encoding_techniques.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3458d5da",
   "metadata": {},
   "source": [
    "#  Encoding\n",
    "In machine learning, encoding refers to the process of converting categorical data into a numerical representation that can be easily processed by machine learning models.\n",
    "- Enable processing of categorical data: Machine learning models typically work with numerical data, so categorical data must be encoded into a numerical representation before it can be processed by the model.\n",
    "- Improve model performance: Encoding categorical data can improve the performance of machine learning models. For example, one-hot encoding can help prevent the model from assuming a false order or relationship between categories, while ordinal encoding can help preserve the relationship between categories. Choosing the appropriate encoding method can make a significant difference in the accuracy of a machine learning model.\n",
    "\n",
    "- Overall, encoding categorical data is an important step in machine learning that helps enable processing of categorical data, preserves information, avoids bias, and improves model performance.\n",
    "\n",
    "\n",
    "## Types of Encoding\n",
    "\n",
    "  -  Without Use Any Encoding Techniques\n",
    "  -  Label Encoding \n",
    "  -  One-Hot Encoding\n",
    "  -  Ordinal Encoding\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "66fa87f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all packegs \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0a2e8814",
   "metadata": {},
   "outputs": [],
   "source": [
    "#load Dataset\n",
    "data = pd.read_csv('supershops.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c4a4b69b",
   "metadata": {},
   "outputs": [
    {
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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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show data\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fa17ab78",
   "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": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "922bb226",
   "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": [
    "#check null values\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "032a8ce5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#handel missing values with mean value\n",
    "missing = data.Transport.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f7764b38",
   "metadata": {},
   "outputs": [],
   "source": [
    "#fillna\n",
    "data.Transport = data.Transport.fillna(missing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7427dfb8",
   "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": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c9a7f658",
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check missing valus after fill values with mean\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "43615c7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = data.copy()\n",
    "data3 = data.copy()\n",
    "data4 = data.copy()\n",
    "data5 = data.copy()\n",
    "data6 = data.copy()\n",
    "data7 = data.copy()\n",
    "data8 = data.copy()\n",
    "data9 = data.copy()\n",
    "data10 = data.copy()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca61b150",
   "metadata": {},
   "source": [
    "# LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "51c99477",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "822f14d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "data2.Area = le.fit_transform(data2[['Area']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "538cd137",
   "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>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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af738a01",
   "metadata": {},
   "source": [
    "# using loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "acf7a6e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in data3.columns:\n",
    "    if data3[col].dtype == np.number:\n",
    "        continue\n",
    "    else:\n",
    "        data3[col] = le.fit_transform(data3[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e600b924",
   "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",
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       "      <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",
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      ],
      "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a9f9b968",
   "metadata": {},
   "outputs": [
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       "      <th>Marketing Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Transport</th>\n",
       "      <th>Area</th>\n",
       "      <th>Profit</th>\n",
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       "    <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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data4.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14526aa4",
   "metadata": {},
   "source": [
    "# One hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bbdd7377",
   "metadata": {},
   "outputs": [
    {
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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",
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       "      <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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "522e23ae",
   "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",
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       "    }\n",
       "\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>Dhaka</th>\n",
       "      <th>Rangpur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy = pd.get_dummies(data4['Area'],drop_first=True)\n",
    "dummy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6253f5c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "data4 = pd.concat([data4, dummy], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "69a0bf0c",
   "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",
       "      <th>Dhaka</th>\n",
       "      <th>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>Dhaka</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>Ctg</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>Rangpur</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>Dhaka</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>Rangpur</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     Area     Profit  Dhaka  \\\n",
       "0        114523.61       136897.80  471784.10    Dhaka  192261.83      1   \n",
       "1        162597.70       151377.59  443898.53      Ctg  191792.06      0   \n",
       "2        153441.51       101145.55  407934.54  Rangpur  191050.39      0   \n",
       "3        144372.41       118671.85  383199.62    Dhaka  182901.99      1   \n",
       "4        142107.34        91391.77  366168.42  Rangpur  166187.94      0   \n",
       "\n",
       "   Rangpur  \n",
       "0        0  \n",
       "1        0  \n",
       "2        1  \n",
       "3        0  \n",
       "4        1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "96b6f3e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#loop\n",
    "for col in data5.columns:\n",
    "    if data5[col].dtype == np.number:\n",
    "        continue\n",
    "    one = pd.get_dummies(data5[col], drop_first=True,prefix='Area')\n",
    "    data5.drop(data5[[col]], axis=1, inplace=True)\n",
    "    data5 = pd.concat([data5, one], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "50807a67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>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",
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       "      <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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data5.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5687d98e",
   "metadata": {},
   "source": [
    "# Ordinal Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "140bdc75",
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data6.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "548207eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ef658e2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "order = data6.Area.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b670d0cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Dhaka', 'Ctg', 'Rangpur', 'Dhaka', 'Rangpur', 'Dhaka', 'Ctg',\n",
       "       'Rangpur', 'Dhaka', 'Ctg', 'Rangpur', 'Ctg', 'Rangpur', 'Ctg',\n",
       "       'Rangpur', 'Dhaka', 'Ctg', 'Dhaka', 'Rangpur', 'Dhaka', 'Ctg',\n",
       "       'Dhaka', 'Rangpur', 'Rangpur', 'Dhaka', 'Ctg', 'Rangpur', 'Dhaka',\n",
       "       'Rangpur', 'Dhaka', 'Rangpur', 'Dhaka', 'Ctg', 'Rangpur', 'Ctg',\n",
       "       'Dhaka', 'Rangpur', 'Ctg', 'Dhaka', 'Ctg', 'Ctg', 'Rangpur', 'Ctg',\n",
       "       'Dhaka', 'Ctg', 'Dhaka', 'Rangpur', 'Ctg', 'Dhaka', 'Ctg'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0bd02d8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "order = data6.Area.unique() # order = ['Dhaka','Rangpur','Ctg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "da4613cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Dhaka', 'Ctg', 'Rangpur'], dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7f195c1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ordinal = OrdinalEncoder(categories=[order])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "abf914c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrdinalEncoder(categories=[array(['Dhaka', 'Ctg', 'Rangpur'], dtype=object)])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordinal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2144485e",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoded = ordinal.fit_transform(data6[['Area']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dcafd7d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.],\n",
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       "       [1.],\n",
       "       [0.],\n",
       "       [1.]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c2bb9448",
   "metadata": {},
   "outputs": [],
   "source": [
    "data6.Area = encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "486223e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
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       "      <th>0</th>\n",
       "      <td>114523.61</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>192261.83</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>1.0</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>2.0</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>0.0</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>2.0</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   0.0  192261.83\n",
       "1        162597.70       151377.59  443898.53   1.0  191792.06\n",
       "2        153441.51       101145.55  407934.54   2.0  191050.39\n",
       "3        144372.41       118671.85  383199.62   0.0  182901.99\n",
       "4        142107.34        91391.77  366168.42   2.0  166187.94"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data6.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "337b0921",
   "metadata": {},
   "source": [
    "# Without Use Any Encoding Techniques\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d0a8ea64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <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": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6acc718c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data7.Area = data7.Area.replace(['Dhaka','Ctg','Rangpur'], [1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "23c42194",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Transport</th>\n",
       "      <th>Area</th>\n",
       "      <th>Profit</th>\n",
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       "      <th>0</th>\n",
       "      <td>114523.61</td>\n",
       "      <td>136897.80</td>\n",
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       "      <td>1</td>\n",
       "      <td>192261.83</td>\n",
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       "      <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>3</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>3</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     2  191792.06\n",
       "2        153441.51       101145.55  407934.54     3  191050.39\n",
       "3        144372.41       118671.85  383199.62     1  182901.99\n",
       "4        142107.34        91391.77  366168.42     3  166187.94"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data7.head()"
   ]
  }
 ],
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