car_price_prediction.ipynb

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  "metadata": {
    "colab": {
      "name": "car price prediction.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyO6qmZof7ghH97+d2Ein1oB",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Raj-Banerjee/ML-old_car_price_prediction/blob/main/car_price_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "12Y7P61OdYSv"
      },
      "source": [
        "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.linear_model import Lasso\n",
        "from sklearn import metrics"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yjNky0fBfT13"
      },
      "source": [
        "car_dataset= pd.read_csv('/content/CAR DETAILS FROM CAR DEKHO.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "jASngS58gFci",
        "outputId": "a7b93a40-c8cc-4e7d-c0b0-b61a71632de7"
      },
      "source": [
        "car_dataset.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\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>name</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>0</th>\n",
              "      <td>Maruti 800 AC</td>\n",
              "      <td>2007</td>\n",
              "      <td>60000</td>\n",
              "      <td>70000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First Owner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Maruti Wagon R LXI Minor</td>\n",
              "      <td>2007</td>\n",
              "      <td>135000</td>\n",
              "      <td>50000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First Owner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Hyundai Verna 1.6 SX</td>\n",
              "      <td>2012</td>\n",
              "      <td>600000</td>\n",
              "      <td>100000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First Owner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Datsun RediGO T Option</td>\n",
              "      <td>2017</td>\n",
              "      <td>250000</td>\n",
              "      <td>46000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First Owner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Honda Amaze VX i-DTEC</td>\n",
              "      <td>2014</td>\n",
              "      <td>450000</td>\n",
              "      <td>141000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Manual</td>\n",
              "      <td>Second Owner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Maruti Alto LX BSIII</td>\n",
              "      <td>2007</td>\n",
              "      <td>140000</td>\n",
              "      <td>125000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First Owner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Hyundai Xcent 1.2 Kappa S</td>\n",
              "      <td>2016</td>\n",
              "      <td>550000</td>\n",
              "      <td>25000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First Owner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Tata Indigo Grand Petrol</td>\n",
              "      <td>2014</td>\n",
              "      <td>240000</td>\n",
              "      <td>60000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Manual</td>\n",
              "      <td>Second Owner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Hyundai Creta 1.6 VTVT S</td>\n",
              "      <td>2015</td>\n",
              "      <td>850000</td>\n",
              "      <td>25000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First Owner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Maruti Celerio Green VXI</td>\n",
              "      <td>2017</td>\n",
              "      <td>365000</td>\n",
              "      <td>78000</td>\n",
              "      <td>CNG</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First Owner</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        name  year  ...  transmission         owner\n",
              "0              Maruti 800 AC  2007  ...        Manual   First Owner\n",
              "1   Maruti Wagon R LXI Minor  2007  ...        Manual   First Owner\n",
              "2       Hyundai Verna 1.6 SX  2012  ...        Manual   First Owner\n",
              "3     Datsun RediGO T Option  2017  ...        Manual   First Owner\n",
              "4      Honda Amaze VX i-DTEC  2014  ...        Manual  Second Owner\n",
              "5       Maruti Alto LX BSIII  2007  ...        Manual   First Owner\n",
              "6  Hyundai Xcent 1.2 Kappa S  2016  ...        Manual   First Owner\n",
              "7   Tata Indigo Grand Petrol  2014  ...        Manual  Second Owner\n",
              "8   Hyundai Creta 1.6 VTVT S  2015  ...        Manual   First Owner\n",
              "9   Maruti Celerio Green VXI  2017  ...        Manual   First Owner\n",
              "\n",
              "[10 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2jh5FCYKfFqn"
      },
      "source": [
        "car_dataset['selling_price'] = car_dataset['selling_price']/100000"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4NyHf-CannTJ"
      },
      "source": [
        "Getting the details about the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MySX3NRxgYm_",
        "outputId": "cfa34ec8-8e3b-47d6-b830-8647821313b2"
      },
      "source": [
        "car_dataset.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4340, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4woXojkthAz7",
        "outputId": "127bddfa-ba4d-474e-b584-0973ab0a4157"
      },
      "source": [
        "car_dataset.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4340 entries, 0 to 4339\n",
            "Data columns (total 8 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   name           4340 non-null   object \n",
            " 1   year           4340 non-null   int64  \n",
            " 2   selling_price  4340 non-null   float64\n",
            " 3   km_driven      4340 non-null   int64  \n",
            " 4   fuel           4340 non-null   object \n",
            " 5   seller_type    4340 non-null   object \n",
            " 6   transmission   4340 non-null   object \n",
            " 7   owner          4340 non-null   object \n",
            "dtypes: float64(1), int64(2), object(5)\n",
            "memory usage: 271.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NVdvhiE3nbjC"
      },
      "source": [
        "checking for the number of missing values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nex_HtmOnPSh",
        "outputId": "00a67e60-0d38-4637-cfd9-ecacb33443d3"
      },
      "source": [
        "car_dataset.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "name             0\n",
              "year             0\n",
              "selling_price    0\n",
              "km_driven        0\n",
              "fuel             0\n",
              "seller_type      0\n",
              "transmission     0\n",
              "owner            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5LPDCE-en1vi",
        "outputId": "83173906-e5de-41d4-8b4a-78df720a2cf7"
      },
      "source": [
        "#  Check for the differnt cateogories of the data\n",
        "print(car_dataset.fuel.value_counts())\n",
        "print(car_dataset.transmission.value_counts())\n",
        "print(car_dataset.owner.value_counts())\n",
        "print(car_dataset.seller_type.value_counts())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Diesel      2153\n",
            "Petrol      2123\n",
            "CNG           40\n",
            "LPG           23\n",
            "Electric       1\n",
            "Name: fuel, dtype: int64\n",
            "Manual       3892\n",
            "Automatic     448\n",
            "Name: transmission, dtype: int64\n",
            "First Owner             2832\n",
            "Second Owner            1106\n",
            "Third Owner              304\n",
            "Fourth & Above Owner      81\n",
            "Test Drive Car            17\n",
            "Name: owner, dtype: int64\n",
            "Individual          3244\n",
            "Dealer               994\n",
            "Trustmark Dealer     102\n",
            "Name: seller_type, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AQ5f3DIppya6"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "<br>\n",
        "<h4> Encoding of the cateogorical data :</h4> <br>\n",
        "This means that the data is now converted into the numeric value for each part i:e transmission or fuel type into numbers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jgsSYQeToupH"
      },
      "source": [
        "car_dataset.replace({'fuel':{'Petrol':0,'Diesel':1,'CNG':2,'LPG':3,'Electric':4}},inplace=True)\n",
        "car_dataset.replace({'transmission':{'Manual':0,'Automatic':1}},inplace=True)\n",
        "car_dataset.replace({'owner':{'First Owner':0,'Second Owner':1,'Third Owner':2,'Fourth & Above Owner':3,'Test Drive Car':4}},inplace=True)\n",
        "car_dataset.replace({'seller_type':{'Individual':0,'Dealer':1,'Trustmark Dealer':2}},inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "vmNAeq3ytsZV",
        "outputId": "e0b2fc83-c00f-47e4-a0d1-941a25ddc9b2"
      },
      "source": [
        "car_dataset.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>name</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>0</th>\n",
              "      <td>Maruti 800 AC</td>\n",
              "      <td>2007</td>\n",
              "      <td>0.60</td>\n",
              "      <td>70000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Maruti Wagon R LXI Minor</td>\n",
              "      <td>2007</td>\n",
              "      <td>1.35</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Hyundai Verna 1.6 SX</td>\n",
              "      <td>2012</td>\n",
              "      <td>6.00</td>\n",
              "      <td>100000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Datsun RediGO T Option</td>\n",
              "      <td>2017</td>\n",
              "      <td>2.50</td>\n",
              "      <td>46000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Honda Amaze VX i-DTEC</td>\n",
              "      <td>2014</td>\n",
              "      <td>4.50</td>\n",
              "      <td>141000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       name  year  ...  transmission  owner\n",
              "0             Maruti 800 AC  2007  ...             0      0\n",
              "1  Maruti Wagon R LXI Minor  2007  ...             0      0\n",
              "2      Hyundai Verna 1.6 SX  2012  ...             0      0\n",
              "3    Datsun RediGO T Option  2017  ...             0      0\n",
              "4     Honda Amaze VX i-DTEC  2014  ...             0      1\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dPTwIiP7vV9Y"
      },
      "source": [
        "Spliting the Dataset into training and target"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fFQEAu0Ftzhf"
      },
      "source": [
        "x = car_dataset.drop(['name','selling_price'],axis=1)\n",
        "y = car_dataset['selling_price']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8JiAxulswmBI",
        "outputId": "4476e41c-cdf8-43be-ba64-45fc7be1d597"
      },
      "source": [
        "print(y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0       0.60000\n",
            "1       1.35000\n",
            "2       6.00000\n",
            "3       2.50000\n",
            "4       4.50000\n",
            "         ...   \n",
            "4335    4.09999\n",
            "4336    4.09999\n",
            "4337    1.10000\n",
            "4338    8.65000\n",
            "4339    2.25000\n",
            "Name: selling_price, Length: 4340, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0u-kTz6SwpGa"
      },
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hwKi_-D-zYVv"
      },
      "source": [
        "Model Training\n",
        "1. Linear Regression Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_yKSR6oHy5BH"
      },
      "source": [
        "lin_reg_model = LinearRegression()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Df0nJwpTzsyg",
        "outputId": "1df21302-fd82-445b-fc18-4a180bf34643"
      },
      "source": [
        "lin_reg_model.fit(x_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QYPFLjAHfc35"
      },
      "source": [
        "Model Evvaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "12siUNb6z91F"
      },
      "source": [
        "# Prediction of Training Data\n",
        "training_data_prediction = lin_reg_model.predict(x_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nJ5aRU62fSl_",
        "outputId": "0080a6a3-36de-4603-b4ee-623731e4f086"
      },
      "source": [
        "# R Square Error\n",
        "error_score = metrics.r2_score(y_train, training_data_prediction)\n",
        "print(\"R Square Error: \",error_score)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R Square Error:  0.42832288947625463\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HBI9mRhtbDHr"
      },
      "source": [
        "Visualising the Actual prices and the predicted prices"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B3vYpOebgIVl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "c2f6fb88-9075-4fdf-cd96-0e85f44e4b24"
      },
      "source": [
        "plt.scatter(y_train, training_data_prediction)\n",
        "plt.xlabel(\"Actual Prices\")\n",
        "plt.ylabel(\"Predicted Prices\")\n",
        "plt.title(\"Actual Prices VS Predicted Prices\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gcR5EvWJgnea"
      },
      "source": [
        "### Now Calculate for the Test Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KW2P6Yokbt3Z"
      },
      "source": [
        "# Prediction of Testing Data\n",
        "testing_data_prediction = lin_reg_model.predict(x_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C6w6e1yCg6CO",
        "outputId": "82d568bc-0d80-46c0-9e9d-3f57582680d1"
      },
      "source": [
        "# R Square error\n",
        "error_score = metrics.r2_score(y_test, testing_data_prediction)\n",
        "print(\"R Square Error For Test Data: \",error_score)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R Square Error For Test Data:  0.4918005460679514\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "EBCar0ivhd4t",
        "outputId": "c0091dba-d915-4671-e627-c90dc21f13db"
      },
      "source": [
        "plt.scatter(y_test, testing_data_prediction)\n",
        "plt.xlabel(\"Actual Prices\")\n",
        "plt.ylabel(\"Predicted Prices\")\n",
        "plt.title(\"Actual Prices VS Predicted Prices\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WVktfeMhSnsZ"
      },
      "source": [
        "# Now By Lasso Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DyNwjm8zWnbL"
      },
      "source": [
        "lass_reg_model = Lasso()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YB2B-MHSWncO",
        "outputId": "e2559ba4-2376-43f4-fc7e-d6140b44651b"
      },
      "source": [
        "lass_reg_model.fit(x_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,\n",
              "      normalize=False, positive=False, precompute=False, random_state=None,\n",
              "      selection='cyclic', tol=0.0001, warm_start=False)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "czF0CvIoW5wF"
      },
      "source": [
        "Model Evvaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FpS_2pzbW5wH"
      },
      "source": [
        "# Prediction of Training Data\n",
        "training_data_prediction = lass_reg_model.predict(x_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1T3EAe6DW5wJ",
        "outputId": "d60b61de-e554-4d30-a05b-dac77e831f50"
      },
      "source": [
        "# R Square Error\n",
        "error_score = metrics.r2_score(y_train, training_data_prediction)\n",
        "print(\"R Square Error: \",error_score)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R Square Error:  0.16762979724555904\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "7RoZ-lSmW5wM",
        "outputId": "53c8aa5e-9a4f-4521-8362-369f9289282f"
      },
      "source": [
        "plt.scatter(y_train, training_data_prediction)\n",
        "plt.xlabel(\"Actual Prices\")\n",
        "plt.ylabel(\"Predicted Prices\")\n",
        "plt.title(\"Actual Prices VS Predicted Prices\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CVlsUTbBW5wP"
      },
      "source": [
        "# Prediction of Testing Data\n",
        "testing_data_prediction = lass_reg_model.predict(x_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jA7mxjUxW5wR",
        "outputId": "8971734a-55e3-476f-c3e2-4cecdc298330"
      },
      "source": [
        "# R Square error\n",
        "error_score = metrics.r2_score(y_test, testing_data_prediction)\n",
        "print(\"R Square Error For Test Data: \",error_score)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R Square Error For Test Data:  0.17773832837050252\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "cbDlbaAGW5wT",
        "outputId": "2ed6f2d0-daa8-4ce2-e39e-ca1eeb4d40c8"
      },
      "source": [
        "plt.scatter(y_test, testing_data_prediction)\n",
        "plt.xlabel(\"Actual Prices\")\n",
        "plt.ylabel(\"Predicted Prices\")\n",
        "plt.title(\"Actual Prices VS Predicted Prices\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jhoJYcAoa60I"
      },
      "source": [
        "Printing the Output"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BdPSwD6Ma1eI",
        "outputId": "df83151e-086b-4cb0-8ab5-062e50bb4cd4"
      },
      "source": [
        "print(lin_reg_model.coef_)\n",
        "print(lin_reg_model.intercept_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 3.99312030e-01 -5.88804365e-06  2.10230588e+00  9.33887102e-01\n",
            "  8.65301306e+00 -1.32434698e-01]\n",
            "-800.6258923790391\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i1FI5byNbRFz"
      },
      "source": [
        "# Sample Output "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rR1P1LAZcnwY"
      },
      "source": [
        "The Output will be in format of Year, KM Driven, Fuel, Seller Type,  Transmission, Owner"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fIK1OSGPbLQt",
        "outputId": "b2b00a7f-df1c-4800-d503-6e609bbfc9a6"
      },
      "source": [
        "cost = (lin_reg_model.predict([[2014,100000,0,0,0,1]]))\n",
        "print(cost*100000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[286729.78018046]\n"
          ]
        }
      ]
    }
  ]
}