Profit of Company using LinearRegression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "36db67dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7af4937b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"1000_Companies.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d15e08b6",
   "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>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</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>California</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>Florida</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>New York</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>Florida</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>131876.90</td>\n",
       "      <td>99814.71</td>\n",
       "      <td>362861.36</td>\n",
       "      <td>New York</td>\n",
       "      <td>156991.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>134615.46</td>\n",
       "      <td>147198.87</td>\n",
       "      <td>127716.82</td>\n",
       "      <td>California</td>\n",
       "      <td>156122.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>130298.13</td>\n",
       "      <td>145530.06</td>\n",
       "      <td>323876.68</td>\n",
       "      <td>Florida</td>\n",
       "      <td>155752.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120542.52</td>\n",
       "      <td>148718.95</td>\n",
       "      <td>311613.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>152211.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>123334.88</td>\n",
       "      <td>108679.17</td>\n",
       "      <td>304981.62</td>\n",
       "      <td>California</td>\n",
       "      <td>149759.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   R&D Spend  Administration  Marketing Spend       State     Profit\n",
       "0  165349.20       136897.80        471784.10    New York  192261.83\n",
       "1  162597.70       151377.59        443898.53  California  191792.06\n",
       "2  153441.51       101145.55        407934.54     Florida  191050.39\n",
       "3  144372.41       118671.85        383199.62    New York  182901.99\n",
       "4  142107.34        91391.77        366168.42     Florida  166187.94\n",
       "5  131876.90        99814.71        362861.36    New York  156991.12\n",
       "6  134615.46       147198.87        127716.82  California  156122.51\n",
       "7  130298.13       145530.06        323876.68     Florida  155752.60\n",
       "8  120542.52       148718.95        311613.29    New York  152211.77\n",
       "9  123334.88       108679.17        304981.62  California  149759.96"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "61e7285e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a9db2993",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='State', ylabel='count'>"
      ]
     },
     "execution_count": 31,
     "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.countplot(x=df[\"State\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "76307bd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    344\n",
       "New York      334\n",
       "Florida       322\n",
       "Name: State, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"State\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a77ebe8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   R&D Spend        1000 non-null   float64\n",
      " 1   Administration   1000 non-null   float64\n",
      " 2   Marketing Spend  1000 non-null   float64\n",
      " 3   State            1000 non-null   object \n",
      " 4   Profit           1000 non-null   float64\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 39.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4665ffa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R&D Spend          0\n",
       "Administration     0\n",
       "Marketing Spend    0\n",
       "State              0\n",
       "Profit             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "05fc34ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6dd3f756",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder=OrdinalEncoder()\n",
    "c=['State']\n",
    "df[c]=encoder.fit_transform(df[c])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f1f1d456",
   "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>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>2.0</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>0.0</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>1.0</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>2.0</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>1.0</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   R&D Spend  Administration  Marketing Spend  State     Profit\n",
       "0  165349.20       136897.80        471784.10    2.0  192261.83\n",
       "1  162597.70       151377.59        443898.53    0.0  191792.06\n",
       "2  153441.51       101145.55        407934.54    1.0  191050.39\n",
       "3  144372.41       118671.85        383199.62    2.0  182901.99\n",
       "4  142107.34        91391.77        366168.42    1.0  166187.94"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fb355a95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table id=\"T_d8882\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_d8882_level0_col0\" class=\"col_heading level0 col0\" >R&D Spend</th>\n",
       "      <th id=\"T_d8882_level0_col1\" class=\"col_heading level0 col1\" >Administration</th>\n",
       "      <th id=\"T_d8882_level0_col2\" class=\"col_heading level0 col2\" >Marketing Spend</th>\n",
       "      <th id=\"T_d8882_level0_col3\" class=\"col_heading level0 col3\" >State</th>\n",
       "      <th id=\"T_d8882_level0_col4\" class=\"col_heading level0 col4\" >Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_d8882_level0_row0\" class=\"row_heading level0 row0\" >R&D Spend</th>\n",
       "      <td id=\"T_d8882_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_d8882_row0_col1\" class=\"data row0 col1\" >0.582434</td>\n",
       "      <td id=\"T_d8882_row0_col2\" class=\"data row0 col2\" >0.978407</td>\n",
       "      <td id=\"T_d8882_row0_col3\" class=\"data row0 col3\" >-0.001360</td>\n",
       "      <td id=\"T_d8882_row0_col4\" class=\"data row0 col4\" >0.945245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8882_level0_row1\" class=\"row_heading level0 row1\" >Administration</th>\n",
       "      <td id=\"T_d8882_row1_col0\" class=\"data row1 col0\" >0.582434</td>\n",
       "      <td id=\"T_d8882_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_d8882_row1_col2\" class=\"data row1 col2\" >0.520465</td>\n",
       "      <td id=\"T_d8882_row1_col3\" class=\"data row1 col3\" >-0.018386</td>\n",
       "      <td id=\"T_d8882_row1_col4\" class=\"data row1 col4\" >0.741560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8882_level0_row2\" class=\"row_heading level0 row2\" >Marketing Spend</th>\n",
       "      <td id=\"T_d8882_row2_col0\" class=\"data row2 col0\" >0.978407</td>\n",
       "      <td id=\"T_d8882_row2_col1\" class=\"data row2 col1\" >0.520465</td>\n",
       "      <td id=\"T_d8882_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_d8882_row2_col3\" class=\"data row2 col3\" >-0.001420</td>\n",
       "      <td id=\"T_d8882_row2_col4\" class=\"data row2 col4\" >0.917270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8882_level0_row3\" class=\"row_heading level0 row3\" >State</th>\n",
       "      <td id=\"T_d8882_row3_col0\" class=\"data row3 col0\" >-0.001360</td>\n",
       "      <td id=\"T_d8882_row3_col1\" class=\"data row3 col1\" >-0.018386</td>\n",
       "      <td id=\"T_d8882_row3_col2\" class=\"data row3 col2\" >-0.001420</td>\n",
       "      <td id=\"T_d8882_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_d8882_row3_col4\" class=\"data row3 col4\" >-0.005718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8882_level0_row4\" class=\"row_heading level0 row4\" >Profit</th>\n",
       "      <td id=\"T_d8882_row4_col0\" class=\"data row4 col0\" >0.945245</td>\n",
       "      <td id=\"T_d8882_row4_col1\" class=\"data row4 col1\" >0.741560</td>\n",
       "      <td id=\"T_d8882_row4_col2\" class=\"data row4 col2\" >0.917270</td>\n",
       "      <td id=\"T_d8882_row4_col3\" class=\"data row4 col3\" >-0.005718</td>\n",
       "      <td id=\"T_d8882_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x245530ab880>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr=df.corr()\n",
    "corr.style.background_gradient()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "150e3181",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df[[\"R&D Spend\",\"Administration\",\"Marketing Spend\",\"State\"]]\n",
    "y=df[\"Profit\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "272335a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ee0cf483",
   "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>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>38385.0</td>\n",
       "      <td>116125.9672</td>\n",
       "      <td>142843.00150</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>125610.0</td>\n",
       "      <td>129007.7526</td>\n",
       "      <td>311143.87720</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>101322.0</td>\n",
       "      <td>125420.7901</td>\n",
       "      <td>264280.11490</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>12914.0</td>\n",
       "      <td>112364.2939</td>\n",
       "      <td>93696.63744</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>109909.0</td>\n",
       "      <td>126688.9574</td>\n",
       "      <td>280848.75480</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>110391.0</td>\n",
       "      <td>126760.1413</td>\n",
       "      <td>281778.77510</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>53078.0</td>\n",
       "      <td>118295.8964</td>\n",
       "      <td>171193.18510</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>22876.0</td>\n",
       "      <td>113835.5275</td>\n",
       "      <td>112918.34360</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>66766.0</td>\n",
       "      <td>120317.4026</td>\n",
       "      <td>197604.21850</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>112108.0</td>\n",
       "      <td>127013.7157</td>\n",
       "      <td>285091.73130</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>800 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     R&D Spend  Administration  Marketing Spend  State\n",
       "687    38385.0     116125.9672     142843.00150    0.0\n",
       "500   125610.0     129007.7526     311143.87720    1.0\n",
       "332   101322.0     125420.7901     264280.11490    0.0\n",
       "979    12914.0     112364.2939      93696.63744    0.0\n",
       "817   109909.0     126688.9574     280848.75480    2.0\n",
       "..         ...             ...              ...    ...\n",
       "835   110391.0     126760.1413     281778.77510    0.0\n",
       "192    53078.0     118295.8964     171193.18510    0.0\n",
       "629    22876.0     113835.5275     112918.34360    0.0\n",
       "559    66766.0     120317.4026     197604.21850    0.0\n",
       "684   112108.0     127013.7157     285091.73130    0.0\n",
       "\n",
       "[800 rows x 4 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "53373c54",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e28f89a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter',x='R&D Spend',y='Profit',color='yellow')\n",
    "plt.title(\"Expenditure Vs Profit\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1b3d3533",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter',x='Marketing Spend',y='Profit',color='orange')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7fe3e613",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter',x='State',y='Profit',color='green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f24abc02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor=LinearRegression()\n",
    "regressor.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "54a081c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 89624.88499983  88261.44060358  94728.57723845 176034.49990954\n",
      "  83234.84214495 110393.04058199 131831.18880607  91307.52461765\n",
      " 164414.26754671  53585.35570743  66786.11950078 150921.89704628\n",
      " 126746.78121459  59174.341725   177341.80724482  75139.98127599\n",
      " 118605.9491146  164403.24374731 171336.34862472 181885.06087421\n",
      " 118762.26624783  85504.5201526  181345.84072646  83979.76201107\n",
      " 104839.00200137 101067.00608604  53722.31401539  56706.45386115\n",
      "  68732.18107141 209811.34272095 121136.34391499 111557.54775873\n",
      " 101309.70393418 138315.39958078  63806.10546728 108690.79660343\n",
      " 185830.38310114 171796.58186122 174997.97325294 118029.33822338\n",
      "  96553.52264734 165281.03076793 107557.31012676  50557.29080259\n",
      " 116871.82636073  58469.02312669 158771.35516035  78902.17488961\n",
      " 160082.45834529 131494.74562654 185233.67354482 174962.79318566\n",
      "  93579.64586335  78164.61392891 180561.94494338  84821.87449117\n",
      " 143256.92009399 170435.21728058  84188.13777206 105128.89266269\n",
      " 142016.17711741  52352.69882961 141673.45079288 138995.34025103\n",
      "  98128.17857189 113945.01159246 126574.18191679 151953.63890674\n",
      "  58689.41162929 174639.30763053 124455.78288593 168894.28704729\n",
      "  91639.09627154 155988.19861521  84141.93382092  77701.26598966\n",
      " 120771.67191431  93202.83024556 138851.31833079 143435.03120172\n",
      " 171502.27009022 139913.58651319 106191.16076064 155470.92422805\n",
      " 139849.10179055 109994.04650482  69508.71795972  87971.28828425\n",
      " 139964.21158468 148297.48006333 157734.33411496  58369.48912141\n",
      "  93529.74798521 113004.58676514  56381.2521726  107236.26789414\n",
      " 147708.49291933 151962.21946809 167625.09747992 118582.41810675\n",
      " 121120.89899524 139386.24824009 157345.63664385 122319.5973879\n",
      "  86914.16840487 105151.69639275  95157.47229309 178205.73461428\n",
      " 181770.2127381  109652.54183621 165082.82081555 167634.66689263\n",
      " 157820.13925373 174599.34276711 170025.19931955  52139.17476133\n",
      " 176766.05450263 104585.0186007   82545.09933122 138709.60895693\n",
      " 144630.66101185 162033.66822962 170016.12436942 120808.56812142\n",
      " 159235.56115351 110046.51849225 169491.36019494  60596.99166217\n",
      " 158866.23532304 157998.74491915 174328.19840197 156112.61616403\n",
      " 103547.63396397  85458.91251291 141434.05433182 165734.80936825\n",
      " 121539.62832572 170562.43564135 100864.86949721  82457.94166529\n",
      " 178471.00347238 101623.88174527  70331.4587885   90072.65723255\n",
      "  61072.22154665  68736.34043862  73136.92467492 176742.02902153\n",
      "  90064.57113377 128998.71699737  93032.57243205  88583.13235127\n",
      " 172664.56674901  60729.9896911  169370.01131309 166278.94500381\n",
      " 165779.92245025 102529.4900122  181410.55817988  74063.62063787\n",
      "  91101.59217916 135622.46937921  64747.25748795  71599.65942006\n",
      "  60428.68269046 184641.61773385 176640.28450044 158725.38392932\n",
      " 141178.22408898 154429.11848164  58374.50655107  90978.39637071\n",
      " 152703.57620466 168752.21397631  72309.26825255 116422.20721207\n",
      "  80448.38430783 149647.19558454 116627.64518798 130022.82306379\n",
      " 174888.14264374 293492.48984534 146100.99822157 150464.91899219\n",
      "  86467.61783875  70328.52100342  70458.08685528  68869.46933558\n",
      " 120487.1622868   89500.33665356 167178.54698757 125346.44063256\n",
      "  67033.96565195 140708.04064616 118376.48566826 165726.35967814\n",
      " 169009.62956152 146839.78084885 141215.12028542 108907.75284131]\n"
     ]
    }
   ],
   "source": [
    "y_pred=regressor.predict(x_test)\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4a928161",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.27572958e-01,  8.44490415e-01,  1.06639226e-01, -3.54869857e+02])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fa241101",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-51144.72642572175\n"
     ]
    }
   ],
   "source": [
    "print(regressor.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1e56e05e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "91.12595959622044"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r=r2_score(y_test,y_pred)\n",
    "r*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "62473489",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Profit'>"
      ]
     },
     "execution_count": 45,
     "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.regplot(data=df,x=y_test,y=y_pred,color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f304ec0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}