2_6_15_Agricultural_Crop_Yield_Predicti.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# 📊 Project Overview: Predictive Modeling with Random Forest and Support Vector Regression\n",
        "\n",
        "## 📄 Objective\n",
        "This project aims to develop predictive models using Random Forest and Support Vector Regression (SVR) to analyze a dataset related to ZIP codes and their associated values.\n",
        "\n",
        "## 🗂️ Dataset\n",
        "- **Source**: `qs.census2007zipcode.txt`\n",
        "- **Size**: Sample of 5000 rows\n",
        "- **Key Variables**:\n",
        "  - `VALUE`: Target variable for prediction\n",
        "  - Other relevant features that influence `VALUE`\n",
        "\n",
        "## 🔍 Data Exploration\n",
        "- **Initial Analysis**:\n",
        "  - Displayed the first few rows, dataset information, and descriptive statistics.\n",
        "  - Checked for missing values and duplicate rows.\n",
        "  \n",
        "- **Visualizations**:\n",
        "  - Histogram of `VALUE` to understand its distribution.\n",
        "  - Correlation matrix to identify relationships.\n",
        "  - Scatter plot comparing `YEAR` and `VALUE`.\n",
        "\n",
        "## 🔧 Data Preprocessing\n",
        "- **Missing Values**:\n",
        "  - Dropped columns with over 60% missing values.\n",
        "  - Filled remaining missing values with mean (numerical) and mode (categorical).\n",
        "  \n",
        "- **Feature Selection**:\n",
        "  - Dropped unnecessary columns and converted data types appropriately.\n",
        "  \n",
        "- **Encoding**:\n",
        "  - One-hot encoding for categorical variables.\n",
        "  \n",
        "- **Data Splitting**:\n",
        "  - Split dataset into training and testing sets (70/30).\n",
        "\n",
        "## ⚙️ Model Development\n",
        "### 1. Random Forest Regressor\n",
        "- **Training**: Model trained on the training dataset.\n",
        "- **Predictions**: Generated predictions on the testing dataset.\n",
        "\n",
        "### 2. Support Vector Regressor\n",
        "- **Hyperparameter Tuning**: Utilized GridSearchCV to find optimal hyperparameters.\n",
        "- **Training and Predictions**: Model trained and predictions generated.\n",
        "\n",
        "## 💾 Results\n",
        "- **Predictions Saved**: Results saved to `predictions.csv`.\n",
        "- **Evaluation Metrics**:\n",
        "  - Mean Squared Error (MSE) and R² Score calculated for both models.\n",
        "  \n",
        "## 📈 Visualizations of Results\n",
        "- **Residual Plots**: Analyzed residuals for both models.\n",
        "- **Actual vs. Predicted Plots**: Compared actual and predicted values for insights into model performance.\n",
        "\n",
        "## 📁 Conclusion\n",
        "- The project successfully demonstrated the implementation of Random Forest and SVR for predictive modeling, providing insights into the dataset."
      ],
      "metadata": {
        "id": "X_RLs7HeCHMS"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "x6Cv5F0_CIrw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JAnv5Z8YCIv0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sERIdBoVCI5V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "RIkag7Iq_nHE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📚 Step 1: Importing Libraries\n",
        "In this step, we import the necessary libraries for data manipulation, visualization, and machine learning.\n",
        "\n",
        "## 🛠️ Libraries Used:\n",
        "- **NumPy**: For numerical operations and array handling.\n",
        "- **Pandas**: For data manipulation and analysis.\n",
        "- **Matplotlib**: For data visualization.\n",
        "- **Seaborn**: For enhanced visualization capabilities.\n",
        "- **Scikit-learn**: For implementing machine learning algorithms."
      ],
      "metadata": {
        "id": "VvXRadXACPzU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing necessary libraries"
      ],
      "metadata": {
        "id": "qBubIDSp060t"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd  # Library for data manipulation and analysis"
      ],
      "metadata": {
        "id": "iiou6GSB8Bvd"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np  # Library for numerical operations"
      ],
      "metadata": {
        "id": "aGeu_C6q8Byd"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor  # Random Forest Regressor model"
      ],
      "metadata": {
        "id": "b8yglC3t8B1P"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVR  # Support Vector Regressor model"
      ],
      "metadata": {
        "id": "-8sMCIEc8B3X"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split, GridSearchCV  # Functions for splitting data and hyperparameter tuning\n"
      ],
      "metadata": {
        "id": "Kjp6Wuw_8htf"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error, r2_score  # Metrics for evaluating model performance\n"
      ],
      "metadata": {
        "id": "63tvvGdU8hwo"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler  # Standardization of features"
      ],
      "metadata": {
        "id": "5VtyxUd18hzm"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings  # Library to manage warnings"
      ],
      "metadata": {
        "id": "Lwy7mpqr8h2e"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt  # Library for plotting"
      ],
      "metadata": {
        "id": "IDP_n0Gv8h5i"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns  # Library for statistical data visualization"
      ],
      "metadata": {
        "id": "dCX0jV8l8h8e"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "g-xGjg2r9D4v"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "warnings.filterwarnings(\"ignore\")  # Ignore warnings for cleaner output"
      ],
      "metadata": {
        "id": "MZM5yQS_9D75"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "tZbrvQRR_qla"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pUj0jnHI9FOf"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YeNCkm0c9FRf"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_WPs2TWf9FcP"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "qkJkICPv9Fov"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📂 Step 2: Loading the Dataset\n",
        "This step involves loading the dataset into a Pandas DataFrame for further analysis.\n",
        "\n",
        "## 📊 Actions:\n",
        "- **Read CSV**: The dataset is read from the specified file path using `pd.read_csv()`.\n",
        "- **Data Preview**: Display the first few rows of the DataFrame to understand its structure and contents."
      ],
      "metadata": {
        "id": "WT4fsHRxCUNO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the dataset (using a small sample for faster execution)"
      ],
      "metadata": {
        "id": "Vzg0CsxK9Gkf"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_path = \"/content/qs.census2007zipcode.txt\"  # File path for the dataset"
      ],
      "metadata": {
        "id": "EJqteg1l9D_A"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv(data_path, sep='\\t').sample(5000, random_state=42)  # Sample of 5000 rows for faster processing\n"
      ],
      "metadata": {
        "id": "aU9efNXw9ECA"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wqaPMxPB9Olg"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tnIbrGVs9Ooe"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hs7mKErrCYW0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🔍 Step 3: Data Exploration\n",
        "Data exploration helps us understand the dataset's characteristics and identify any issues.\n",
        "\n",
        "## 🔑 Activities:\n",
        "- **Information Summary**: Use `info()` to display the data types and non-null counts of each column.\n",
        "- **Descriptive Statistics**: Utilize `describe()` to view summary statistics.\n",
        "- **Missing Values**: Check for missing values and duplicate rows."
      ],
      "metadata": {
        "id": "MCh_k1fICYtD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the first few rows and basic information"
      ],
      "metadata": {
        "id": "3tOwaVqs9Orx"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"First few rows of the dataset:\")  # Output statement\n",
        "print(dataset.head())  # Show the first 5 rows of the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LdCwz8F39Oup",
        "outputId": "23945de6-8abe-4849-c0f8-ea461c777a1c"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First few rows of the dataset:\n",
            "      SOURCE_DESC         SECTOR_DESC             GROUP_DESC  \\\n",
            "25651      CENSUS  ANIMALS & PRODUCTS            AQUACULTURE   \n",
            "5405       CENSUS           ECONOMICS  FARMS & LAND & ASSETS   \n",
            "4034       CENSUS        DEMOGRAPHICS              OPERATORS   \n",
            "21857      CENSUS               CROPS            FIELD CROPS   \n",
            "27040      CENSUS           ECONOMICS                 INCOME   \n",
            "\n",
            "             COMMODITY_DESC                               CLASS_DESC  \\\n",
            "25651    AQUACULTURE TOTALS                              ALL CLASSES   \n",
            "5405                AG LAND  PASTURELAND, (EXCL CROPLAND & WOODLAND)   \n",
            "4034   OPERATORS, PRINCIPAL                              ALL CLASSES   \n",
            "21857         HAY & HAYLAGE                              ALL CLASSES   \n",
            "27040         GOVT PROGRAMS                                  FEDERAL   \n",
            "\n",
            "            PRODN_PRACTICE_DESC         UTIL_PRACTICE_DESC  \\\n",
            "25651  ALL PRODUCTION PRACTICES  ALL UTILIZATION PRACTICES   \n",
            "5405   ALL PRODUCTION PRACTICES  ALL UTILIZATION PRACTICES   \n",
            "4034    RESIDENCE, ON OPERATION  ALL UTILIZATION PRACTICES   \n",
            "21857  ALL PRODUCTION PRACTICES  ALL UTILIZATION PRACTICES   \n",
            "27040  ALL PRODUCTION PRACTICES  ALL UTILIZATION PRACTICES   \n",
            "\n",
            "          STATISTICCAT_DESC   UNIT_DESC  \\\n",
            "25651  SALES & DISTRIBUTION  OPERATIONS   \n",
            "5405                   AREA  OPERATIONS   \n",
            "4034              OPERATORS   OPERATORS   \n",
            "21857        AREA HARVESTED  OPERATIONS   \n",
            "27040              RECEIPTS  OPERATIONS   \n",
            "\n",
            "                                              SHORT_DESC  ...   COUNTRY_NAME  \\\n",
            "25651  AQUACULTURE TOTALS - OPERATIONS WITH SALES & D...  ...  UNITED STATES   \n",
            "5405   AG LAND, PASTURELAND, (EXCL CROPLAND & WOODLAN...  ...  UNITED STATES   \n",
            "4034   OPERATORS, PRINCIPAL, RESIDENCE, ON OPERATION ...  ...  UNITED STATES   \n",
            "21857     HAY & HAYLAGE - OPERATIONS WITH AREA HARVESTED  ...  UNITED STATES   \n",
            "27040  GOVT PROGRAMS, FEDERAL - OPERATIONS WITH RECEIPTS  ...  UNITED STATES   \n",
            "\n",
            "      LOCATION_DESC    YEAR  FREQ_DESC  BEGIN_CODE END_CODE  \\\n",
            "25651       36310.0  2007.0     ANNUAL         0.0      0.0   \n",
            "5405        35188.0  2007.0     ANNUAL         0.0      0.0   \n",
            "4034        35128.0  2007.0     ANNUAL         0.0      0.0   \n",
            "21857       36080.0  2007.0     ANNUAL         0.0      0.0   \n",
            "27040       36320.0  2007.0     ANNUAL         0.0      0.0   \n",
            "\n",
            "      REFERENCE_PERIOD_DESC  WEEK_ENDING            LOAD_TIME  VALUE  \n",
            "25651                  YEAR          NaN  2012-01-01 00:00:00    1.0  \n",
            "5405                   YEAR          NaN  2012-01-01 00:00:00    9.0  \n",
            "4034                   YEAR          NaN  2012-01-01 00:00:00   27.0  \n",
            "21857                  YEAR          NaN  2012-01-01 00:00:00    4.0  \n",
            "27040                  YEAR          NaN  2012-01-01 00:00:00   43.0  \n",
            "\n",
            "[5 rows x 38 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4dzzqdH99EFJ"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ktyQZUKg9EH8"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nDataset Information:\")  # Output statement\n",
        "print(dataset.info())  # Display summary information about the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BjMqUFi89ELJ",
        "outputId": "833c609b-37cf-44fa-ca23-0f3770184cc7"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Dataset Information:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 5000 entries, 25651 to 6111\n",
            "Data columns (total 38 columns):\n",
            " #   Column                 Non-Null Count  Dtype  \n",
            "---  ------                 --------------  -----  \n",
            " 0   SOURCE_DESC            5000 non-null   object \n",
            " 1   SECTOR_DESC            5000 non-null   object \n",
            " 2   GROUP_DESC             5000 non-null   object \n",
            " 3   COMMODITY_DESC         5000 non-null   object \n",
            " 4   CLASS_DESC             5000 non-null   object \n",
            " 5   PRODN_PRACTICE_DESC    5000 non-null   object \n",
            " 6   UTIL_PRACTICE_DESC     5000 non-null   object \n",
            " 7   STATISTICCAT_DESC      5000 non-null   object \n",
            " 8   UNIT_DESC              5000 non-null   object \n",
            " 9   SHORT_DESC             5000 non-null   object \n",
            " 10  DOMAIN_DESC            5000 non-null   object \n",
            " 11  DOMAINCAT_DESC         5000 non-null   object \n",
            " 12  AGG_LEVEL_DESC         5000 non-null   object \n",
            " 13  STATE_ANSI             5000 non-null   float64\n",
            " 14  STATE_FIPS_CODE        5000 non-null   float64\n",
            " 15  STATE_ALPHA            5000 non-null   object \n",
            " 16  STATE_NAME             5000 non-null   object \n",
            " 17  ASD_CODE               0 non-null      float64\n",
            " 18  ASD_DESC               0 non-null      float64\n",
            " 19  COUNTY_ANSI            0 non-null      float64\n",
            " 20  COUNTY_CODE            0 non-null      float64\n",
            " 21  COUNTY_NAME            0 non-null      float64\n",
            " 22  REGION_DESC            0 non-null      float64\n",
            " 23  ZIP_5                  5000 non-null   float64\n",
            " 24  WATERSHED_CODE         5000 non-null   float64\n",
            " 25  WATERSHED_DESC         0 non-null      float64\n",
            " 26  CONGR_DISTRICT_CODE    0 non-null      float64\n",
            " 27  COUNTRY_CODE           5000 non-null   float64\n",
            " 28  COUNTRY_NAME           5000 non-null   object \n",
            " 29  LOCATION_DESC          5000 non-null   float64\n",
            " 30  YEAR                   5000 non-null   float64\n",
            " 31  FREQ_DESC              5000 non-null   object \n",
            " 32  BEGIN_CODE             5000 non-null   float64\n",
            " 33  END_CODE               5000 non-null   float64\n",
            " 34  REFERENCE_PERIOD_DESC  5000 non-null   object \n",
            " 35  WEEK_ENDING            0 non-null      float64\n",
            " 36  LOAD_TIME              5000 non-null   object \n",
            " 37  VALUE                  5000 non-null   float64\n",
            "dtypes: float64(19), object(19)\n",
            "memory usage: 1.5+ MB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lr6SWzNi8h_P"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dMqdh5_Q8B72"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nDescriptive Statistics:\")  # Output statement\n",
        "print(dataset.describe())  # Display descriptive statistics for numerical columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b5Hgroy58CC0",
        "outputId": "e51fd75d-4f33-4746-bda7-b6adb73b712d"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Descriptive Statistics:\n",
            "       STATE_ANSI  STATE_FIPS_CODE  ASD_CODE  ASD_DESC  COUNTY_ANSI  \\\n",
            "count      5000.0           5000.0       0.0       0.0          0.0   \n",
            "mean          1.0              1.0       NaN       NaN          NaN   \n",
            "std           0.0              0.0       NaN       NaN          NaN   \n",
            "min           1.0              1.0       NaN       NaN          NaN   \n",
            "25%           1.0              1.0       NaN       NaN          NaN   \n",
            "50%           1.0              1.0       NaN       NaN          NaN   \n",
            "75%           1.0              1.0       NaN       NaN          NaN   \n",
            "max           1.0              1.0       NaN       NaN          NaN   \n",
            "\n",
            "       COUNTY_CODE  COUNTY_NAME  REGION_DESC         ZIP_5  WATERSHED_CODE  \\\n",
            "count          0.0          0.0          0.0   5000.000000          5000.0   \n",
            "mean           NaN          NaN          NaN  35793.992600             0.0   \n",
            "std            NaN          NaN          NaN    439.874995             0.0   \n",
            "min            NaN          NaN          NaN  35004.000000             0.0   \n",
            "25%            NaN          NaN          NaN  35477.000000             0.0   \n",
            "50%            NaN          NaN          NaN  35769.000000             0.0   \n",
            "75%            NaN          NaN          NaN  36109.000000             0.0   \n",
            "max            NaN          NaN          NaN  36532.000000             0.0   \n",
            "\n",
            "       WATERSHED_DESC  CONGR_DISTRICT_CODE  COUNTRY_CODE  LOCATION_DESC  \\\n",
            "count             0.0                  0.0        5000.0    5000.000000   \n",
            "mean              NaN                  NaN        9000.0   35793.992600   \n",
            "std               NaN                  NaN           0.0     439.874995   \n",
            "min               NaN                  NaN        9000.0   35004.000000   \n",
            "25%               NaN                  NaN        9000.0   35477.000000   \n",
            "50%               NaN                  NaN        9000.0   35769.000000   \n",
            "75%               NaN                  NaN        9000.0   36109.000000   \n",
            "max               NaN                  NaN        9000.0   36532.000000   \n",
            "\n",
            "         YEAR  BEGIN_CODE  END_CODE  WEEK_ENDING       VALUE  \n",
            "count  5000.0      5000.0    5000.0          0.0  5000.00000  \n",
            "mean   2007.0         0.0       0.0          NaN    21.96060  \n",
            "std       0.0         0.0       0.0          NaN    38.64907  \n",
            "min    2007.0         0.0       0.0          NaN     1.00000  \n",
            "25%    2007.0         0.0       0.0          NaN     2.00000  \n",
            "50%    2007.0         0.0       0.0          NaN     7.00000  \n",
            "75%    2007.0         0.0       0.0          NaN    24.00000  \n",
            "max    2007.0         0.0       0.0          NaN   483.00000  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sMX90msK9XYf"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BaZZfmr-9Xbo"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nMissing Values:\")  # Output statement\n",
        "print(dataset.isnull().sum())  # Display the count of missing values for each column"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DAj-JQsy9Xep",
        "outputId": "f65cee05-5eb0-45f3-df24-d9519438dcdd"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Missing Values:\n",
            "SOURCE_DESC                 0\n",
            "SECTOR_DESC                 0\n",
            "GROUP_DESC                  0\n",
            "COMMODITY_DESC              0\n",
            "CLASS_DESC                  0\n",
            "PRODN_PRACTICE_DESC         0\n",
            "UTIL_PRACTICE_DESC          0\n",
            "STATISTICCAT_DESC           0\n",
            "UNIT_DESC                   0\n",
            "SHORT_DESC                  0\n",
            "DOMAIN_DESC                 0\n",
            "DOMAINCAT_DESC              0\n",
            "AGG_LEVEL_DESC              0\n",
            "STATE_ANSI                  0\n",
            "STATE_FIPS_CODE             0\n",
            "STATE_ALPHA                 0\n",
            "STATE_NAME                  0\n",
            "ASD_CODE                 5000\n",
            "ASD_DESC                 5000\n",
            "COUNTY_ANSI              5000\n",
            "COUNTY_CODE              5000\n",
            "COUNTY_NAME              5000\n",
            "REGION_DESC              5000\n",
            "ZIP_5                       0\n",
            "WATERSHED_CODE              0\n",
            "WATERSHED_DESC           5000\n",
            "CONGR_DISTRICT_CODE      5000\n",
            "COUNTRY_CODE                0\n",
            "COUNTRY_NAME                0\n",
            "LOCATION_DESC               0\n",
            "YEAR                        0\n",
            "FREQ_DESC                   0\n",
            "BEGIN_CODE                  0\n",
            "END_CODE                    0\n",
            "REFERENCE_PERIOD_DESC       0\n",
            "WEEK_ENDING              5000\n",
            "LOAD_TIME                   0\n",
            "VALUE                       0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_v6ivQks9Xhy"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Mf8v0gE19Xk_"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nDuplicate Rows:\")  # Output statement\n",
        "print(dataset.duplicated().sum())  # Count the number of duplicate rows"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uAweTGqB9XoH",
        "outputId": "1b9dd357-c518-4554-d01d-b2828bc18fd0"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Duplicate Rows:\n",
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "pQq-2fU99e34"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7virC5mW9f13"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "t6Ad9dWw9f5A"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "08mgZnVN9f-P"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "DPq0-vVR9inw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📊 Step 4: Data Visualization\n",
        "Visualizations help to identify patterns and relationships within the data.\n",
        "\n",
        "## 📈 Types of Visualizations:\n",
        "- **Histogram**: Displays the distribution of the target variable (`VALUE`).\n",
        "- **Correlation Matrix**: Visualizes correlations between features.\n",
        "- **Scatter Plot**: Compares `YEAR` against `VALUE` to observe trends."
      ],
      "metadata": {
        "id": "ifc4j5bkCc07"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing the dataset\n"
      ],
      "metadata": {
        "id": "5dQLVN1w9hE_"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Histogram of 'VALUE'"
      ],
      "metadata": {
        "id": "UgPtoDK8_ym5"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set figure size\n",
        "sns.histplot(dataset['VALUE'], bins=30, kde=True)  # Histogram with a kernel density estimate\n",
        "plt.title('Histogram of VALUE')  # Title of the histogram\n",
        "plt.xlabel('VALUE')  # X-axis label\n",
        "plt.ylabel('Frequency')  # Y-axis label\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        },
        "id": "WsQr-Nh7_zls",
        "outputId": "bc56b8fe-acb5-46cf-fac6-1e05a894764f"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "R0a5jBIX_2Ua"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IeUqPWDH_2Yd"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation matrix"
      ],
      "metadata": {
        "id": "UFnW9S57_2iq"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))  # Set figure size\n",
        "correlation_matrix = dataset[['VALUE']].corr()  # Calculate the correlation matrix for VALUE\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\")  # Heatmap for correlation matrix\n",
        "plt.title('Correlation Matrix of VALUE')  # Title of the heatmap\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "4c8GqRqr_62j",
        "outputId": "02e3aceb-dbb3-4d50-d538-e38d3d918e49"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DB30fHp7_9bZ"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jYhOQapI_9gI"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Scatter plot (comparing 'YEAR' with 'VALUE')"
      ],
      "metadata": {
        "id": "bj-pOhu8_-LS"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set figure size\n",
        "sns.scatterplot(data=dataset, x='YEAR', y='VALUE')  # Scatter plot of 'VALUE' against 'YEAR'\n",
        "plt.title('Scatter Plot of VALUE vs YEAR')  # Title of the scatter plot\n",
        "plt.xlabel('YEAR')  # X-axis label\n",
        "plt.ylabel('VALUE')  # Y-axis label\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "id": "6PTq3BYA9d_P",
        "outputId": "34fda53d-557a-43b6-990a-c3c7c404da5f"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "5EliCxWh9lCo"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UwkPhdvv9mBw"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ITKisWyP9mFA"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dW9_hVhU9mIC"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "GO5MILEN9mif"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ⚙️ Step 5: Data Preprocessing\n",
        "Data preprocessing ensures that the dataset is ready for modeling.\n",
        "\n",
        "## 🔄 Key Actions:\n",
        "- **Handling Missing Values**: Drop columns with excessive missing data and fill remaining gaps.\n",
        "- **Feature Selection**: Remove unnecessary features and adjust data types.\n",
        "- **Encoding**: Apply one-hot encoding for categorical features.\n",
        "- **Data Splitting**: Divide the dataset into training and testing sets (70%/30%)."
      ],
      "metadata": {
        "id": "yEwoU8cLCgY0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Handling missing values (drop columns with a high percentage of missing values)"
      ],
      "metadata": {
        "id": "7jcUfRUp9nWw"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "threshold = 0.6  # Set threshold to drop columns with more than 60% missing values"
      ],
      "metadata": {
        "id": "Nno3w9Pz9Xxp"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset.dropna(thresh=int((1 - threshold) * dataset.shape[0]), axis=1)  # Drop columns with missing values\n"
      ],
      "metadata": {
        "id": "An6xmB8W9X7_"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tGerUmVj8CF4"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fill remaining missing values with mean for numerical columns and mode for categorical columns"
      ],
      "metadata": {
        "id": "LNu_mrvo8CI1"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for column in dataset.columns:  # Loop through each column in the dataset\n",
        "    if dataset[column].dtype == 'object':  # Check if the column is categorical\n",
        "        dataset[column].fillna(dataset[column].mode()[0], inplace=True)  # Fill missing values with mode\n",
        "    else:  # If the column is numerical\n",
        "        dataset[column].fillna(dataset[column].mean(), inplace=True)  # Fill missing values with mean"
      ],
      "metadata": {
        "id": "6r8NehcM8CLm"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nyMe8MVu904o"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop unnecessary columns that do not contribute to prediction (like identifiers)"
      ],
      "metadata": {
        "id": "y2AxUC6I908K"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset.drop(columns=['ASD_CODE', 'ASD_DESC', 'COUNTY_ANSI', 'COUNTY_CODE',\n",
        "                                'COUNTY_NAME', 'REGION_DESC', 'WATERSHED_DESC',\n",
        "                                'CONGR_DISTRICT_CODE', 'WEEK_ENDING'], errors='ignore')  # Drop specified columns\n"
      ],
      "metadata": {
        "id": "l67O-YcM90_X"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EcoHxG5Q91Cj"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert columns to appropriate data types"
      ],
      "metadata": {
        "id": "jxwYSx3p91F7"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['YEAR'] = dataset['YEAR'].astype(int)  # Convert 'YEAR' column to integer type"
      ],
      "metadata": {
        "id": "Ky_DAIhP91JH"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['VALUE'] = pd.to_numeric(dataset['VALUE'], errors='coerce').fillna(0)  # Convert 'VALUE' to numeric\n"
      ],
      "metadata": {
        "id": "jlLSof3A91ND"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0jxguDre8CQv"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature selection (drop redundant columns based on initial analysis)"
      ],
      "metadata": {
        "id": "sXpMnR5l8CTn"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "features_to_drop = ['SOURCE_DESC', 'SECTOR_DESC', 'GROUP_DESC', 'CLASS_DESC',\n",
        "                    'PRODN_PRACTICE_DESC', 'UTIL_PRACTICE_DESC', 'STATISTICCAT_DESC',\n",
        "                    'UNIT_DESC', 'SHORT_DESC', 'DOMAIN_DESC', 'DOMAINCAT_DESC',\n",
        "                    'AGG_LEVEL_DESC', 'STATE_ANSI', 'STATE_FIPS_CODE', 'STATE_ALPHA',\n",
        "                    'STATE_NAME', 'COUNTRY_NAME', 'LOCATION_DESC', 'FREQ_DESC',\n",
        "                    'BEGIN_CODE', 'END_CODE', 'REFERENCE_PERIOD_DESC', 'LOAD_TIME']  # List of redundant features\n"
      ],
      "metadata": {
        "id": "Ujf8bN8p8CXu"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dT6fdXqB8Cd_"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset.drop(columns=features_to_drop, errors='ignore')  # Drop specified features"
      ],
      "metadata": {
        "id": "hCw8k2el0daI"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "ZUvDqWNB-MSg"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1fdBJ561-NIl"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4GpEGUP9-NQ7"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "85wy35DN-NXn"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "tlwiWGxv-NwY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Splitting the data into features and target"
      ],
      "metadata": {
        "id": "UcBFdJtP-Okk"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = dataset.drop(columns=['VALUE'])  # Features dataset (X) without the target variable"
      ],
      "metadata": {
        "id": "mETraKvq-Q7E"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = dataset['VALUE']  # Target variable (y)"
      ],
      "metadata": {
        "id": "S4Kv_W2B-Lao"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vVOkNt1f-LkQ"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Encoding categorical variables"
      ],
      "metadata": {
        "id": "Rr7GVhDD-Lp_"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = pd.get_dummies(X, drop_first=True)  # One-hot encoding for categorical variables"
      ],
      "metadata": {
        "id": "FtCQpi52-LtX"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7UstYoWr0TIr"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the dataset into training and testing sets"
      ],
      "metadata": {
        "id": "JSZav3eq0WmE"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)  # Split data into training and testing sets\n"
      ],
      "metadata": {
        "id": "jziMdf9u0KMn"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NVaw-c34-fdg"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Standardize the features"
      ],
      "metadata": {
        "id": "y1zRzbAM-fhI"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()  # Create a scaler object"
      ],
      "metadata": {
        "id": "GGzu1mQ4-flA"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = scaler.fit_transform(X_train)  # Fit and transform the training data"
      ],
      "metadata": {
        "id": "WZ25eYeq-foZ"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = scaler.transform(X_test)  # Transform the testing data"
      ],
      "metadata": {
        "id": "iQfCzQh_zlOm"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "8VlwqlHo-pEQ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "I5LS7ipS-p4x"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KO5cFvfB-p9B"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "p7pNtGAg-qHp"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "NLi_IKWG-qYh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ⚙️ Step 6: Model Development\n",
        "This step involves training predictive models on the preprocessed data.\n",
        "\n",
        "## 🔧 Models Used:\n",
        "1. **Random Forest Regressor**:\n",
        "   - Train the model on the training dataset.\n",
        "   - Generate predictions on the testing dataset.\n",
        "\n",
        "2. **Support Vector Regressor (SVR)**:\n",
        "   - Optimize hyperparameters using GridSearchCV.\n",
        "   - Train the SVR model and make predictions."
      ],
      "metadata": {
        "id": "a9h69u3_ClXP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Random Forest Regressor"
      ],
      "metadata": {
        "id": "9GqXD_Ql-rJp"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestRegressor(random_state=42)  # Create a Random Forest Regressor object"
      ],
      "metadata": {
        "id": "snnAvRXN-mdg"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf.fit(X_train, y_train)  # Fit the model on the training data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "JIfGjwuz-mg3",
        "outputId": "d730ac03-91f2-482b-e2ef-276c76c63d68"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(random_state=42)</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(random_state=42)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Xc-O6LzjAsma"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_rf = rf.predict(X_test)  # Make predictions on the testing data"
      ],
      "metadata": {
        "id": "hxOydALa-mkB"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "DSn9K4R1-y6y"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CjJJLdNL-zpZ"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VL5HVify-zup"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lmFpBfpW-z1V"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "lzWnxf3Y-0OS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Support Vector Regressor (with hyperparameter tuning)"
      ],
      "metadata": {
        "id": "6nGnipb0-07p"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "svr = SVR()  # Create a Support Vector Regressor object"
      ],
      "metadata": {
        "id": "JW68yP7q-mnW"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid = {'C': [1, 10, 100], 'gamma': ['scale', 'auto'], 'kernel': ['rbf', 'linear']}  # Hyperparameter grid\n"
      ],
      "metadata": {
        "id": "BUskOt0m-4ou"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grid_svr = GridSearchCV(svr, param_grid, cv=3, scoring='r2')  # Grid search for optimal hyperparameters\n"
      ],
      "metadata": {
        "id": "3Z4WEgEG-4rx"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grid_svr.fit(X_train, y_train)  # Fit the grid search on training data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135
        },
        "id": "-iInpjlG-4xO",
        "outputId": "2b5cfb98-a479-422d-adda-b7837d4ad7e9"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=3, estimator=SVR(),\n",
              "             param_grid={'C': [1, 10, 100], 'gamma': ['scale', 'auto'],\n",
              "                         'kernel': ['rbf', 'linear']},\n",
              "             scoring='r2')"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-2 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-2 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-2 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-2 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3, estimator=SVR(),\n",
              "             param_grid={&#x27;C&#x27;: [1, 10, 100], &#x27;gamma&#x27;: [&#x27;scale&#x27;, &#x27;auto&#x27;],\n",
              "                         &#x27;kernel&#x27;: [&#x27;rbf&#x27;, &#x27;linear&#x27;]},\n",
              "             scoring=&#x27;r2&#x27;)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=3, estimator=SVR(),\n",
              "             param_grid={&#x27;C&#x27;: [1, 10, 100], &#x27;gamma&#x27;: [&#x27;scale&#x27;, &#x27;auto&#x27;],\n",
              "                         &#x27;kernel&#x27;: [&#x27;rbf&#x27;, &#x27;linear&#x27;]},\n",
              "             scoring=&#x27;r2&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: SVR</label><div class=\"sk-toggleable__content fitted\"><pre>SVR(C=100)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SVR<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVR.html\">?<span>Documentation for SVR</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SVR(C=100)</pre></div> </div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_svr = grid_svr.predict(X_test)  # Make predictions on the testing data"
      ],
      "metadata": {
        "id": "yd7Utpe2-43e"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "skhcuX_b_Cm-"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AFMXi1sE_DWO"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "I-QpjA3l_DZf"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "P0_UK5ye_DdO"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "qW2cQzzv_Dz2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save predictions to a CSV file"
      ],
      "metadata": {
        "id": "vtjAz9Bs_E4e"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_df = pd.DataFrame({  # Create a DataFrame for predictions\n",
        "    'Actual': y_test,  # Actual values\n",
        "    'RF_Predicted': y_pred_rf,  # Random Forest predictions\n",
        "    'SVR_Predicted': y_pred_svr  # Support Vector Regressor predictions\n",
        "})"
      ],
      "metadata": {
        "id": "z_b5z0nT-46h"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "l8hYURgJ-4-d"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_df.to_csv(\"/content/predictions.csv\", index=False)  # Save predictions to CSV file"
      ],
      "metadata": {
        "id": "6qtgJ3I8-5DO"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Predictions saved to /content/predictions.csv\")  # Output statement"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cHmCr0QR-5OP",
        "outputId": "67a8e4f2-c1df-40a9-f59d-1c60ca6fd32c"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predictions saved to /content/predictions.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "ZUT9IFLK_Nbe"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "L0GgBdBo_OIu"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "D25mISLE_OMI"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZC3IAv8N_OPy"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "FheeW7PS_OnG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📈 Step 7: Results Evaluation\n",
        "Evaluate the performance of the models using appropriate metrics.\n",
        "\n",
        "## 📊 Metrics:\n",
        "- **Mean Squared Error (MSE)**: Measures the average squared difference between predicted and actual values.\n",
        "- **R² Score**: Indicates the proportion of variance explained by the model.\n",
        "- **Save Predictions**: Store the predictions in a CSV file for further analysis."
      ],
      "metadata": {
        "id": "Cp-PfFSgCpxh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Print evaluation metrics and predicted values"
      ],
      "metadata": {
        "id": "ZYQEw-G8_POO"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n--- Random Forest Results ---\")  # Output statement\n",
        "print(\"Mean Squared Error (MSE):\", mean_squared_error(y_test, y_pred_rf))  # MSE for Random Forest"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r5IARUPB_LO2",
        "outputId": "ddd5aa57-b7ca-4bf6-ada4-da2c5e9ec933"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "--- Random Forest Results ---\n",
            "Mean Squared Error (MSE): 1583.2649886292616\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "m205lUVl_LSP"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jMdVOWJM_LWC"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"R^2 Score:\", r2_score(y_test, y_pred_rf))  # R^2 score for Random Forest\n",
        "print(\"First few Random Forest Predictions:\\n\", y_pred_rf[:5])  # Display first few predictions for Random Forest\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O3ES3y27_Lcs",
        "outputId": "dd70e67d-aa32-4b70-b53d-ac3c03643bc3"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R^2 Score: 0.06389567632747317\n",
            "First few Random Forest Predictions:\n",
            " [  8.185      120.88        41.41216667   5.48454762  38.71      ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6e5w8k-H_LgS"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ol0QakVG_Ljq"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n--- Support Vector Regression Results ---\")  # Output statement\n",
        "print(\"Mean Squared Error (MSE):\", mean_squared_error(y_test, y_pred_svr))  # MSE for SVR"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XCJN714x-mqe",
        "outputId": "15a632bb-ad54-4f28-a135-1e4c961671c1"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "--- Support Vector Regression Results ---\n",
            "Mean Squared Error (MSE): 1749.007769113054\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kBRVf_l4-muZ"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "u4683ime-m13"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"R^2 Score:\", r2_score(y_test, y_pred_svr))  # R^2 score for SVR\n",
        "print(\"First few Support Vector Regressor Predictions:\\n\", y_pred_svr[:5])  # Display first few predictions for SVR\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7IJHRauvziIX",
        "outputId": "0cb6ffa7-6878-4a79-ef62-da150b186ee8"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R^2 Score: -0.0340996273915275\n",
            "First few Support Vector Regressor Predictions:\n",
            " [15.33333633 21.07249924 34.86631718  9.64119661 27.71904179]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "GZUg6v3YBwkf"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XhGGkOSfAucR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "esdyn3F_Auf5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_A4_i5tZBx03"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "hzmfLx2VBx-5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📊 Step 8: Visualizing Results\n",
        "Visualization of results provides insights into model performance.\n",
        "\n",
        "## 📉 Visuals Created:\n",
        "- **Residual Plots**: Analyze the distribution of residuals to check for patterns.\n",
        "- **Actual vs. Predicted**: Plot actual values against predicted values to assess model accuracy."
      ],
      "metadata": {
        "id": "jGG8vAXVCtMd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Residual Plot for Random Forest"
      ],
      "metadata": {
        "id": "xkMCnrwZAusZ"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set figure size\n",
        "residuals_rf = y_test - y_pred_rf  # Calculate residuals for Random Forest\n",
        "sns.scatterplot(x=y_pred_rf, y=residuals_rf)  # Scatter plot of predicted values vs. residuals\n",
        "plt.axhline(y=0, color='r', linestyle='--')  # Horizontal line at 0 for reference\n",
        "plt.title('Residual Plot for Random Forest')  # Title\n",
        "plt.xlabel('Predicted Values')  # X-axis label\n",
        "plt.ylabel('Residuals')  # Y-axis label\n",
        "plt.show()  # Show the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        },
        "id": "yhv4iMgsA20q",
        "outputId": "77718240-a983-4cb1-ced1-71c89eb75de2"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uE6Nef7_A5ey"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "35yBKZUsA5if"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Residual Plot for Support Vector Regressor"
      ],
      "metadata": {
        "id": "o2DOZexzA5wt"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set figure size\n",
        "residuals_svr = y_test - y_pred_svr  # Calculate residuals for SVR\n",
        "sns.scatterplot(x=y_pred_svr, y=residuals_svr)  # Scatter plot of predicted values vs. residuals\n",
        "plt.axhline(y=0, color='r', linestyle='--')  # Horizontal line at 0 for reference\n",
        "plt.title('Residual Plot for Support Vector Regressor')  # Title\n",
        "plt.xlabel('Predicted Values')  # X-axis label\n",
        "plt.ylabel('Residuals')  # Y-axis label\n",
        "plt.show()  # Show the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "id": "IwQi48ZKA233",
        "outputId": "eace1b96-7b2b-429f-b164-2afe980433fc"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "H_E5oK51A_1G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Nupi535uA_-b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Actual vs. Predicted Plot for Random Forest"
      ],
      "metadata": {
        "id": "RLZznNDSBAJI"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set figure size\n",
        "sns.scatterplot(x=y_test, y=y_pred_rf)  # Scatter plot of actual vs. predicted values\n",
        "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--')  # Diagonal line for perfect predictions\n",
        "plt.title('Actual vs. Predicted for Random Forest')  # Title\n",
        "plt.xlabel('Actual Values')  # X-axis label\n",
        "plt.ylabel('Predicted Values')  # Y-axis label\n",
        "plt.show()  # Show the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "id": "PigXEKV0A266",
        "outputId": "60df1149-757b-402e-e04a-015369ed4ba4"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3SbkriqUBGEL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "k9u9eGOYBGUJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Actual vs. Predicted Plot for Support Vector Regressor"
      ],
      "metadata": {
        "id": "Z5Zhy-5fBGdy"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set figure size\n",
        "sns.scatterplot(x=y_test, y=y_pred_svr)  # Scatter plot of actual vs. predicted values\n",
        "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--')  # Diagonal line for perfect predictions\n",
        "plt.title('Actual vs. Predicted for Support Vector Regressor')  # Title\n",
        "plt.xlabel('Actual Values')  # X-axis label\n",
        "plt.ylabel('Predicted Values')  # Y-axis label\n",
        "plt.show()  # Show the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "id": "csL6Ca-DA29y",
        "outputId": "3a39242b-e43b-4a52-f5c8-fc7f61e69806"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uawb5S8kBMIc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Sj4SBPKSBMMb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature Importance Plot for Random Forest"
      ],
      "metadata": {
        "id": "YM3J6TCkBMPt"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "importances = rf.feature_importances_  # Get feature importances from the Random Forest model"
      ],
      "metadata": {
        "id": "6KUPWi2GBQRj"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "indices = np.argsort(importances)[::-1]  # Sort feature importances in descending order"
      ],
      "metadata": {
        "id": "7sPvLxTXBYkT"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hNGKt1zGBgdS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the feature importances"
      ],
      "metadata": {
        "id": "DkLuDEAMBg0p"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 8))  # Set figure size\n",
        "sns.barplot(x=importances[indices], y=X.columns[indices])  # Bar plot for feature importances\n",
        "plt.title('Feature Importances for Random Forest')  # Title\n",
        "plt.xlabel('Importance Score')  # X-axis label\n",
        "plt.ylabel('Features')  # Y-axis label\n",
        "plt.show()  # Show the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "O1tIm_buBbSK",
        "outputId": "dbac21ab-ce31-4e24-e30a-1aed104431bc"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ivD5zx1HBjtl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KxF1LrBzBj8D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Prediction Distribution Plot"
      ],
      "metadata": {
        "id": "k8qDXiWmBkcv"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set figure size\n",
        "sns.kdeplot(y_pred_rf, label='Random Forest', color='blue')  # KDE for Random Forest predictions\n",
        "sns.kdeplot(y_pred_svr, label='SVR', color='orange')  # KDE for SVR predictions\n",
        "plt.title('Prediction Distribution of Models')  # Title\n",
        "plt.xlabel('Predicted Values')  # X-axis label\n",
        "plt.ylabel('Density')  # Y-axis label\n",
        "plt.legend()  # Show legend\n",
        "plt.show()  # Show the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "6in6MMUKBYny",
        "outputId": "9d0a6b9e-52c3-4531-c355-57ae5d9dc1f6"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "DFx8ZkKU_d3x"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pdEktU0s_e8i"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "a5oCFKsk_fAT"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "T3yOJ2ZG_fHp"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "lU9jN8lS_fd6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🏁 Step 9: Conclusion\n",
        "Summarize the outcomes and insights gained from the project.\n",
        "\n",
        "## 📜 Summary:\n",
        "- Successful implementation of Random Forest and SVR for predictive modeling.\n",
        "- Key findings and potential areas for improvement identified."
      ],
      "metadata": {
        "id": "gGZubJe1CzsE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "oPPbqfxr_hLq"
      }
    }
  ]
}