Car_Price_Prediction.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Car Price Prediction"
      ],
      "metadata": {
        "id": "mIZFSnzXSKKg"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "G6dW_4nPFMID"
      },
      "outputs": [],
      "source": [
        "import pandas as pd \n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "6f-yshVLGo4n"
      },
      "outputs": [],
      "source": [
        "df=pd.read_csv(r'https://github.com/YBI-Foundation/Dataset/raw/main/Car%20Price.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
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      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.head of         Brand                                Model  Year  Selling_Price  \\\n",
              "0      Maruti                        Maruti 800 AC  2007          60000   \n",
              "1      Maruti             Maruti Wagon R LXI Minor  2007         135000   \n",
              "2     Hyundai                 Hyundai Verna 1.6 SX  2012         600000   \n",
              "3      Datsun               Datsun RediGO T Option  2017         250000   \n",
              "4       Honda                Honda Amaze VX i-DTEC  2014         450000   \n",
              "...       ...                                  ...   ...            ...   \n",
              "4335  Hyundai  Hyundai i20 Magna 1.4 CRDi (Diesel)  2014         409999   \n",
              "4336  Hyundai           Hyundai i20 Magna 1.4 CRDi  2014         409999   \n",
              "4337   Maruti                  Maruti 800 AC BSIII  2009         110000   \n",
              "4338  Hyundai     Hyundai Creta 1.6 CRDi SX Option  2016         865000   \n",
              "4339  Renault                     Renault KWID RXT  2016         225000   \n",
              "\n",
              "      KM_Driven    Fuel Seller_Type Transmission         Owner  \n",
              "0         70000  Petrol  Individual       Manual   First Owner  \n",
              "1         50000  Petrol  Individual       Manual   First Owner  \n",
              "2        100000  Diesel  Individual       Manual   First Owner  \n",
              "3         46000  Petrol  Individual       Manual   First Owner  \n",
              "4        141000  Diesel  Individual       Manual  Second Owner  \n",
              "...         ...     ...         ...          ...           ...  \n",
              "4335      80000  Diesel  Individual       Manual  Second Owner  \n",
              "4336      80000  Diesel  Individual       Manual  Second Owner  \n",
              "4337      83000  Petrol  Individual       Manual  Second Owner  \n",
              "4338      90000  Diesel  Individual       Manual   First Owner  \n",
              "4339      40000  Petrol  Individual       Manual   First Owner  \n",
              "\n",
              "[4340 rows x 9 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "df.head"
      ]
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        {
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4340 entries, 0 to 4339\n",
            "Data columns (total 9 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   Brand          4340 non-null   object\n",
            " 1   Model          4340 non-null   object\n",
            " 2   Year           4340 non-null   int64 \n",
            " 3   Selling_Price  4340 non-null   int64 \n",
            " 4   KM_Driven      4340 non-null   int64 \n",
            " 5   Fuel           4340 non-null   object\n",
            " 6   Seller_Type    4340 non-null   object\n",
            " 7   Transmission   4340 non-null   object\n",
            " 8   Owner          4340 non-null   object\n",
            "dtypes: int64(3), object(6)\n",
            "memory usage: 305.3+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
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      "execution_count": 5,
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            "text/plain": [
              "              Year  Selling_Price      KM_Driven\n",
              "count  4340.000000   4.340000e+03    4340.000000\n",
              "mean   2013.090783   5.041273e+05   66215.777419\n",
              "std       4.215344   5.785487e+05   46644.102194\n",
              "min    1992.000000   2.000000e+04       1.000000\n",
              "25%    2011.000000   2.087498e+05   35000.000000\n",
              "50%    2014.000000   3.500000e+05   60000.000000\n",
              "75%    2016.000000   6.000000e+05   90000.000000\n",
              "max    2020.000000   8.900000e+06  806599.000000"
            ],
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              "      <th>mean</th>\n",
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              "      <td>5.041273e+05</td>\n",
              "      <td>66215.777419</td>\n",
              "    </tr>\n",
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              "      <th>std</th>\n",
              "      <td>4.215344</td>\n",
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              "      <td>46644.102194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1992.000000</td>\n",
              "      <td>2.000000e+04</td>\n",
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              "      <th>50%</th>\n",
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          "data": {
            "text/plain": [
              "Brand        \n",
              "Maruti           1280\n",
              "Hyundai           821\n",
              "Mahindra          365\n",
              "Tata              361\n",
              "Honda             252\n",
              "Ford              238\n",
              "Toyota            206\n",
              "Chevrolet         188\n",
              "Renault           146\n",
              "Volkswagen        107\n",
              "Skoda              68\n",
              "Nissan             64\n",
              "Audi               60\n",
              "BMW                39\n",
              "Fiat               37\n",
              "Datsun             37\n",
              "Mercedes-Benz      35\n",
              "Mitsubishi          6\n",
              "Jaguar              6\n",
              "Land                5\n",
              "Ambassador          4\n",
              "Volvo               4\n",
              "Jeep                3\n",
              "OpelCorsa           2\n",
              "MG                  2\n",
              "Isuzu               1\n",
              "Force               1\n",
              "Daewoo              1\n",
              "Kia                 1\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
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      ],
      "source": [
        "df[['Brand']].value_counts()"
      ]
    },
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      "execution_count": 7,
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Model                               \n",
              "Maruti Swift Dzire VDI                  69\n",
              "Maruti Alto 800 LXI                     59\n",
              "Maruti Alto LXi                         47\n",
              "Hyundai EON Era Plus                    35\n",
              "Maruti Alto LX                          35\n",
              "                                        ..\n",
              "Mahindra KUV 100 G80 K4 Plus             1\n",
              "Mahindra KUV 100 mFALCON D75 K8          1\n",
              "Mahindra KUV 100 mFALCON D75 K8 AW       1\n",
              "Mahindra KUV 100 mFALCON G80 K2 Plus     1\n",
              "Volvo XC60 D5 Inscription                1\n",
              "Length: 1491, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
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        "df[['Model']].value_counts()"
      ]
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      "execution_count": 8,
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      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Fuel    \n",
              "Diesel      2153\n",
              "Petrol      2123\n",
              "CNG           40\n",
              "LPG           23\n",
              "Electric       1\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "df[['Fuel']].value_counts()"
      ]
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      "execution_count": 9,
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      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Seller_Type     \n",
              "Individual          3244\n",
              "Dealer               994\n",
              "Trustmark Dealer     102\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "df[['Seller_Type']].value_counts()"
      ]
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Transmission\n",
              "Manual          3892\n",
              "Automatic        448\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
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      "source": [
        "df[['Transmission']].value_counts()"
      ]
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      "execution_count": 11,
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      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Owner               \n",
              "First Owner             2832\n",
              "Second Owner            1106\n",
              "Third Owner              304\n",
              "Fourth & Above Owner      81\n",
              "Test Drive Car            17\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df[['Owner']].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Brand', 'Model', 'Year', 'Selling_Price', 'KM_Driven', 'Fuel',\n",
              "       'Seller_Type', 'Transmission', 'Owner'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "df.columns"
      ]
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            "text/plain": [
              "(4340, 9)"
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          "execution_count": 13
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        "df.shape"
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      "execution_count": 14,
      "metadata": {
        "id": "UGCCs9S-I5lt"
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      "source": [
        "df.replace({'Fuel':{'Petrol':0,'Diesel':1,'CNG':2,'LPG':3,'Electric':4}},inplace=True)"
      ]
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      "execution_count": 15,
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        "df.replace({'Seller_Type':{'Individual':0,'Dealer':1,'Trustmark Dealer':2}},inplace=True)"
      ]
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      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
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      "source": [
        "df.replace({'Transmission':{'Manual':0,'Automatic':1}},inplace=True)"
      ]
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      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "w0Ueo-ecKUkk"
      },
      "outputs": [],
      "source": [
        "df.replace({'Owner':{'First Owner':0,'Second Owner':1,'Third Owner':2,'Fourth & Above Owner':3,'Test Drive Car':4}},inplace=True)"
      ]
    },
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      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
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      "source": [
        "y=df['Selling_Price']"
      ]
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              "(4340,)"
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          "execution_count": 19
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        "outputId": "4fe0d395-b64b-47ae-8637-4b229904d3ad"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        60000\n",
              "1       135000\n",
              "2       600000\n",
              "3       250000\n",
              "4       450000\n",
              "         ...  \n",
              "4335    409999\n",
              "4336    409999\n",
              "4337    110000\n",
              "4338    865000\n",
              "4339    225000\n",
              "Name: Selling_Price, Length: 4340, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "4Gud_CS8LJzl"
      },
      "outputs": [],
      "source": [
        "x=df[['Year','KM_Driven','Fuel','Seller_Type','Transmission','Owner']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EyE25W7JLeVK",
        "outputId": "0e7ee262-8305-48da-bf13-4df8a8e06cb0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4340, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "x.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "65goFTvrLnCz",
        "outputId": "e517b8bd-813f-4fc8-b090-21c31ef216a9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Year  KM_Driven  Fuel  Seller_Type  Transmission  Owner\n",
              "0     2007      70000     0            0             0      0\n",
              "1     2007      50000     0            0             0      0\n",
              "2     2012     100000     1            0             0      0\n",
              "3     2017      46000     0            0             0      0\n",
              "4     2014     141000     1            0             0      1\n",
              "...    ...        ...   ...          ...           ...    ...\n",
              "4335  2014      80000     1            0             0      1\n",
              "4336  2014      80000     1            0             0      1\n",
              "4337  2009      83000     0            0             0      1\n",
              "4338  2016      90000     1            0             0      0\n",
              "4339  2016      40000     0            0             0      0\n",
              "\n",
              "[4340 rows x 6 columns]"
            ],
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              "      <th>3</th>\n",
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              "      <th>4339</th>\n",
              "      <td>2016</td>\n",
              "      <td>40000</td>\n",
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              "        buttonEl.style.display =\n",
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            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "OUcafMvALnu4"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "3444IiLcL4da"
      },
      "outputs": [],
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(x,y, test_size=0.3,random_state=222529)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U6xyRtz3MKuF",
        "outputId": "58ecca95-d561-4ee9-9e20-62255ca0a9d1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((3038, 6), (1302, 6), (3038,), (1302,))"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "x_train.shape,x_test.shape,y_train.shape,y_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "P2xjnzgLMTzB"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "gKY2Y8nbMfbw"
      },
      "outputs": [],
      "source": [
        "lr=LinearRegression()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6hMbv7HXMiDL",
        "outputId": "b6dc9fff-8719-41ed-9a18-e84e734e8736"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "lr.fit(x_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "2LsDhUFjMoHG"
      },
      "outputs": [],
      "source": [
        "y_pred=lr.predict(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jt2Ay5rnMxrE",
        "outputId": "8a51b005-32fe-4b67-9640-3aa1c5ba5cab"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1302,)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "y_pred.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t8u4WrQ5Mz5w",
        "outputId": "8aceae29-6cdf-483e-bbbf-4f3fc82ab0ef"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([401110.15351234, 617658.74774638, 536466.81883579, ...,\n",
              "       425240.41060649, 631846.59426308, 472136.35070334])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "y_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "Tq9vSPDiM1Ga"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import mean_squared_error,mean_absolute_error,r2_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "azvcR_mYNMgx",
        "outputId": "a6e9ad31-8670-46ac-d61f-d40bc9662ff5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "211916283349.51315"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "mean_squared_error(y_test,y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-LMs86XyNUn7",
        "outputId": "ba743e22-4e48-486e-ae15-ce2e376d9597"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "235659.72181216907"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "mean_absolute_error(y_test,y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "liYeNGBUNca1",
        "outputId": "f0c5323a-cad0-465f-ffc8-bfd6e4a10f2c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.42015598785727215"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "r2_score(y_test,y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "9Gz7Ev2tNetP",
        "outputId": "35e620da-fae5-467b-cd5c-8ee76bf7d202"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(y_test,y_pred)\n",
        "plt.xlabel(\"Actual Prices\")\n",
        "plt.ylabel(\"Predicted Prices\")\n",
        "plt.title(\"Actual Price vs Predicted Prices\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "_plyA3g9N7nQ"
      },
      "outputs": [],
      "source": [
        "df_new=df.sample(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "-zDEMImHOG4S",
        "outputId": "6982dcc0-94aa-4ab1-f2dd-5f23d869e87d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Brand                              Model  Year  Selling_Price  \\\n",
              "1057  Skoda  Skoda Octavia Elegance 2.0 TDI AT  2014        1200000   \n",
              "\n",
              "      KM_Driven  Fuel  Seller_Type  Transmission  Owner  \n",
              "1057     135000     1            0             1      2  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-eb9069b9-9824-4163-87cd-4501bdc0dda5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Brand</th>\n",
              "      <th>Model</th>\n",
              "      <th>Year</th>\n",
              "      <th>Selling_Price</th>\n",
              "      <th>KM_Driven</th>\n",
              "      <th>Fuel</th>\n",
              "      <th>Seller_Type</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Owner</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1057</th>\n",
              "      <td>Skoda</td>\n",
              "      <td>Skoda Octavia Elegance 2.0 TDI AT</td>\n",
              "      <td>2014</td>\n",
              "      <td>1200000</td>\n",
              "      <td>135000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb9069b9-9824-4163-87cd-4501bdc0dda5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-eb9069b9-9824-4163-87cd-4501bdc0dda5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-eb9069b9-9824-4163-87cd-4501bdc0dda5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "df_new"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "waUOo2JEOH4x",
        "outputId": "e9be4659-a2ac-45b1-e125-6f314f969a57"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "df_new.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "pjlHsTcyOM1F"
      },
      "outputs": [],
      "source": [
        "x_new=df_new.drop(['Brand','Model','Selling_Price'],axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "KSDzBRYCOagD"
      },
      "outputs": [],
      "source": [
        "y_pred_new=lr.predict(x_new)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "4PqiFSq3Oq3C"
      },
      "outputs": [],
      "source": [
        "y_pred_new=lr.predict(x_new)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CYIa3KC5OvCu",
        "outputId": "4fbb2121-eaeb-46a4-c61d-56ec226a6b08"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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