LinearRegression Task FINAL.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Car_Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ritz</td>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sx4</td>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ciaz</td>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wagon r</td>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>swift</td>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_Name  Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type  \\\n",
       "0     ritz  2014           3.35           5.59       27000    Petrol   \n",
       "1      sx4  2013           4.75           9.54       43000    Diesel   \n",
       "2     ciaz  2017           7.25           9.85        6900    Petrol   \n",
       "3  wagon r  2011           2.85           4.15        5200    Petrol   \n",
       "4    swift  2014           4.60           6.87       42450    Diesel   \n",
       "\n",
       "  Seller_Type Transmission  Owner  \n",
       "0      Dealer       Manual      0  \n",
       "1      Dealer       Manual      0  \n",
       "2      Dealer       Manual      0  \n",
       "3      Dealer       Manual      0  \n",
       "4      Dealer       Manual      0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('car.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Car_Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>city</td>\n",
       "      <td>2016</td>\n",
       "      <td>9.50</td>\n",
       "      <td>11.6</td>\n",
       "      <td>33988</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>brio</td>\n",
       "      <td>2015</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.9</td>\n",
       "      <td>60000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>city</td>\n",
       "      <td>2009</td>\n",
       "      <td>3.35</td>\n",
       "      <td>11.0</td>\n",
       "      <td>87934</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>city</td>\n",
       "      <td>2017</td>\n",
       "      <td>11.50</td>\n",
       "      <td>12.5</td>\n",
       "      <td>9000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>brio</td>\n",
       "      <td>2016</td>\n",
       "      <td>5.30</td>\n",
       "      <td>5.9</td>\n",
       "      <td>5464</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Car_Name  Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type  \\\n",
       "296     city  2016           9.50           11.6       33988    Diesel   \n",
       "297     brio  2015           4.00            5.9       60000    Petrol   \n",
       "298     city  2009           3.35           11.0       87934    Petrol   \n",
       "299     city  2017          11.50           12.5        9000    Diesel   \n",
       "300     brio  2016           5.30            5.9        5464    Petrol   \n",
       "\n",
       "    Seller_Type Transmission  Owner  \n",
       "296      Dealer       Manual      0  \n",
       "297      Dealer       Manual      0  \n",
       "298      Dealer       Manual      0  \n",
       "299      Dealer       Manual      0  \n",
       "300      Dealer       Manual      0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(301, 9)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 301 entries, 0 to 300\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Car_Name       301 non-null    object \n",
      " 1   Year           301 non-null    int64  \n",
      " 2   Selling_Price  301 non-null    float64\n",
      " 3   Present_Price  301 non-null    float64\n",
      " 4   Kms_Driven     301 non-null    int64  \n",
      " 5   Fuel_Type      301 non-null    object \n",
      " 6   Seller_Type    301 non-null    object \n",
      " 7   Transmission   301 non-null    object \n",
      " 8   Owner          301 non-null    int64  \n",
      "dtypes: float64(2), int64(3), object(4)\n",
      "memory usage: 21.3+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Name         0\n",
       "Year             0\n",
       "Selling_Price    0\n",
       "Present_Price    0\n",
       "Kms_Driven       0\n",
       "Fuel_Type        0\n",
       "Seller_Type      0\n",
       "Transmission     0\n",
       "Owner            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "x='Selling_Price'\n",
    "y='Present_Price'\n",
    "plt.bar(x,y,data=data)\n",
    "plt.xlabel('Selling Price')\n",
    "plt.ylabel('Present Price')\n",
    "plt.title(\"Selling Price Vs Present Price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Anaconda\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Selling_Price', ylabel='Density'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['Selling_Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x255627e8400>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data['Present_Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Present_Price', ylabel='Count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data['Present_Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "label=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Car_Name']=label.fit_transform(data['Car_Name'])\n",
    "data['Fuel_Type']=label.fit_transform(data['Fuel_Type'])\n",
    "data['Seller_Type']=label.fit_transform(data['Seller_Type'])\n",
    "data['Transmission']=label.fit_transform(data['Transmission'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Car_Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90</td>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>93</td>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68</td>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>96</td>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>92</td>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Car_Name  Year  Selling_Price  Present_Price  Kms_Driven  Fuel_Type  \\\n",
       "0        90  2014           3.35           5.59       27000          2   \n",
       "1        93  2013           4.75           9.54       43000          1   \n",
       "2        68  2017           7.25           9.85        6900          2   \n",
       "3        96  2011           2.85           4.15        5200          2   \n",
       "4        92  2014           4.60           6.87       42450          1   \n",
       "\n",
       "   Seller_Type  Transmission  Owner  \n",
       "0            0             1      0  \n",
       "1            0             1      0  \n",
       "2            0             1      0  \n",
       "3            0             1      0  \n",
       "4            0             1      0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Car_Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Car_Name</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.017265</td>\n",
       "      <td>0.499198</td>\n",
       "      <td>0.475539</td>\n",
       "      <td>0.064453</td>\n",
       "      <td>-0.371446</td>\n",
       "      <td>-0.829718</td>\n",
       "      <td>-0.059855</td>\n",
       "      <td>-0.081192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <td>0.017265</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.236141</td>\n",
       "      <td>-0.047584</td>\n",
       "      <td>-0.524342</td>\n",
       "      <td>-0.053643</td>\n",
       "      <td>-0.039896</td>\n",
       "      <td>0.000394</td>\n",
       "      <td>-0.182104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Selling_Price</th>\n",
       "      <td>0.499198</td>\n",
       "      <td>0.236141</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.878983</td>\n",
       "      <td>0.029187</td>\n",
       "      <td>-0.509467</td>\n",
       "      <td>-0.550724</td>\n",
       "      <td>-0.367128</td>\n",
       "      <td>-0.088344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Present_Price</th>\n",
       "      <td>0.475539</td>\n",
       "      <td>-0.047584</td>\n",
       "      <td>0.878983</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.203647</td>\n",
       "      <td>-0.440415</td>\n",
       "      <td>-0.512030</td>\n",
       "      <td>-0.348715</td>\n",
       "      <td>0.008057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kms_Driven</th>\n",
       "      <td>0.064453</td>\n",
       "      <td>-0.524342</td>\n",
       "      <td>0.029187</td>\n",
       "      <td>0.203647</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.166801</td>\n",
       "      <td>-0.101419</td>\n",
       "      <td>-0.162510</td>\n",
       "      <td>0.089216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel_Type</th>\n",
       "      <td>-0.371446</td>\n",
       "      <td>-0.053643</td>\n",
       "      <td>-0.509467</td>\n",
       "      <td>-0.440415</td>\n",
       "      <td>-0.166801</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.352415</td>\n",
       "      <td>0.080466</td>\n",
       "      <td>0.055705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seller_Type</th>\n",
       "      <td>-0.829718</td>\n",
       "      <td>-0.039896</td>\n",
       "      <td>-0.550724</td>\n",
       "      <td>-0.512030</td>\n",
       "      <td>-0.101419</td>\n",
       "      <td>0.352415</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.063240</td>\n",
       "      <td>0.124269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transmission</th>\n",
       "      <td>-0.059855</td>\n",
       "      <td>0.000394</td>\n",
       "      <td>-0.367128</td>\n",
       "      <td>-0.348715</td>\n",
       "      <td>-0.162510</td>\n",
       "      <td>0.080466</td>\n",
       "      <td>0.063240</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.050316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Owner</th>\n",
       "      <td>-0.081192</td>\n",
       "      <td>-0.182104</td>\n",
       "      <td>-0.088344</td>\n",
       "      <td>0.008057</td>\n",
       "      <td>0.089216</td>\n",
       "      <td>0.055705</td>\n",
       "      <td>0.124269</td>\n",
       "      <td>-0.050316</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Car_Name      Year  Selling_Price  Present_Price  Kms_Driven  \\\n",
       "Car_Name       1.000000  0.017265       0.499198       0.475539    0.064453   \n",
       "Year           0.017265  1.000000       0.236141      -0.047584   -0.524342   \n",
       "Selling_Price  0.499198  0.236141       1.000000       0.878983    0.029187   \n",
       "Present_Price  0.475539 -0.047584       0.878983       1.000000    0.203647   \n",
       "Kms_Driven     0.064453 -0.524342       0.029187       0.203647    1.000000   \n",
       "Fuel_Type     -0.371446 -0.053643      -0.509467      -0.440415   -0.166801   \n",
       "Seller_Type   -0.829718 -0.039896      -0.550724      -0.512030   -0.101419   \n",
       "Transmission  -0.059855  0.000394      -0.367128      -0.348715   -0.162510   \n",
       "Owner         -0.081192 -0.182104      -0.088344       0.008057    0.089216   \n",
       "\n",
       "               Fuel_Type  Seller_Type  Transmission     Owner  \n",
       "Car_Name       -0.371446    -0.829718     -0.059855 -0.081192  \n",
       "Year           -0.053643    -0.039896      0.000394 -0.182104  \n",
       "Selling_Price  -0.509467    -0.550724     -0.367128 -0.088344  \n",
       "Present_Price  -0.440415    -0.512030     -0.348715  0.008057  \n",
       "Kms_Driven     -0.166801    -0.101419     -0.162510  0.089216  \n",
       "Fuel_Type       1.000000     0.352415      0.080466  0.055705  \n",
       "Seller_Type     0.352415     1.000000      0.063240  0.124269  \n",
       "Transmission    0.080466     0.063240      1.000000 -0.050316  \n",
       "Owner           0.055705     0.124269     -0.050316  1.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=data[['Present_Price']]\n",
    "y=data[['Selling_Price']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Present_Price\n",
      "0             5.59\n",
      "1             9.54\n",
      "2             9.85\n",
      "3             4.15\n",
      "4             6.87\n",
      "..             ...\n",
      "296          11.60\n",
      "297           5.90\n",
      "298          11.00\n",
      "299          12.50\n",
      "300           5.90\n",
      "\n",
      "[301 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Selling_Price\n",
      "0             3.35\n",
      "1             4.75\n",
      "2             7.25\n",
      "3             2.85\n",
      "4             4.60\n",
      "..             ...\n",
      "296           9.50\n",
      "297           4.00\n",
      "298           3.35\n",
      "299          11.50\n",
      "300           5.30\n",
      "\n",
      "[301 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((301, 1), (240, 1), (61, 1))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0)\n",
    "x.shape,x_train.shape,x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_train,y_train)\n",
    "plt.plot(x_train,model.predict(x_train),color='green')\n",
    "plt.xlabel('Present price')\n",
    "plt.ylabel('Selling Price')\n",
    "plt.title('Present Price Vs Selling Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_test,y_test)\n",
    "plt.plot(x_test,model.predict(x_test),color='green')\n",
    "plt.xlabel('Present price')\n",
    "plt.ylabel('Selling Price')\n",
    "plt.title('Present Price Vs Selling Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training Data 0.7641953850242089\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy on training Data',model.score(x_train,y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on testing Data 0.7925288537728552\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy on testing Data',model.score(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.51694101]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[5.59]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.62652067]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[9.85]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}