Hash_Encoding_Technique.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "39ad8405",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b6be1a17",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('online_profit.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e94eb1e8",
   "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>Dhaka</td>\n",
       "      <td>192261.83</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>Ctg</td>\n",
       "      <td>191792.06</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
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       "      <td>NaN</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>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",
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      "text/plain": [
       "   Marketing Spend  Administration  Transport     Area     Profit\n",
       "0        114523.61       136897.80  471784.10    Dhaka  192261.83\n",
       "1              NaN       151377.59  443898.53      Ctg  191792.06\n",
       "2        153441.51       101145.55  407934.54      NaN  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": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6bb56c86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Marketing Spend    2\n",
       "Administration     0\n",
       "Transport          0\n",
       "Area               3\n",
       "Profit             1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "58b02cad",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean = df['Marketing Spend'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8c133697",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70691.35312500001"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8ccd7c05",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Marketing Spend'] = df['Marketing Spend'].fillna(mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a793c2eb",
   "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",
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       "      <td>114523.610000</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>192261.83</td>\n",
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       "      <th>1</th>\n",
       "      <td>70691.353125</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",
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       "      <td>Rangpur</td>\n",
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      ],
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       "   Marketing Spend  Administration  Transport     Area     Profit\n",
       "0    114523.610000       136897.80  471784.10    Dhaka  192261.83\n",
       "1     70691.353125       151377.59  443898.53      Ctg  191792.06\n",
       "2    153441.510000       101145.55  407934.54      NaN  191050.39\n",
       "3    144372.410000       118671.85  383199.62    Dhaka  182901.99\n",
       "4    142107.340000        91391.77  366168.42  Rangpur  166187.94"
      ]
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     "execution_count": 8,
     "metadata": {},
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1178e1e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Area'] = df['Area'].fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1d7d82cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "median = df['Profit'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5f05fce7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "107404.34"
      ]
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "18a0ab47",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Profit'] = df['Profit'].fillna(median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "293b3fc7",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Marketing Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Transport</th>\n",
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       "      <td>Dhaka</td>\n",
       "      <td>192261.83</td>\n",
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       "      <th>1</th>\n",
       "      <td>70691.353125</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>Ctg</td>\n",
       "      <td>191792.06</td>\n",
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       "      <th>2</th>\n",
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       "      <td>Dhaka</td>\n",
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       "      <td>Rangpur</td>\n",
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       "   Marketing Spend  Administration  Transport     Area     Profit\n",
       "0    114523.610000       136897.80  471784.10    Dhaka  192261.83\n",
       "1     70691.353125       151377.59  443898.53      Ctg  191792.06\n",
       "2    153441.510000       101145.55  407934.54      Ctg  191050.39\n",
       "3    144372.410000       118671.85  383199.62    Dhaka  182901.99\n",
       "4    142107.340000        91391.77  366168.42  Rangpur  166187.94"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "87e0ba2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import category_encoders as ce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1decef40",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoders = ce.HashingEncoder(cols='Area',n_components=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "61c9052e",
   "metadata": {},
   "outputs": [],
   "source": [
    "enco = encoders.fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "35fb8fd9",
   "metadata": {},
   "outputs": [
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       "   col_0  col_1  col_2  Marketing Spend  Administration  Transport     Profit\n",
       "0      0      1      0    114523.610000       136897.80  471784.10  192261.83\n",
       "1      0      0      1     70691.353125       151377.59  443898.53  191792.06\n",
       "2      0      0      1    153441.510000       101145.55  407934.54  191050.39\n",
       "3      0      1      0    144372.410000       118671.85  383199.62  182901.99\n",
       "4      1      0      0    142107.340000        91391.77  366168.42  166187.94"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "enco.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b522b16c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = enco"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f42a3e52",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   col_0  col_1  col_2  Marketing Spend  Administration  Transport     Profit\n",
       "0      0      1      0    114523.610000       136897.80  471784.10  192261.83\n",
       "1      0      0      1     70691.353125       151377.59  443898.53  191792.06\n",
       "2      0      0      1    153441.510000       101145.55  407934.54  191050.39\n",
       "3      0      1      0    144372.410000       118671.85  383199.62  182901.99\n",
       "4      1      0      0    142107.340000        91391.77  366168.42  166187.94"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8fdfba04",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop(['Profit'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bf2963b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['Profit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d75211f4",
   "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>col_0</th>\n",
       "      <th>col_1</th>\n",
       "      <th>col_2</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Transport</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",
       "      <td>114523.610000</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>70691.353125</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>153441.510000</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>144372.410000</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>142107.340000</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col_0  col_1  col_2  Marketing Spend  Administration  Transport\n",
       "0      0      1      0    114523.610000       136897.80  471784.10\n",
       "1      0      0      1     70691.353125       151377.59  443898.53\n",
       "2      0      0      1    153441.510000       101145.55  407934.54\n",
       "3      0      1      0    144372.410000       118671.85  383199.62\n",
       "4      1      0      0    142107.340000        91391.77  366168.42"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a3c7dec2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x173fd65c220>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Marketing Spend and Administration Line\")\n",
    "plt.xlabel(\"Marketing Spend\")\n",
    "plt.ylabel(\"Administration\")\n",
    "plt.scatter(df['Marketing Spend'],df['Administration'],marker=\"D\",color=\"Green\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "74d99fb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Marketing Spend', ylabel='Count'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['Marketing Spend'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7a6d3489",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x173f3537ac0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Transport and Administration Line\")\n",
    "plt.xlabel(\"Transport\")\n",
    "plt.ylabel(\"Area\")\n",
    "plt.scatter(df['Transport'],df['Administration'],marker=\"+\",color=\"Red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "734ca00c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Administration', ylabel='Count'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['Administration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4733aba5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Transport', ylabel='Count'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['Transport'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4213acca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x173f36ab5b0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Marketing Spend and Profit Line\")\n",
    "plt.xlabel(\"Marketing Spend\")\n",
    "plt.ylabel(\"Profit\")\n",
    "plt.scatter(df['Marketing Spend'],df['Profit'],marker=\"s\",color=\"Blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0c57d299",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Profit', ylabel='Count'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['Profit'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "607f65f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c6468570",
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain, xtest, ytrain, ytest = train_test_split(x,y,train_size=.70,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "af45c3cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35, 6)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "52c83e55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 6)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "acad943e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35,)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytrain.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d95cfccf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15,)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "db416011",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8c33ae18",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5166621c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "61e2a413",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13    134307.35\n",
       "39     81005.76\n",
       "30     99937.59\n",
       "45     64926.08\n",
       "17    125370.37\n",
       "48     35673.41\n",
       "26    105733.54\n",
       "25    107404.34\n",
       "32     97427.84\n",
       "19    122776.86\n",
       "12    141585.52\n",
       "4     166187.94\n",
       "37     89949.14\n",
       "8     152211.77\n",
       "3     182901.99\n",
       "Name: Profit, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f60101c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.865858970562524"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(xtest.values,ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "140cea98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2.32540305e+03, -1.26402403e+03,  3.58942709e+03,  5.58987738e-01,\n",
       "        1.65545425e-01,  1.52238111e-01])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e6b1760e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17127.450653436143"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "13c6780e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([174367.04513447])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[0,1,0,114523.61,136897.80,471784.10]])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
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