Car_Price_Prediction_Model_.ipynb

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      "cell_type": "markdown",
      "source": [
        "## Import Dependencies \n",
        "  - Importing libraries for using them"
      ],
      "metadata": {
        "id": "T3AGijxGYxM4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import Lasso\n",
        "from sklearn import metrics # for accuracy and precision in model evaluation"
      ],
      "metadata": {
        "id": "tzqSr-hFY6AX"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finding Current Working Directory "
      ],
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    {
      "cell_type": "code",
      "source": [
        "print(os.getcwd())"
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      "execution_count": 6,
      "outputs": [
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          "text": [
            "/content\n"
          ]
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    {
      "cell_type": "markdown",
      "source": [
        "## Data Collection and Preprocessing \n",
        "  - Download Vehicle dataset from kaggle website "
      ],
      "metadata": {
        "id": "4nlqct6OZoVB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Loading the data from csv file to pandas data frame\n",
        "car_dataset = pd.read_csv(\"/content/car data.csv\" , na_filter = True)"
      ],
      "metadata": {
        "id": "-uMoYOi-Zgnv"
      },
      "execution_count": 7,
      "outputs": []
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      "source": [
        "# Inspecting the first 5 rows of dataset \n",
        "car_dataset.head()"
      ],
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      "execution_count": 8,
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              "  Car_Name  Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type  \\\n",
              "0     ritz  2014           3.35           5.59       27000    Petrol   \n",
              "1      sx4  2013           4.75           9.54       43000    Diesel   \n",
              "2     ciaz  2017           7.25           9.85        6900    Petrol   \n",
              "3  wagon r  2011           2.85           4.15        5200    Petrol   \n",
              "4    swift  2014           4.60           6.87       42450    Diesel   \n",
              "\n",
              "  Seller_Type Transmission  Owner  \n",
              "0      Dealer       Manual      0  \n",
              "1      Dealer       Manual      0  \n",
              "2      Dealer       Manual      0  \n",
              "3      Dealer       Manual      0  \n",
              "4      Dealer       Manual      0  "
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              "      <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",
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      "source": [
        "# checking the number of rows and columns in dataset\n",
        "car_dataset.shape"
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        "# Inspecting the last 10 rows of dataset \n",
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              "    Car_Name  Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type  \\\n",
              "291     brio  2015           5.40           6.10       31427    Petrol   \n",
              "292     jazz  2016           6.40           8.40       12000    Petrol   \n",
              "293     city  2010           3.25           9.90       38000    Petrol   \n",
              "294    amaze  2014           3.75           6.80       33019    Petrol   \n",
              "295     city  2015           8.55          13.09       60076    Diesel   \n",
              "296     city  2016           9.50          11.60       33988    Diesel   \n",
              "297     brio  2015           4.00           5.90       60000    Petrol   \n",
              "298     city  2009           3.35          11.00       87934    Petrol   \n",
              "299     city  2017          11.50          12.50        9000    Diesel   \n",
              "300     brio  2016           5.30           5.90        5464    Petrol   \n",
              "\n",
              "    Seller_Type Transmission  Owner  \n",
              "291      Dealer       Manual      0  \n",
              "292      Dealer       Manual      0  \n",
              "293      Dealer       Manual      0  \n",
              "294      Dealer       Manual      0  \n",
              "295      Dealer       Manual      0  \n",
              "296      Dealer       Manual      0  \n",
              "297      Dealer       Manual      0  \n",
              "298      Dealer       Manual      0  \n",
              "299      Dealer       Manual      0  \n",
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              "      <td>6.80</td>\n",
              "      <td>33019</td>\n",
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              "  </style>\n",
              "\n",
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              "\n",
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              "                                                     [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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# getting information about dataset\n",
        "car_dataset.info()"
      ],
      "metadata": {
        "id": "8buaLYKwcmXc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a4a0db5-d3a0-4277-a3c3-ec772e0ec355"
      },
      "execution_count": 11,
      "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",
      "source": [
        "# Finding Missing values in dataset\n",
        " \n",
        "car_dataset.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zlU_fYrnqUP_",
        "outputId": "0fd638c0-560e-43d2-fecd-420790643889"
      },
      "execution_count": 12,
      "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": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Total null or missing values\n",
        "\n",
        "car_dataset.isnull().sum().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ebR1OrMqe4m",
        "outputId": "55630748-6e86-4034-aad9-834338e0bce0"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "car_dataset.head(8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "OA44nBjprH1G",
        "outputId": "22671b8a-30a3-4b7d-b3ee-cef52ea29a6b"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Car_Name  Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type  \\\n",
              "0           ritz  2014           3.35           5.59       27000    Petrol   \n",
              "1            sx4  2013           4.75           9.54       43000    Diesel   \n",
              "2           ciaz  2017           7.25           9.85        6900    Petrol   \n",
              "3        wagon r  2011           2.85           4.15        5200    Petrol   \n",
              "4          swift  2014           4.60           6.87       42450    Diesel   \n",
              "5  vitara brezza  2018           9.25           9.83        2071    Diesel   \n",
              "6           ciaz  2015           6.75           8.12       18796    Petrol   \n",
              "7        s cross  2015           6.50           8.61       33429    Diesel   \n",
              "\n",
              "  Seller_Type Transmission  Owner  \n",
              "0      Dealer       Manual      0  \n",
              "1      Dealer       Manual      0  \n",
              "2      Dealer       Manual      0  \n",
              "3      Dealer       Manual      0  \n",
              "4      Dealer       Manual      0  \n",
              "5      Dealer       Manual      0  \n",
              "6      Dealer       Manual      0  \n",
              "7      Dealer       Manual      0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-48a15b52-4de5-4a55-84b9-19f4935d630e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <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",
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              "      <td>ritz</td>\n",
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              "      <td>5.59</td>\n",
              "      <td>27000</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ciaz</td>\n",
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              "      <td>9.85</td>\n",
              "      <td>6900</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>wagon r</td>\n",
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              "      <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",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>vitara brezza</td>\n",
              "      <td>2018</td>\n",
              "      <td>9.25</td>\n",
              "      <td>9.83</td>\n",
              "      <td>2071</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>ciaz</td>\n",
              "      <td>2015</td>\n",
              "      <td>6.75</td>\n",
              "      <td>8.12</td>\n",
              "      <td>18796</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>s cross</td>\n",
              "      <td>2015</td>\n",
              "      <td>6.50</td>\n",
              "      <td>8.61</td>\n",
              "      <td>33429</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
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          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking the distribution of categorical data \n",
        "print(car_dataset.Car_Name.value_counts().head(10),\"\\n\")\n",
        "print(car_dataset.Fuel_Type.value_counts(),\"\\n\")\n",
        "print(car_dataset.Seller_Type.value_counts(),\"\\n\")\n",
        "print(car_dataset.Transmission.value_counts(),\"\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I5s2_QgIqmB2",
        "outputId": "851b562e-5be3-40c1-ef69-657f7dcd5fb7"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "city             26\n",
            "corolla altis    16\n",
            "verna            14\n",
            "fortuner         11\n",
            "brio             10\n",
            "ciaz              9\n",
            "innova            9\n",
            "i20               9\n",
            "grand i10         8\n",
            "jazz              7\n",
            "Name: Car_Name, dtype: int64 \n",
            "\n",
            "Petrol    239\n",
            "Diesel     60\n",
            "CNG         2\n",
            "Name: Fuel_Type, dtype: int64 \n",
            "\n",
            "Dealer        195\n",
            "Individual    106\n",
            "Name: Seller_Type, dtype: int64 \n",
            "\n",
            "Manual       261\n",
            "Automatic     40\n",
            "Name: Transmission, dtype: int64 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Encoding the Categorical data "
      ],
      "metadata": {
        "id": "KPVnQfs4sNel"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Encoding the data 'Fuel_type' Column\n",
        "# Inplace is true as we have to change actual data rather than copy of data in dataset\n",
        "\n",
        "car_dataset.replace({'Fuel_Type' :{'Petrol' : 0, 'Diesel' : 1, 'CNG' : 2 }} , inplace = True)\n",
        "car_dataset.Fuel_Type.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LK3ZR9GesMUd",
        "outputId": "2ce28969-06b7-4d29-87a0-c66cd9de0d27"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    1\n",
              "2    0\n",
              "3    0\n",
              "4    1\n",
              "5    1\n",
              "6    0\n",
              "7    1\n",
              "8    1\n",
              "9    1\n",
              "Name: Fuel_Type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Encoding the data 'Seller_type' Column\n",
        "\n",
        "car_dataset.replace({'Seller_Type' :{'Dealer' : 0, 'Individual' : 1 }} , inplace = True)\n",
        "car_dataset.Seller_Type.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KDsxW_58stbc",
        "outputId": "66101dae-f802-4ee9-9c10-9067b87597bc"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    0\n",
              "2    0\n",
              "3    0\n",
              "4    0\n",
              "5    0\n",
              "6    0\n",
              "7    0\n",
              "8    0\n",
              "9    0\n",
              "Name: Seller_Type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Encoding the data 'Transmission' Column\n",
        "\n",
        "car_dataset.replace({'Transmission' :{'Manual' : 0, 'Automatic' : 1 }} , inplace = True)\n",
        "car_dataset.Transmission.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8IedbJWXtYQ0",
        "outputId": "cc2c3013-3888-4d92-e8cf-912b7e2f4049"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    0\n",
              "2    0\n",
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              "7    0\n",
              "8    0\n",
              "9    0\n",
              "Name: Transmission, dtype: int64"
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          "metadata": {},
          "execution_count": 18
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    {
      "cell_type": "code",
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        "car_dataset.head(8)"
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        "id": "fULfpiDYtqnN",
        "outputId": "7e2c3c80-4a58-4b88-c6bb-4d4308ea6335"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Car_Name  Year  Selling_Price  Present_Price  Kms_Driven  Fuel_Type  \\\n",
              "0           ritz  2014           3.35           5.59       27000          0   \n",
              "1            sx4  2013           4.75           9.54       43000          1   \n",
              "2           ciaz  2017           7.25           9.85        6900          0   \n",
              "3        wagon r  2011           2.85           4.15        5200          0   \n",
              "4          swift  2014           4.60           6.87       42450          1   \n",
              "5  vitara brezza  2018           9.25           9.83        2071          1   \n",
              "6           ciaz  2015           6.75           8.12       18796          0   \n",
              "7        s cross  2015           6.50           8.61       33429          1   \n",
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          "metadata": {},
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        }
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "## Splitting the data into Training and Test data "
      ],
      "metadata": {
        "id": "SxHz_AmCuFdM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = car_dataset.drop({'Car_Name' , 'Selling_Price'}, axis =1)\n",
        "Y = car_dataset['Selling_Price']"
      ],
      "metadata": {
        "id": "vDo1h3Mct1FE"
      },
      "execution_count": 23,
      "outputs": []
    },
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        },
        "id": "nV7tSv8G4rEN",
        "outputId": "8b3742b7-f6c6-41fe-acb3-41de62d40041"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Year  Present_Price  Kms_Driven  Fuel_Type  Seller_Type  Transmission  \\\n",
            "0    2014           5.59       27000          0            0             0   \n",
            "1    2013           9.54       43000          1            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          1            0             0   \n",
            "..    ...            ...         ...        ...          ...           ...   \n",
            "296  2016          11.60       33988          1            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          1            0             0   \n",
            "300  2016           5.90        5464          0            0             0   \n",
            "\n",
            "     Owner  \n",
            "0        0  \n",
            "1        0  \n",
            "2        0  \n",
            "3        0  \n",
            "4        0  \n",
            "..     ...  \n",
            "296      0  \n",
            "297      0  \n",
            "298      0  \n",
            "299      0  \n",
            "300      0  \n",
            "\n",
            "[301 rows x 7 columns]\n"
          ]
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    {
      "cell_type": "code",
      "source": [
        "print(Y)"
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        },
        "id": "TY9sVQ5b48d0",
        "outputId": "0d19a5db-83a4-4dab-cc01-d579fb562339"
      },
      "execution_count": 25,
      "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",
      "source": [
        "## Splitting data into training and test data"
      ],
      "metadata": {
        "id": "p7Vqj3iI4_2m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size = 0.1, random_state = 2 )"
      ],
      "metadata": {
        "id": "9TCQejS545Od"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test"
      ],
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        "colab": {
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          "height": 1000
        },
        "id": "r7602YQ85ySM",
        "outputId": "3ac0b1d9-148e-4637-f7ca-82f3ece9c8fc"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Year  Present_Price  Kms_Driven  Fuel_Type  Seller_Type  Transmission  \\\n",
              "99   2010         20.450       50024          1            0             0   \n",
              "161  2014          0.826       23000          0            1             0   \n",
              "89   2014          6.760       40000          0            0             0   \n",
              "30   2012          5.980       51439          1            0             0   \n",
              "232  2015         14.790       12900          0            0             1   \n",
              "290  2014          6.400       19000          0            0             0   \n",
              "35   2011          7.740       49998          2            0             0   \n",
              "7    2015          8.610       33429          1            0             0   \n",
              "183  2013          0.470       21000          0            1             0   \n",
              "13   2015          7.710       26000          0            0             0   \n",
              "269  2015         10.000       18828          0            0             0   \n",
              "65   2014          6.950       45000          1            0             0   \n",
              "178  2014          0.520       19000          0            1             1   \n",
              "258  2015         13.600       25000          0            0             0   \n",
              "227  2011          4.430       57000          0            0             0   \n",
              "133  2016          0.950         500          0            1             0   \n",
              "130  2017          0.870       11000          0            1             0   \n",
              "156  2017          0.520       15000          0            1             0   \n",
              "237  2015         13.600       68000          1            0             0   \n",
              "262  2015          5.800       40023          0            0             0   \n",
              "112  2014          2.400        7000          0            1             0   \n",
              "282  2014         14.000       63000          1            0             0   \n",
              "164  2016          0.540       14000          0            1             0   \n",
              "275  2016         13.600       30753          0            0             1   \n",
              "154  2014          0.880        8000          0            1             0   \n",
              "29   2015         10.380       45000          1            0             0   \n",
              "141  2016          0.800       20000          0            1             0   \n",
              "192  2007          0.750       49000          0            1             0   \n",
              "216  2016          4.430       12500          0            0             0   \n",
              "3    2011          4.150        5200          0            0             0   \n",
              "159  2017          0.510        4000          0            1             1   \n",
              "\n",
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              "154      0  \n",
              "29       0  \n",
              "141      0  \n",
              "192      1  \n",
              "216      0  \n",
              "3        0  \n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>269</th>\n",
              "      <td>2015</td>\n",
              "      <td>10.000</td>\n",
              "      <td>18828</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>2014</td>\n",
              "      <td>6.950</td>\n",
              "      <td>45000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>2014</td>\n",
              "      <td>0.520</td>\n",
              "      <td>19000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>258</th>\n",
              "      <td>2015</td>\n",
              "      <td>13.600</td>\n",
              "      <td>25000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>227</th>\n",
              "      <td>2011</td>\n",
              "      <td>4.430</td>\n",
              "      <td>57000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>2016</td>\n",
              "      <td>0.950</td>\n",
              "      <td>500</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>2017</td>\n",
              "      <td>0.870</td>\n",
              "      <td>11000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>2017</td>\n",
              "      <td>0.520</td>\n",
              "      <td>15000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>237</th>\n",
              "      <td>2015</td>\n",
              "      <td>13.600</td>\n",
              "      <td>68000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>262</th>\n",
              "      <td>2015</td>\n",
              "      <td>5.800</td>\n",
              "      <td>40023</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>2014</td>\n",
              "      <td>2.400</td>\n",
              "      <td>7000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>282</th>\n",
              "      <td>2014</td>\n",
              "      <td>14.000</td>\n",
              "      <td>63000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164</th>\n",
              "      <td>2016</td>\n",
              "      <td>0.540</td>\n",
              "      <td>14000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>275</th>\n",
              "      <td>2016</td>\n",
              "      <td>13.600</td>\n",
              "      <td>30753</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>2014</td>\n",
              "      <td>0.880</td>\n",
              "      <td>8000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>2015</td>\n",
              "      <td>10.380</td>\n",
              "      <td>45000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>2016</td>\n",
              "      <td>0.800</td>\n",
              "      <td>20000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>2007</td>\n",
              "      <td>0.750</td>\n",
              "      <td>49000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>216</th>\n",
              "      <td>2016</td>\n",
              "      <td>4.430</td>\n",
              "      <td>12500</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>2011</td>\n",
              "      <td>4.150</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>159</th>\n",
              "      <td>2017</td>\n",
              "      <td>0.510</td>\n",
              "      <td>4000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e5046b21-0305-434c-9d80-b72209e7ba27')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-e5046b21-0305-434c-9d80-b72209e7ba27 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-e5046b21-0305-434c-9d80-b72209e7ba27');\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": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training Model using Regresion model"
      ],
      "metadata": {
        "id": "Ddn7QRxT54-k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Loading Linear Regression Model\n",
        "lr_model = LinearRegression()"
      ],
      "metadata": {
        "id": "hmt8Hg2b51Q7"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr_model.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iiz0nQbQ5_qb",
        "outputId": "a97c2d0a-a4af-4636-cb40-ce63f09fcccd"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model Evaluation in Linear Regression"
      ],
      "metadata": {
        "id": "8nyKsxgd6OJq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Prediction based on Training Data \n",
        "tr_data_prediction = lr_model.predict(X_train)\n",
        "# tr_data_prediction"
      ],
      "metadata": {
        "id": "fKx5kPdz6KTs"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# R squared Error for Training data\n",
        "\n",
        "error_score = metrics.r2_score(Y_train, tr_data_prediction)\n",
        "print(\"R squared error : \", error_score)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Cn4_D0d6lNS",
        "outputId": "bba5ab19-2d3c-4f24-9254-f6e73ad38fed"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R squared error :  0.8799451660493711\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot predicted and actual prices of model\n",
        "# Both must be close to each other\n",
        "\n",
        "plt.figure(figsize=(20,10))\n",
        "plt.scatter(Y_train, tr_data_prediction, s =100, color = 'green')\n",
        "plt.xlabel(\"Actual Price\")\n",
        "plt.ylabel(\"Predicted Price\")\n",
        "plt.title(\" Actual Prices VS Predicted Prices\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "jcdhj0EE7Eey",
        "outputId": "da2a07de-b6ae-4ea8-887f-c52bf02443f5"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Prediction based on Test Data \n",
        "test_data_prediction = lr_model.predict(X_test)\n",
        "# test_data_prediction"
      ],
      "metadata": {
        "id": "YBz9_Ha19WGj"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# R squared Error for Test data\n",
        "\n",
        "error_score_1 = metrics.r2_score(Y_test, test_data_prediction)\n",
        "print(\"R squared error : \", error_score_1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HTcr41Rn9qFZ",
        "outputId": "36c330a6-3ebc-4809-e0e4-0cf2edebad96"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R squared error :  0.8365766715027051\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot predicted and actual prices of model using test data\n",
        "# Both must be close to each other\n",
        "\n",
        "plt.figure(figsize=(20,10))\n",
        "plt.scatter(Y_test, test_data_prediction , s =100, color = 'green')\n",
        "plt.xlabel(\"Actual Price\")\n",
        "plt.ylabel(\"Predicted Price\")\n",
        "plt.title(\" Actual Prices VS Predicted Prices\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "R0OC16fg70uU",
        "outputId": "b2e8e3c6-955f-4b44-a833-6c7d424ca2e1"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Using Lasso Regression"
      ],
      "metadata": {
        "id": "dxSyyQmG-VL7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Loading Linear Regression Model\n",
        "lasso_model = Lasso()"
      ],
      "metadata": {
        "id": "KVu_jSzg-rAS"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lasso_model.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a521dae-5fb0-48f8-d47b-5b7ec2a827b4",
        "id": "j_zB7hw4-rAU"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Lasso()"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model Evaluation in Lasso Regression"
      ],
      "metadata": {
        "id": "kE1GRQgO-rAV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Prediction based on Training Data \n",
        "tr_data_prediction = lasso_model.predict(X_train)\n",
        "# tr_data_prediction"
      ],
      "metadata": {
        "id": "j3PE87hP-rAW"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# R squared Error for Training data\n",
        "\n",
        "error_score = metrics.r2_score(Y_train, tr_data_prediction)\n",
        "print(\"R squared error : \", error_score)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f381432a-5be8-4839-b82d-2edf6ce9809c",
        "id": "Y6AiGTrC-rAW"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R squared error :  0.8427856123435794\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot predicted and actual prices of model\n",
        "# Both must be close to each other\n",
        "\n",
        "plt.figure(figsize=(20,10))\n",
        "plt.scatter(Y_train, tr_data_prediction, s =100, color = 'red')\n",
        "plt.xlabel(\"Actual Price\")\n",
        "plt.ylabel(\"Predicted Price\")\n",
        "plt.title(\" Actual Prices VS Predicted Prices\")\n",
        "plt.show()\n",
        "\n",
        "# Here we have better fit than Linear Regression "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "outputId": "891014c8-f2ae-40c0-d1d3-5b3b8a9a630f",
        "id": "BD2vHLdD-rAX"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Prediction based on Test Data \n",
        "test_data_prediction = lasso_model.predict(X_test)\n",
        "# test_data_prediction"
      ],
      "metadata": {
        "id": "C9zDZoHQ-rAY"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# R squared Error for Test data\n",
        "\n",
        "error_score_1 = metrics.r2_score(Y_test, test_data_prediction)\n",
        "print(\"R squared error : \", error_score_1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "45ac8618-ffeb-4e19-bd01-35ee4b3949a3",
        "id": "BKBx-GE0-rAY"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R squared error :  0.8709167941173195\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot predicted and actual prices of model using test data\n",
        "# Both must be close to each other\n",
        "\n",
        "plt.figure(figsize=(20,10))\n",
        "plt.scatter(Y_test, test_data_prediction , s =100, color = 'red')\n",
        "plt.xlabel(\"Actual Price\")\n",
        "plt.ylabel(\"Predicted Price\")\n",
        "plt.title(\" Actual Prices VS Predicted Prices\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "outputId": "7836af3b-8c70-478e-ac9c-e30b18a436dc",
        "id": "034U9u-z-rAY"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Hence we have created Car Price Prediction Model using both Linear Regression and Lasso Regression"
      ],
      "metadata": {
        "id": "DCBsi4GQ_nBg"
      }
    }
  ]
}