Car_pice_prediction.ipynb

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  "metadata": {
    "colab": {
      "name": "Car pice prediction.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyN5jJ1dGBOKbYtJf3aI85Sa",
      "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/itsabdullah15/Basic-ML-Projects-/blob/main/Car_pice_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": "V-COx1SxPPpU"
      },
      "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": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "940cn1hfSItk"
      },
      "source": [
        "# Uploading data from csv file to pd dataframe\n",
        "car_dataset = pd.read_csv('/content/car data.csv')"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "rgEgQX64TrV3",
        "outputId": "4c810525-26f0-4d0b-a10d-63e3f7007d47"
      },
      "source": [
        "# inspecting the first 5 rows \n",
        "car_dataset.head()"
      ],
      "execution_count": 42,
      "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": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DVC7NH7WTy53",
        "outputId": "d2f848ad-7d48-4fc1-f9a9-7a8168941e64"
      },
      "source": [
        "# checking the number of rows and columns\n",
        "car_dataset.shape"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(301, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xEsF8KqlT9V4",
        "outputId": "781e8070-ac72-4325-aa82-438f072d7121"
      },
      "source": [
        "# getting information about the dataset\n",
        "car_dataset.info()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 301 entries, 0 to 300\n",
            "Data columns (total 9 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Car_Name       301 non-null    object \n",
            " 1   Year           301 non-null    int64  \n",
            " 2   Selling_Price  301 non-null    float64\n",
            " 3   Present_Price  301 non-null    float64\n",
            " 4   Kms_Driven     301 non-null    int64  \n",
            " 5   Fuel_Type      301 non-null    object \n",
            " 6   Seller_Type    301 non-null    object \n",
            " 7   Transmission   301 non-null    object \n",
            " 8   Owner          301 non-null    int64  \n",
            "dtypes: float64(2), int64(3), object(4)\n",
            "memory usage: 21.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t-7SaGNUUFn5",
        "outputId": "a1c6ffa1-a2e7-41d1-9a0b-dd2bf3773e06"
      },
      "source": [
        "# checking the number of missing values\n",
        "car_dataset.isnull().sum()"
      ],
      "execution_count": 45,
      "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": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q-6WypgGXxZ_",
        "outputId": "a6a4726d-0420-4c0a-bd34-6cd092418878"
      },
      "source": [
        "# checking the distribution of categorical data\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": 46,
      "outputs": [
        {
          "output_type": "stream",
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zta9jxMmYWqz"
      },
      "source": [
        "# encoding \"Fuel_Type\" Column\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": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "mRXBH5dAaYep",
        "outputId": "bdc1a830-e914-4a30-db5e-e4b620276a2f"
      },
      "source": [
        "car_dataset.head()"
      ],
      "execution_count": 48,
      "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>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": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HWu6HMg3akkD"
      },
      "source": [
        "X = car_dataset.drop(['Car_Name','Selling_Price'],axis=1)\n",
        "Y = car_dataset['Selling_Price']"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3-JmiC-hcXOf",
        "outputId": "291c2be8-dfb2-4c52-afd3-d87db5acd167"
      },
      "source": [
        "print(X)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HIuBKGQWc0ZB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d9763994-d1ed-48b4-82a0-18beea90f6af"
      },
      "source": [
        "print(Y)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-1mYJ-KFdIGF"
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.1, random_state=2)"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7pZWyl2adX9x"
      },
      "source": [
        "# loading the linear regression model\n",
        "lin_reg_model = LinearRegression()"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fYirp5HzdoKw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "586baad9-5cbe-4713-f177-dfeba5a6b072"
      },
      "source": [
        "lin_reg_model.fit(X_train,Y_train)"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AYaMWDjVht-a"
      },
      "source": [
        "# prediction on Training data\n",
        "training_data_prediction = lin_reg_model.predict(X_train)"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JQNbf6jYjtqx",
        "outputId": "79ff7593-74e2-49c3-acf8-2b214b2bb1ec"
      },
      "source": [
        "# R squared Error\n",
        "error_score = metrics.r2_score(Y_train, training_data_prediction)\n",
        "print(\"R squared Error : \", error_score)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R squared Error :  0.8799451660493716\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Kfs7DJXFjzfk",
        "outputId": "b3fa1f77-8386-48a8-cb58-598d598dd423"
      },
      "source": [
        "plt.scatter(Y_train, training_data_prediction)\n",
        "plt.xlabel(\"Actual Price\")\n",
        "plt.ylabel(\"Predicted Price\")\n",
        "plt.title(\" Actual Prices vs Predicted Prices\")\n",
        "plt.show()"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ddUDpaCyj1Ym"
      },
      "source": [
        "# prediction on Training data\n",
        "test_data_prediction = lin_reg_model.predict(X_test)"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Cbk1xKuj6QQ",
        "outputId": "fd075433-f723-4766-cc4d-8771e10e8905"
      },
      "source": [
        "# R squared Error\n",
        "error_score = metrics.r2_score(Y_test, test_data_prediction)\n",
        "print(\"R squared Error : \", error_score)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R squared Error :  0.8365766715027014\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Z-ikiqqSj8je",
        "outputId": "0f81c10f-1b8a-46fb-b89a-688bf7ca3f7d"
      },
      "source": [
        "plt.scatter(Y_test, test_data_prediction)\n",
        "plt.xlabel(\"Actual Price\")\n",
        "plt.ylabel(\"Predicted Price\")\n",
        "plt.title(\" Actual Prices vs Predicted Prices\")\n",
        "plt.show()"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j3eO1zIKj-Yw"
      },
      "source": [
        "\n",
        "\n",
        "# loading the linear regression model\n",
        "lass_reg_model = Lasso()"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YFrI23jikCmh",
        "outputId": "103c0792-0150-4a49-fbf7-c372091235ef"
      },
      "source": [
        "lass_reg_model.fit(X_train,Y_train)"
      ],
      "execution_count": 63,
      "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": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tX1qudMGkEjr"
      },
      "source": [
        "# prediction on Training data\n",
        "training_data_prediction = lass_reg_model.predict(X_train)"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K3uIPxSKkHBG",
        "outputId": "2e247a2c-0428-4062-9bc8-afb2e8631c8c"
      },
      "source": [
        "# R squared Error\n",
        "error_score = metrics.r2_score(Y_train, training_data_prediction)\n",
        "print(\"R squared Error : \", error_score)"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R squared Error :  0.8427856123435795\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "mjVTs7IKkJkI",
        "outputId": "9c9981df-9154-45a3-c89b-3136c99d021f"
      },
      "source": [
        "plt.scatter(Y_train, training_data_prediction)\n",
        "plt.xlabel(\"Actual Price\")\n",
        "plt.ylabel(\"Predicted Price\")\n",
        "plt.title(\" Actual Prices vs Predicted Prices\")\n",
        "plt.show()"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RUkpW2qokQ7T"
      },
      "source": [
        "# prediction on Training data\n",
        "test_data_prediction = lass_reg_model.predict(X_test)"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ndVVHifckUse",
        "outputId": "6fa96979-82bf-46ab-adda-b60e7f8f54e9"
      },
      "source": [
        "# R squared Error\n",
        "error_score = metrics.r2_score(Y_test, test_data_prediction)\n",
        "print(\"R squared Error : \", error_score)"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R squared Error :  0.8709167941173201\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "0ZmbDnFHkWNu",
        "outputId": "f0d97dc5-6dfc-44bd-9435-f63f1c25c647"
      },
      "source": [
        "plt.scatter(Y_test, test_data_prediction)\n",
        "plt.xlabel(\"Actual Price\")\n",
        "plt.ylabel(\"Predicted Price\")\n",
        "plt.title(\" Actual Prices vs Predicted Prices\")\n",
        "plt.show()"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ar_IGp68kXwO"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}