Car_price_prediction (1).ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "NAME: Sondos Mohamed\n",
        "\n",
        "\n",
        "Task3 in Oasis Infobyte intership\n",
        "\n",
        "In this exciting machine learning project, I will guide you through the intricate process of training a car price prediction model. Delving into essential factors such as brand reputation, car features, horsepower, and mileage, we'll explore the dynamic world of predicting car prices. This engaging project aligns with Task 3 of the Oasis Infobyte internship, offering a hands-on opportunity to master the art of machine learning in the realm of car price prediction. Let's embark on this journey together!"
      ],
      "metadata": {
        "id": "c_RelZeJXPp3"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "mRX4VG8HEJbu"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/car data.csv')\n",
        "print(df.head(5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0cX1YMc7ESw8",
        "outputId": "564b1e15-5a6b-4c04-a353-4e54aa591cb1"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Car_Name  Year  Selling_Price  Present_Price  Driven_kms 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",
            "  Selling_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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B9rzvuPHEjR8",
        "outputId": "86d9b636-964e-4182-9433-d812000bdeae"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.describe of     Car_Name  Year  Selling_Price  Present_Price  Driven_kms 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",
              "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",
              "    Selling_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",
              "..           ...          ...    ...  \n",
              "296       Dealer       Manual      0  \n",
              "297       Dealer       Manual      0  \n",
              "298       Dealer       Manual      0  \n",
              "299       Dealer       Manual      0  \n",
              "300       Dealer       Manual      0  \n",
              "\n",
              "[301 rows x 9 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MnjM8TTzEmax",
        "outputId": "16282f47-0586-425e-f593-d8afced8e8b4"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Car_Name', 'Year', 'Selling_Price', 'Present_Price', 'Driven_kms',\n",
              "       'Fuel_Type', 'Selling_type', 'Transmission', 'Owner'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gs0QHyzyAOcS",
        "outputId": "934e8eef-9f71-403f-a6a5-923d10f485fd"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Car_Name         0\n",
              "Year             0\n",
              "Selling_Price    0\n",
              "Present_Price    0\n",
              "Driven_kms       0\n",
              "Fuel_Type        0\n",
              "Selling_type     0\n",
              "Transmission     0\n",
              "Owner            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocessing\n",
        "df = df.drop(['Car_Name', 'Selling_type'], axis=1)  # Remove irrelevant columns\n",
        "df_encoded = pd.get_dummies(df, columns=['Fuel_Type', 'Transmission'])  # Convert categorical variables"
      ],
      "metadata": {
        "id": "2jEiyz0IErK7"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split features and target variable\n",
        "X = df_encoded.drop('Selling_Price', axis=1)\n",
        "y = df_encoded['Selling_Price']"
      ],
      "metadata": {
        "id": "Yw9eMe19E_RL"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a plot using seaborn\n",
        "sns.set_style('whitegrid')\n",
        "plt.figure(figsize=(12, 10))\n",
        "sns.distplot(df_encoded['Selling_Price'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Oo7KU5JrRd_e",
        "outputId": "986766a0-6050-4fcd-8093-5caf73be946e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-2535a2ec4cfd>:4: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(df_encoded['Selling_Price'])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Check the correlation among all the features of this dataset"
      ],
      "metadata": {
        "id": "If0pyyFGSZ7e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "3VzJQpJMSTyR",
        "outputId": "2ba04c7b-77a7-4945-b799-0a28bdfa66b9"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-2f6f6606aa2c>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  df.corr()\n"
          ]
        },
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              "                   Year  Selling_Price  Present_Price  Driven_kms     Owner\n",
              "Year           1.000000       0.236141      -0.047192   -0.524342 -0.182104\n",
              "Selling_Price  0.236141       1.000000       0.878914    0.029187 -0.088344\n",
              "Present_Price -0.047192       0.878914       1.000000    0.203618  0.008058\n",
              "Driven_kms    -0.524342       0.029187       0.203618    1.000000  0.089216\n",
              "Owner         -0.182104      -0.088344       0.008058    0.089216  1.000000"
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,12))\n",
        "correlations = df.corr()\n",
        "sns.heatmap(correlations, cmap=\"coolwarm\", annot=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "wqDR2M_hS2ij",
        "outputId": "d5709a9a-3072-4a87-8ef6-294f30c00dcb"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-10-3793b13bdf3e>:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  correlations = df.corr()\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1200 with 2 Axes>"
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            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the dataset into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "7Cgv8ONlFA6H"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Preprocessing\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "UOSbBEsZAix6"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "MODEL 1: Linear Regression"
      ],
      "metadata": {
        "id": "8bVOxYnnA-5e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train a Linear Regression model\n",
        "linear_model = LinearRegression()\n",
        "linear_model.fit(X_train_scaled, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "EzUca8-VFFTH",
        "outputId": "e35911d7-f6e1-4b2e-bad2-509d5b8697d3"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the models\n",
        "linear_pred = linear_model.predict(X_test_scaled)\n",
        "linear_mse = mean_squared_error(y_test, linear_pred)\n",
        "linear_r2 = r2_score(y_test, linear_pred)\n",
        "print('Linear Regression Mean Squared Error:', linear_mse)\n",
        "print('Linear Regression R-squared:', linear_r2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lnwMCp6cFKLo",
        "outputId": "cb51e139-b2d5-434d-d0eb-d4c350a88a36"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Linear Regression Mean Squared Error: 3.577833375739793\n",
            "Linear Regression R-squared: 0.8446823273536489\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "MODEL 2: Random Forest Regression"
      ],
      "metadata": {
        "id": "eWhc1G20BMFd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train a Random Forest Regression model\n",
        "rf_model = RandomForestRegressor()\n",
        "rf_model.fit(X_train_scaled, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "0QfBuKs4GXbx",
        "outputId": "620c0570-0623-4e56-997e-5ee71b5e4e62"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf_pred = rf_model.predict(X_test_scaled)\n",
        "rf_mse = mean_squared_error(y_test, rf_pred)\n",
        "rf_r2 = r2_score(y_test, rf_pred)\n",
        "print('Random Forest Regression Mean Squared Error:', rf_mse)\n",
        "print('Random Forest Regression R-squared:', rf_r2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hX0SnRCOGZLt",
        "outputId": "08a318af-e276-4758-bf54-741e5de3e8fa"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Regression Mean Squared Error: 0.8950367359016402\n",
            "Random Forest Regression R-squared: 0.9611454732084931\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "MODEL 3:  Support Vector Regression"
      ],
      "metadata": {
        "id": "pJOY9lX-BrXW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train a Support Vector Regression model\n",
        "svr_model = SVR()\n",
        "svr_model.fit(X_train_scaled, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "BB1EG5beGd2s",
        "outputId": "0b12fe19-552e-436b-cfab-3af71f943fa8"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "svr_pred = svr_model.predict(X_test_scaled)\n",
        "svr_mse = mean_squared_error(y_test, svr_pred)\n",
        "svr_r2 = r2_score(y_test, svr_pred)\n",
        "print('Support Vector Regression Mean Squared Error:', svr_mse)\n",
        "print('Support Vector Regression R-squared:', svr_r2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MFrcsVoDGrAO",
        "outputId": "ee5dab5b-dc8d-425a-971d-5571324d6e8f"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Support Vector Regression Mean Squared Error: 5.440805924827156\n",
            "Support Vector Regression R-squared: 0.7638086448366537\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "MODEL 4: Gradient Boosting Regression"
      ],
      "metadata": {
        "id": "4t-KsZWCYadG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train a Gradient Boosting Regression model\n",
        "gb_model = GradientBoostingRegressor()\n",
        "gb_model.fit(X_train_scaled, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "wivddtv2Gvfe",
        "outputId": "098771e9-38c2-4151-b12a-91e14427350a"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingRegressor()"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gb_pred = gb_model.predict(X_test_scaled)\n",
        "gb_mse = mean_squared_error(y_test, gb_pred)\n",
        "gb_r2 = r2_score(y_test, gb_pred)\n",
        "print('Gradient Boosting Regression Mean Squared Error:', gb_mse)\n",
        "print('Gradient Boosting Regression R-squared:', gb_r2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ySReU8WiG2Dt",
        "outputId": "e3d8ae16-d39c-45ce-bd34-f4b7e4ad9834"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gradient Boosting Regression Mean Squared Error: 0.8707620187571663\n",
            "Gradient Boosting Regression R-squared: 0.9621992653153568\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "MODEL 5: Decision Tree Regression"
      ],
      "metadata": {
        "id": "dkFTXnx6P5kM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train a Decision Tree Regression model\n",
        "dt_model = DecisionTreeRegressor()\n",
        "dt_model.fit(X_train_scaled, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "eoH4W83vP4wZ",
        "outputId": "b8faee1e-bd2d-445a-cdc1-f78ec8ff1f40"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor()"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt_pred = dt_model.predict(X_test_scaled)\n",
        "dt_mse = mean_squared_error(y_test, dt_pred)\n",
        "dt_r2 = r2_score(y_test, dt_pred)\n",
        "print('Decision Tree Regression Mean Squared Error:', dt_mse)\n",
        "print('Decision Tree Regression R-squared:', dt_r2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M_D8YGaaQBLr",
        "outputId": "f9977a55-7716-49e8-abb3-a274c5f5e177"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree Regression Mean Squared Error: 2.047288524590164\n",
            "Decision Tree Regression R-squared: 0.9111249587442911\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Visualize Predictions\n",
        "plt.figure(figsize=(14, 10))\n",
        "\n",
        "# Linear Regression\n",
        "plt.subplot(221)\n",
        "plt.scatter(y_test, linear_pred)\n",
        "plt.plot([min(y_test), max(y_test)], [min(linear_pred), max(linear_pred)], color='red', linestyle='--')\n",
        "plt.xlabel('Actual Price')\n",
        "plt.ylabel('Predicted Price')\n",
        "plt.title('Linear Regression')\n",
        "\n",
        "# Random Forest Regression\n",
        "plt.subplot(222)\n",
        "plt.scatter(y_test, rf_pred)\n",
        "plt.plot([min(y_test), max(y_test)], [min(rf_pred), max(rf_pred)], color='red', linestyle='--')\n",
        "plt.xlabel('Actual Price')\n",
        "plt.ylabel('Predicted Price')\n",
        "plt.title('Random Forest Regression')\n",
        "\n",
        "# Support Vector Regression\n",
        "plt.subplot(223)\n",
        "plt.scatter(y_test, svr_pred)\n",
        "plt.plot([min(y_test), max(y_test)], [min(svr_pred), max(svr_pred)], color='red', linestyle='--')\n",
        "plt.xlabel('Actual Price')\n",
        "plt.ylabel('Predicted Price')\n",
        "plt.title('Support Vector Regression')\n",
        "\n",
        "# Gradient Boosting Regression\n",
        "plt.subplot(224)\n",
        "plt.scatter(y_test, gb_pred)\n",
        "plt.plot([min(y_test), max(y_test)], [min(gb_pred), max(gb_pred)], color='red', linestyle='--')\n",
        "plt.xlabel('Actual Price')\n",
        "plt.ylabel('Predicted Price')\n",
        "plt.title('Gradient Boosting Regression')\n",
        "\n",
        "# Decision Tree Regression\n",
        "plt.figure(figsize=(6, 4))\n",
        "plt.scatter(y_test, dt_pred)\n",
        "plt.plot([min(y_test), max(y_test)], [min(dt_pred), max(dt_pred)], color='red', linestyle='--')\n",
        "plt.xlabel('Actual Price')\n",
        "plt.ylabel('Predicted Price')\n",
        "plt.title('Decision Tree Regression')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Ta_7TBoZHBkv",
        "outputId": "bc8ada88-653f-4fc8-8396-79eb5c2cd063"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x1000 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test Scores\n",
        "test_scores = {\n",
        "    'Linear Regression': linear_r2,\n",
        "    'Random Forest Regression': rf_r2,\n",
        "    'Support Vector Regression': svr_r2,\n",
        "    'Gradient Boosting Regression': gb_r2,\n",
        "    'Decision Tree Regression': dt_r2\n",
        "}\n",
        "\n",
        "print('Test Scores:')\n",
        "for model, score in test_scores.items():\n",
        "    print(f'{model}: {score}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tcwluym--7yz",
        "outputId": "388e5e9f-8822-4292-9056-8f45da39c2a0"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Scores:\n",
            "Linear Regression: 0.8446823273536489\n",
            "Random Forest Regression: 0.9611454732084931\n",
            "Support Vector Regression: 0.7638086448366537\n",
            "Gradient Boosting Regression: 0.9621992653153568\n",
            "Decision Tree Regression: 0.9111249587442911\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Based on the test scores and visualizations, the Gradient Boosting and Decision Tree Regression models demonstrated the highest performance."
      ],
      "metadata": {
        "id": "eEqXIcNdWtkA"
      }
    }
  ]
}