Project_5_Car_Price_Prediction_using_Machine_Learning_with_Python.ipynb

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  "metadata": {
    "colab": {
      "name": "Project  5 - Car Price Prediction using Machine Learning with Python.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyO2e/2dorPFfnPYAJE64b/r",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DeveloperAkram/Colab-Notebooks/blob/master/Project_5_Car_Price_Prediction_using_Machine_Learning_with_Python.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jYie-Sg-pE0o"
      },
      "source": [
        "Immporting the Dependenies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hymHCts_o8xA"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\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": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YYm4a2_uqXnr"
      },
      "source": [
        "Data Collection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PZRM4lvcqSBj"
      },
      "source": [
        "# loading the data from csvv file to pandas datafram\n",
        "\n",
        "car_dataset = pd.read_csv('/content/car data.csv')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "Tf4PDvu3q76N",
        "outputId": "6b310010-d2cf-4506-d702-a0494e8afb5d"
      },
      "source": [
        "# first 5 rows of the dataframe\n",
        "\n",
        "car_dataset.head()"
      ],
      "execution_count": 3,
      "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>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  ...  Seller_Type  Transmission Owner\n",
              "0     ritz  2014           3.35  ...       Dealer        Manual     0\n",
              "1      sx4  2013           4.75  ...       Dealer        Manual     0\n",
              "2     ciaz  2017           7.25  ...       Dealer        Manual     0\n",
              "3  wagon r  2011           2.85  ...       Dealer        Manual     0\n",
              "4    swift  2014           4.60  ...       Dealer        Manual     0\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "pxpChhvQrPdE",
        "outputId": "4603e28a-3597-4fb4-c16a-02133e0bc58d"
      },
      "source": [
        "car_dataset.tail()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "</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  ...  Seller_Type  Transmission Owner\n",
              "296     city  2016           9.50  ...       Dealer        Manual     0\n",
              "297     brio  2015           4.00  ...       Dealer        Manual     0\n",
              "298     city  2009           3.35  ...       Dealer        Manual     0\n",
              "299     city  2017          11.50  ...       Dealer        Manual     0\n",
              "300     brio  2016           5.30  ...       Dealer        Manual     0\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MAteT14WrWkW",
        "outputId": "985663d5-2244-4b14-854d-84762f9faa82"
      },
      "source": [
        "# no of rows and colummns\n",
        "\n",
        "car_dataset.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(301, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xWF6W4J-rkcu",
        "outputId": "3490d00f-6f51-4503-833a-0b4f4e5e3e5e"
      },
      "source": [
        "# getting some information about dataset\n",
        "\n",
        "car_dataset.info()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BpQ0dSi9sAwg",
        "outputId": "d43f0147-6ff9-48f6-9acd-fa812eb5a3ac"
      },
      "source": [
        "# checking the no of missing values\n",
        "\n",
        "car_dataset.isnull().sum()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wz4ZWHvysUqF",
        "outputId": "6d79a5ad-59ab-491d-8254-7026579951eb"
      },
      "source": [
        "# checking the distribution of categorical data\n",
        "\n",
        "print(car_dataset.Fuel_Type.value_counts())\n",
        "print(car_dataset.Seller_Type.value_counts())\n",
        "print(car_dataset.Transmission.value_counts())"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Petrol    239\n",
            "Diesel     60\n",
            "CNG         2\n",
            "Name: Fuel_Type, dtype: int64\n",
            "Dealer        195\n",
            "Individual    106\n",
            "Name: Seller_Type, dtype: int64\n",
            "Manual       261\n",
            "Automatic     40\n",
            "Name: Transmission, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FsTSkgCetm_p"
      },
      "source": [
        "Encoding the categorical data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i5W0Imj4tO8W"
      },
      "source": [
        "# encoding 'Fuel Type' colummn\n",
        "car_dataset.replace({'Fuel_Type':{'Petrol':0, 'Diesel':1, 'CNG':2}}, inplace=True)\n",
        "\n",
        "# encoding 'Seller Type' column\n",
        "car_dataset.replace({'Seller_Type':{'Dealer':0, 'Individual':1}}, inplace=True)\n",
        "\n",
        "# encoding 'Transmission' column\n",
        "car_dataset.replace({'Transmission':{'Manual':0, 'Automatic':1}}, inplace=True)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "s6Akfa09vOds",
        "outputId": "53862dc2-4bf3-4055-a46c-5e52b2a192e0"
      },
      "source": [
        "car_dataset.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Car_Name  Year  Selling_Price  ...  Seller_Type  Transmission  Owner\n",
              "0     ritz  2014           3.35  ...            0             0      0\n",
              "1      sx4  2013           4.75  ...            0             0      0\n",
              "2     ciaz  2017           7.25  ...            0             0      0\n",
              "3  wagon r  2011           2.85  ...            0             0      0\n",
              "4    swift  2014           4.60  ...            0             0      0\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tcw42_FAvcIy"
      },
      "source": [
        "Splitting the data into Training data and Test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W-6M8cU3vSuv"
      },
      "source": [
        "X = car_dataset.drop(['Car_Name', 'Selling_Price'], axis=1)\n",
        "Y = car_dataset['Selling_Price']"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nI69tOOtv4oE",
        "outputId": "bdf3ce1f-39d7-4e09-d683-95d03d0e9074"
      },
      "source": [
        "print(X)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Year  Present_Price  Kms_Driven  ...  Seller_Type  Transmission  Owner\n",
            "0    2014           5.59       27000  ...            0             0      0\n",
            "1    2013           9.54       43000  ...            0             0      0\n",
            "2    2017           9.85        6900  ...            0             0      0\n",
            "3    2011           4.15        5200  ...            0             0      0\n",
            "4    2014           6.87       42450  ...            0             0      0\n",
            "..    ...            ...         ...  ...          ...           ...    ...\n",
            "296  2016          11.60       33988  ...            0             0      0\n",
            "297  2015           5.90       60000  ...            0             0      0\n",
            "298  2009          11.00       87934  ...            0             0      0\n",
            "299  2017          12.50        9000  ...            0             0      0\n",
            "300  2016           5.90        5464  ...            0             0      0\n",
            "\n",
            "[301 rows x 7 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WXmVSyBqwF-d",
        "outputId": "60da9848-ae47-42ec-9775-2562292d1144"
      },
      "source": [
        "print(Y)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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",
            "Name: Selling_Price, Length: 301, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rx_YYDRjwTsD"
      },
      "source": [
        "Splitting Training and Test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b5rL6ifMwHR-"
      },
      "source": [
        "X_train, X_test, Y_tarin, Y_test = train_test_split(X, Y, test_size = 0.1, random_state = 2)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3uuyHlAaw18R",
        "outputId": "245e0786-56b3-4d6a-b7ac-912cbe0508ee"
      },
      "source": [
        "print(X.shape, X_train.shape, Y_tarin.shape)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(301, 7) (270, 7) (270,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J1NjMk7FxHmK"
      },
      "source": [
        "Model Training\n",
        "\n",
        "\n",
        "1.   Linear Regression\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1yOBBTxXxF1v"
      },
      "source": [
        "# loading the linear regression model\n",
        "\n",
        "lin_reg_model = LinearRegression()"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mvQLH2UpxdRP",
        "outputId": "ecfef74e-d50d-423d-d8aa-2535f4cf4d90"
      },
      "source": [
        "lin_reg_model.fit(X_train, Y_tarin)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DHZ78llUxoS4"
      },
      "source": [
        "Model Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7TlDgNf3xh6b"
      },
      "source": [
        "# prediction on Training data\n",
        "\n",
        "tarining_data_pred = lin_reg_model.predict(X_train)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yRrAtV2Kx2kG",
        "outputId": "e8b88e55-c1f4-42dc-d592-49c2c4416a96"
      },
      "source": [
        "# R squared Error for regression\n",
        "\n",
        "error_score = metrics.r2_score(Y_tarin, tarining_data_pred)\n",
        "print('R squared Error: ', error_score)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R squared Error:  0.8799451660493711\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GL4ZsYr3yklz"
      },
      "source": [
        "Visualize the actual price and predicted price"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "B4zNBuInyRdm",
        "outputId": "a5f4d360-9401-4029-8728-bc06d6662b5c"
      },
      "source": [
        "plt.scatter(Y_tarin, tarining_data_pred)\n",
        "plt.xlabel('Actual Price')\n",
        "plt.ylabel('Predictied Price')\n",
        "plt.title('Actual Prices vs Predictied Prices')\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "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": "XjRgYDmYzAY3"
      },
      "source": [
        "# prediction on Test data\n",
        "\n",
        "test_data_pred = lin_reg_model.predict(X_test)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ETCcfva3zUfh",
        "outputId": "033a7c57-1cb9-45a5-c89f-314f776275ad"
      },
      "source": [
        "# R squared Error for regression\n",
        "\n",
        "error_score = metrics.r2_score(Y_test, test_data_pred)\n",
        "print('R squared Error: ', error_score)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R squared Error:  0.8365766715027051\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "3ec4BHKUzdid",
        "outputId": "17bb688f-f6e1-49d4-ca51-c3957e9c904b"
      },
      "source": [
        "plt.scatter(Y_test, test_data_pred)\n",
        "plt.xlabel('Actual Price')\n",
        "plt.ylabel('Predictied Price')\n",
        "plt.title('Actual Prices vs Predictied Prices')\n",
        "plt.show()"
      ],
      "execution_count": 23,
      "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": "MfGRwXBWz9fp"
      },
      "source": [
        "2. Lasso Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BJGyC7V20WEr"
      },
      "source": [
        "# loading the lasso regression model\n",
        "\n",
        "lasso_reg_model = Lasso()"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H5HeAGVG0WE0",
        "outputId": "84d0a9a3-2a76-4094-d3b1-f1ebe862932e"
      },
      "source": [
        "lasso_reg_model.fit(X_train, Y_tarin)"
      ],
      "execution_count": 25,
      "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": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bbffvDxj0WE0"
      },
      "source": [
        "Model Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N5tn4g3g0WE1"
      },
      "source": [
        "# prediction on Training data\n",
        "\n",
        "tarining_data_pred = lasso_reg_model.predict(X_train)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iDWEDzk20WE1",
        "outputId": "f7a1101f-9f4a-4430-9d0d-40b39f979aa9"
      },
      "source": [
        "# R squared Error for regression\n",
        "\n",
        "error_score = metrics.r2_score(Y_tarin, tarining_data_pred)\n",
        "print('R squared Error: ', error_score)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R squared Error:  0.8427856123435794\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FaqjZVbP0WE1"
      },
      "source": [
        "Visualize the actual price and predicted price"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "qs5IqNag0WE1",
        "outputId": "be9923e2-7fc9-49a3-90b9-679130681d7b"
      },
      "source": [
        "plt.scatter(Y_tarin, tarining_data_pred)\n",
        "plt.xlabel('Actual Price')\n",
        "plt.ylabel('Predictied Price')\n",
        "plt.title('Actual Prices vs Predictied Prices')\n",
        "plt.show()"
      ],
      "execution_count": 28,
      "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": "4Y_KN0hq0WE2"
      },
      "source": [
        "# prediction on Test data\n",
        "\n",
        "test_data_pred = lasso_reg_model.predict(X_test)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w7yrSLMg0WE2",
        "outputId": "7dbc16a8-6aca-4b2d-e79d-a7e371d80948"
      },
      "source": [
        "# R squared Error for regression\n",
        "\n",
        "error_score = metrics.r2_score(Y_test, test_data_pred)\n",
        "print('R squared Error: ', error_score)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R squared Error:  0.8709167941173195\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "hSlZXOIp0WE3",
        "outputId": "74b9f8a9-d925-4b7d-c32e-016b87060316"
      },
      "source": [
        "plt.scatter(Y_test, test_data_pred)\n",
        "plt.xlabel('Actual Price')\n",
        "plt.ylabel('Predictied Price')\n",
        "plt.title('Actual Prices vs Predictied Prices')\n",
        "plt.show()"
      ],
      "execution_count": 31,
      "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": "NuvpFIQM029S"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}