51       101145.55  407934.54     2  191050.39\n",
       "3        144372.41       118671.85  383199.62     1  182901.99\n",
       "4        142107.34        91391.77  366168.42     2  166187.94"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6a061bd",
   "metadata": {},
   "source": [
    "## Another way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ca29492e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('supershop.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "48a59f52",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.core.dtypes.common import is_numeric_dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ba04fe36",
   "metadata": {},
   "outputs": [],
   "source": [
    "for column in df.columns:\n",
    "    if is_numeric_dtype(df[column]):\n",
    "        continue\n",
    "    else:\n",
    "        df[column] = label.fit_transform(df[column])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2d3fdd03",
   "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>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>1</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>0</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>2</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>1</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>2</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     1  192261.83\n",
       "1        162597.70       151377.59  443898.53     0  191792.06\n",
       "2        153441.51       101145.55  407934.54     2  191050.39\n",
       "3        144372.41       118671.85  383199.62     1  182901.99\n",
       "4        142107.34        91391.77  366168.42     2  166187.94"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbca6f7e",
   "metadata": {},
   "source": [
    "# One Hot Encoder Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e4290d54",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('supershop.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1aa60b85",
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy = pd.get_dummies(df['Area'])  # create dummy variables using pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c90e6120",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ctg</th>\n",
       "      <th>Dhaka</th>\n",
       "      <th>Rangpur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ctg  Dhaka  Rangpur\n",
       "0    0      1        0\n",
       "1    1      0        0\n",
       "2    0      0        1\n",
       "3    0      1        0\n",
       "4    0      0        1"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "77950111",
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy = pd.get_dummies(df['Area'], drop_first=True)  # we can drop 1 row, bz we know what is the value of third one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b2911522",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dhaka</th>\n",
       "      <th>Rangpur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Dhaka  Rangpur\n",
       "0      1        0\n",
       "1      0        0\n",
       "2      0        1\n",
       "3      1        0\n",
       "4      0        1"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "af472831",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = df.drop('Area', axis=1)    # axis=1 for columns; and axis=0 for rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "14e76e27",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Transport</th>\n",
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       "    </tr>\n",
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       "      <td>443898.53</td>\n",
       "      <td>191792.06</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</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>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>166187.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Marketing Spend  Administration  Transport     Profit\n",
       "0        114523.61       136897.80  471784.10  192261.83\n",
       "1        162597.70       151377.59  443898.53  191792.06\n",
       "2        153441.51       101145.55  407934.54  191050.39\n",
       "3        144372.41       118671.85  383199.62  182901.99\n",
       "4        142107.34        91391.77  366168.42  166187.94"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "76c1cbc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# now concat data-frames\n",
    "df = pd.concat([new_df, dummy], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a211f7d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Transport</th>\n",
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       "      <th>Dhaka</th>\n",
       "      <th>Rangpur</th>\n",
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       "      <td>1</td>\n",
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       "      <th>4</th>\n",
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       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>166187.94</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Marketing Spend  Administration  Transport     Profit  Dhaka  Rangpur\n",
       "0        114523.61       136897.80  471784.10  192261.83      1        0\n",
       "1        162597.70       151377.59  443898.53  191792.06      0        0\n",
       "2        153441.51       101145.55  407934.54  191050.39      0        1\n",
       "3        144372.41       118671.85  383199.62  182901.99      1        0\n",
       "4        142107.34        91391.77  366168.42  166187.94      0        1"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f0f7b6f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# now our dependent and independents are\n",
    "y = df['Profit']\n",
    "x = df.drop('Profit', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "653ecbd9",
   "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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "592be59d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Transport</th>\n",
       "      <th>Dhaka</th>\n",
       "      <th>Rangpur</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Marketing Spend  Administration  Transport  Dhaka  Rangpur\n",
       "0        114523.61       136897.80  471784.10      1        0\n",
       "1        162597.70       151377.59  443898.53      0        0\n",
       "2        153441.51       101145.55  407934.54      0        1\n",
       "3        144372.41       118671.85  383199.62      1        0\n",
       "4        142107.34        91391.77  366168.42      0        1"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "355e1fa5",
   "metadata": {},
   "source": [
    "# One Hot Encoder Using Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "bb8fe59f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('supershop.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "bf51070c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a list with all string/ nominal columns; if more than one -> cols = ['Area', 'Price', 'Somethings']\n",
    "cols = ['Area'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "883b5707",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in cols:\n",
    "    one_hot = pd.get_dummies(df[col], drop_first=True, prefix=col)\n",
    "    df = pd.concat([df, one_hot], axis=1).drop(col, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "36b4ea77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Transport</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Area_Dhaka</th>\n",
       "      <th>Area_Rangpur</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>192261.83</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>191792.06</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>191050.39</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>182901.99</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>166187.94</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Marketing Spend  Administration  Transport     Profit  Area_Dhaka  \\\n",
       "0        114523.61       136897.80  471784.10  192261.83           1   \n",
       "1        162597.70       151377.59  443898.53  191792.06           0   \n",
       "2        153441.51       101145.55  407934.54  191050.39           0   \n",
       "3        144372.41       118671.85  383199.62  182901.99           1   \n",
       "4        142107.34        91391.77  366168.42  166187.94           0   \n",
       "\n",
       "   Area_Rangpur  \n",
       "0             0  \n",
       "1             0  \n",
       "2             1  \n",
       "3             0  \n",
       "4             1  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "702e9fa1",
   "metadata": {},
   "source": [
    "# Ordinal Encoder Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b47ac213",
   "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": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('supershop.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6bb04154",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Dhaka', 'Ctg', 'Rangpur'], dtype=object)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Area.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2069c815",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "66f892ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "city = ['Dhaka', 'Ctg', 'Rangpur']\n",
    "\n",
    "ordinal = OrdinalEncoder(categories=[city])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "df960093",
   "metadata": {},
   "outputs": [],
   "source": [
    "encodedValues = ordinal.fit_transform(df[['Area']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "dff4347e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [2.],\n",
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       "       [1.],\n",
       "       [2.],\n",
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       "       [2.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [0.],\n",
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       "       [0.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [1.]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encodedValues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "9c3746b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_area = pd.DataFrame(encodedValues, columns=['Area'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e7a01c5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Area\n",
       "0   0.0\n",
       "1   1.0\n",
       "2   2.0\n",
       "3   0.0\n",
       "4   2.0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_area.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "bf494519",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now concat and drop \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "fb078aef",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = df.drop('Area', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "56a7f4db",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Transport</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>471784.10</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>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>191050.39</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</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>166187.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Marketing Spend  Administration  Transport     Profit\n",
       "0        114523.61       136897.80  471784.10  192261.83\n",
       "1        162597.70       151377.59  443898.53  191792.06\n",
       "2        153441.51       101145.55  407934.54  191050.39\n",
       "3        144372.41       118671.85  383199.62  182901.99\n",
       "4        142107.34        91391.77  366168.42  166187.94"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "62177f06",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([new_df, new_area], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "5381a229",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
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       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>182901.99</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>166187.94</td>\n",
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      "text/plain": [
       "   Marketing Spend  Administration  Transport     Profit  Area\n",
       "0        114523.61       136897.80  471784.10  192261.83   0.0\n",
       "1        162597.70       151377.59  443898.53  191792.06   1.0\n",
       "2        153441.51       101145.55  407934.54  191050.39   2.0\n",
       "3        144372.41       118671.85  383199.62  182901.99   0.0\n",
       "4        142107.34        91391.77  366168.42  166187.94   2.0"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dff82468",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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