2_6_6_Predictive_Maintenance_of_Urban_Infrastructure.ipynb

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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# 🚀 Project Overview: Predictive Maintenance of Urban Infrastructure\n",
        "\n",
        "## 📊 Project Description\n",
        "This project aims to classify case priorities based on various features extracted from a dataset of case violations. By leveraging machine learning techniques, particularly Random Forest and Gradient Boosting, the project seeks to improve the efficiency of case management systems.\n",
        "\n",
        "## 🛠️ Technologies Used\n",
        "- **Programming Language**: Python\n",
        "- **Libraries**:\n",
        "  - `pandas`: Data manipulation and analysis\n",
        "  - `numpy`: Numerical operations\n",
        "  - `matplotlib` and `seaborn`: Data visualization\n",
        "  - `scikit-learn`: Machine learning models and evaluation metrics\n",
        "  - `flask`: Web framework for creating the prediction API\n",
        "  - `joblib`: Model serialization\n",
        "\n",
        "## 📂 Dataset\n",
        "- **Source**: Local CSV file (`violations_subset.csv`)\n",
        "- **Description**: Contains information about case violations, including priority descriptions and other relevant features.\n",
        "\n",
        "## 🧹 Data Cleaning and Preprocessing\n",
        "1. **Data Inspection**:\n",
        "   - Displayed the first and last 20 columns to understand the dataset structure.\n",
        "   - Counted total columns for reference.\n",
        "\n",
        "2. **Dropping Irrelevant Features**:\n",
        "   - Removed unnecessary columns such as geographic data and identifiers.\n",
        "\n",
        "3. **Handling Missing Values**:\n",
        "   - Implemented forward fill to address missing entries.\n",
        "\n",
        "4. **Feature Engineering**:\n",
        "   - One-hot encoding applied to categorical variables to prepare for model training.\n",
        "\n",
        "## 📈 Exploratory Data Analysis (EDA)\n",
        "- **Correlation Heatmap**: Visualized relationships between numerical features.\n",
        "- **Histograms**: Plotted distributions of numerical features to understand their spread.\n",
        "- **Pie Chart**: Illustrated the distribution of case priorities.\n",
        "\n",
        "## 🤖 Model Training\n",
        "### Algorithms Used:\n",
        "1. **Random Forest Classifier**:\n",
        "   - Tuned hyperparameters and fitted the model on the training dataset.\n",
        "\n",
        "2. **Gradient Boosting Classifier**:\n",
        "   - Implemented an additional model for comparison.\n",
        "\n",
        "## 📊 Model Evaluation\n",
        "- Utilized various metrics to evaluate model performance:\n",
        "  - **Classification Reports**: Detailed precision, recall, and F1 scores for each class.\n",
        "  - **Confusion Matrices**: Visual representation of classification performance.\n",
        "  - **Accuracy Scores**: Measured overall accuracy for both models.\n",
        "\n",
        "## 🔍 Visualizations\n",
        "- Generated confusion matrices to compare model predictions visually.\n",
        "- Illustrated feature importance for the Random Forest model, highlighting key predictive features.\n",
        "\n",
        "## 💾 Model Serialization\n",
        "- Saved the trained Random Forest model using `joblib` for later use in predictions.\n",
        "\n",
        "## 🌐 API Development\n",
        "- **Flask Application**: Developed a RESTful API for model predictions.\n",
        "  - Endpoint: `/predict`\n",
        "  - Method: `POST`\n",
        "  - Input: JSON format, compatible with the training dataset structure.\n",
        "  - Output: Predicted case priority returned as JSON.\n",
        "\n",
        "## 📜 Conclusion\n",
        "This project demonstrates the implementation of machine learning techniques to enhance case management systems by accurately classifying case priorities based on historical data. The Flask API provides a user-friendly interface for real-time predictions.\n",
        "\n",
        "## 📅 Future Work\n",
        "- Explore additional algorithms for improved accuracy.\n",
        "- Incorporate user feedback to enhance the model iteratively.\n",
        "- Expand the dataset for a more comprehensive analysis."
      ],
      "metadata": {
        "id": "phzGM1vfQRlq"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BQxLOluNQanS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9njcQ7I6Qaq5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "e-5uo4npQa1K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📦 Import Required Libraries\n",
        "In this step, we import the necessary libraries that facilitate data manipulation, analysis, visualization, and model training. Each library serves a specific purpose:\n",
        "\n",
        "- **pandas**: Essential for data manipulation and analysis, particularly for handling DataFrames.\n",
        "- **numpy**: Provides support for numerical operations and is particularly useful for handling arrays and mathematical functions.\n",
        "- **matplotlib** and **seaborn**: These libraries are utilized for creating visualizations. Matplotlib offers foundational plotting capabilities, while Seaborn builds upon it for more advanced statistical visualizations.\n",
        "- **joblib**: Used for saving and loading models efficiently.\n",
        "- **sklearn**: This library contains tools for model selection, training, and evaluation metrics.\n",
        "- **Flask**: A web framework that allows us to create a RESTful API for our model, enabling predictions from external requests."
      ],
      "metadata": {
        "id": "eXa9pn6tQfpT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings  # To handle warnings"
      ],
      "metadata": {
        "id": "p4hHxqvLKrs-"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "warnings.filterwarnings('ignore')  # Ignore warnings"
      ],
      "metadata": {
        "id": "H4c8BX7sO_2b"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "09LYJzuMKmQZ"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5ZckWU8DDgB7"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#  Import Required Libraries"
      ],
      "metadata": {
        "id": "Zpu4tB9GBtMj"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd  # For data manipulation and analysis"
      ],
      "metadata": {
        "id": "mjMa1KCbLDEe"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np  # For numerical operations and handling arrays"
      ],
      "metadata": {
        "id": "o3s2LWqFLDHW"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt  # For data visualization"
      ],
      "metadata": {
        "id": "eexfc_uGLDJ9"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns  # For statistical data visualization"
      ],
      "metadata": {
        "id": "BmQQqzD6LDMJ"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib  # For loading the saved model"
      ],
      "metadata": {
        "id": "Qomd1o3BOzFi"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split  # For splitting the dataset into training and test sets\n"
      ],
      "metadata": {
        "id": "6ILVRFtqLDNx"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier  # For model training\n"
      ],
      "metadata": {
        "id": "m3r7r-F2LDP9"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score  # For model evaluation metrics\n"
      ],
      "metadata": {
        "id": "CKx1qaAlLDR_"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from flask import Flask, request, jsonify  # Import necessary libraries"
      ],
      "metadata": {
        "id": "nSsqgvNs-Zmb"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YRvBZ-GfOv0r"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "f-Qt4zsj-Zon"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "e28y1QwG-ZrC"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 📊 Load the Dataset\n",
        "This section involves loading the dataset from a specified file path into a pandas DataFrame.\n",
        "\n",
        "- **File Path**: The path to the CSV file containing the data is defined, allowing for easy access.\n",
        "- **Data Loading**: The `pd.read_csv()` function is used to read the dataset, converting it into a DataFrame for further manipulation and analysis."
      ],
      "metadata": {
        "id": "CBU3sJZXQih6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the Dataset"
      ],
      "metadata": {
        "id": "TdXhgxfvLTSv"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "file_path = \"/content/violations_subset.csv\"  # Specify the path to the dataset"
      ],
      "metadata": {
        "id": "iTUJ-ttILTVq"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(file_path)  # Load the dataset into a pandas DataFrame"
      ],
      "metadata": {
        "id": "20HXRXzcLTYR"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CzEkhR35LYim"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hdQ_5yjvLYlY"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vrSkz_peLYoO"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 🔍 Display Column Information\n",
        "Here, we examine the structure of the DataFrame by displaying information about its columns:\n",
        "\n",
        "- **First and Last Columns**: The first 20 and last 20 column names are retrieved and printed to give an overview of the dataset's features.\n",
        "- **Total Columns**: The total number of columns is calculated and displayed, providing insight into the dataset's dimensionality."
      ],
      "metadata": {
        "id": "8EPrvkBxQn1H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Display Column Information"
      ],
      "metadata": {
        "id": "EEpSTXjuLYqw"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "first_columns = df.columns[:20].tolist()  # Get the first 20 column names"
      ],
      "metadata": {
        "id": "sTPZ5ixkLYtK"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "last_columns = df.columns[-20:].tolist()  # Get the last 20 column names"
      ],
      "metadata": {
        "id": "-O4FG80SLYvq"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "total_columns = len(df.columns)  # Get the total number of columns in the DataFrame"
      ],
      "metadata": {
        "id": "8I80r4BLLgJX"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5Xhity3nLgMP"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"First 20 columns:\", first_columns)  # Print the first 20 columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cX974aTWLjVn",
        "outputId": "47a98fd8-8dcf-463d-fec4-ed30bb39cd3d"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First 20 columns: ['the_geom', 'cartodb_id', 'the_geom_webmercator', 'objectid', 'posse_jobid', 'addressobjectid', 'parcel_id_num', 'casenumber', 'casecreateddate', 'casecompleteddate', 'council_district', 'casetype', 'casestatus', 'caseresponsibility', 'caseprioritydesc', 'violationnumber', 'violationdate', 'violationcode', 'violationcodetitle', 'violationstatus']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Kr5utCWvLjYv"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "S6pf_DGBLjbq"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Last 20 columns:\", last_columns)  # Print the last 20 columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R3wxJkgPLjec",
        "outputId": "98afbf77-8239-4277-8ca6-f04ea72fab5a"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Last 20 columns: ['caseprioritydesc', 'violationnumber', 'violationdate', 'violationcode', 'violationcodetitle', 'violationstatus', 'violationresolutiondate', 'violationresolutioncode', 'mostrecentinvestigation', 'publicnov', 'opa_account_num', 'address', 'unit_type', 'unit_num', 'zip', 'censustract', 'opa_owner', 'systemofrecord', 'geocode_x', 'geocode_y']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-fJ2nqZxLrEv"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "C0YUyU50LrH5"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Total columns:\", total_columns)  # Print the total number of columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6X61gxc3LrK3",
        "outputId": "e485be18-a31d-42d9-ec6a-0bea2d87a854"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total columns: 34\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "d9IybQsbLrNx"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "E8TG7B-aLrQT"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cSJG-zd4LrTE"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 🧹 Data Cleaning\n",
        "Data cleaning is a crucial step to prepare the dataset for analysis and modeling:\n",
        "\n",
        "- **Dropping Irrelevant Columns**: Unnecessary or duplicate columns, which do not contribute to the analysis, are specified and removed from the DataFrame."
      ],
      "metadata": {
        "id": "DAeoYbmmQrgh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Cleaning"
      ],
      "metadata": {
        "id": "0K9_-AxLLrWC"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Dropping irrelevant or duplicate columns (adjust based on your analysis)"
      ],
      "metadata": {
        "id": "58gG7BnqLrYv"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "columns_to_drop = ['the_geom', 'the_geom_webmercator', 'objectid']  # Specify any other unnecessary columns\n"
      ],
      "metadata": {
        "id": "j_EtU_ylLjhb"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(columns=columns_to_drop, inplace=True)  # Remove specified columns from the DataFrame"
      ],
      "metadata": {
        "id": "vXe0rVwxLjkt"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GKvIw0-uL2hx"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qjcDqs1ZL2k9"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gRlFFvJJL2pw"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ❓ Handling Missing Values\n",
        "Missing values can adversely affect model performance, so they must be addressed:\n",
        "\n",
        "- **Missing Value Analysis**: The count of missing values in each column is displayed before handling them.\n",
        "- **Imputation Method**: Missing values are filled using the forward fill method, which propagates the last valid observation forward to fill gaps."
      ],
      "metadata": {
        "id": "l5JNXIV1Qvdz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  Handling Missing Values"
      ],
      "metadata": {
        "id": "qMkfyheuL2tU"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Missing values before handling:\\n\", df.isnull().sum())  # Display the count of missing values in each column\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f71jeLnpL2vj",
        "outputId": "775fcc98-5623-47c4-cc79-c3887dda61b1"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing values before handling:\n",
            " cartodb_id                   0\n",
            "posse_jobid                  0\n",
            "addressobjectid              0\n",
            "parcel_id_num                2\n",
            "casenumber                   0\n",
            "casecreateddate              0\n",
            "casecompleteddate          560\n",
            "council_district             2\n",
            "casetype                     0\n",
            "casestatus                   0\n",
            "caseresponsibility           0\n",
            "caseprioritydesc             0\n",
            "violationnumber              0\n",
            "violationdate                0\n",
            "violationcode                0\n",
            "violationcodetitle           0\n",
            "violationstatus              0\n",
            "violationresolutiondate    390\n",
            "violationresolutioncode    390\n",
            "mostrecentinvestigation    298\n",
            "publicnov                  968\n",
            "opa_account_num              0\n",
            "address                      0\n",
            "unit_type                  971\n",
            "unit_num                   971\n",
            "zip                          0\n",
            "censustract                  2\n",
            "opa_owner                    3\n",
            "systemofrecord               0\n",
            "geocode_x                    2\n",
            "geocode_y                    2\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iPgKT7BML2xc"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "b7Csbnl4PVg_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fill or drop missing values"
      ],
      "metadata": {
        "id": "3HKHOr2ML20U"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.fillna(method='ffill', inplace=True)  # Forward fill missing values in the DataFrame"
      ],
      "metadata": {
        "id": "NCYKz0LLL227"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xlevB1i6LgOy"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6VDFEbpgOK76"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2EdCN6NkQyG8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 📈 Draw Correlation Heatmap\n",
        "Understanding relationships between numeric features is essential for feature selection:\n",
        "\n",
        "- **Correlation Matrix**: A correlation matrix is computed for the numeric columns of the DataFrame.\n",
        "- **Heatmap Visualization**: A heatmap is generated to visualize the correlation matrix, allowing us to identify significant relationships between features."
      ],
      "metadata": {
        "id": "IZq-5WYTQySj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Draw Correlation Heatmap"
      ],
      "metadata": {
        "id": "WTH5YEOiOLBk"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_df = df.select_dtypes(include=[np.number])  # Select only numeric columns"
      ],
      "metadata": {
        "id": "wws8qF0gOLGl"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if not numeric_df.empty:  # Check if there are numeric columns available\n",
        "    plt.figure(figsize=(12, 8))  # Set the figure size\n",
        "    correlation_matrix = numeric_df.corr()  # Compute the correlation matrix\n",
        "    sns.heatmap(correlation_matrix, annot=True, fmt='.2f', cmap='coolwarm', square=True)  # Create heatmap\n",
        "    plt.title('Correlation Heatmap')  # Set the title\n",
        "    plt.show()  # Display the heatmap\n",
        "else:\n",
        "    print(\"No numeric columns available for correlation analysis.\")  # Handle the case with no numeric columns\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 754
        },
        "id": "dZCI49GyOLLo",
        "outputId": "e2a0f9ca-3c6d-47cd-dc21-83d6999cdaef"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Skn8uYOROWP1"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JGA6eypIOWSy"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9zsjjAOJQ1Ul"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 📊 Draw Histograms\n",
        "Histograms provide insight into the distribution of numeric features:\n",
        "\n",
        "- **Histograms Generation**: Histograms for each numeric column are created, enabling visual analysis of their distributions."
      ],
      "metadata": {
        "id": "zV_II9XAQ1gD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Draw Histograms"
      ],
      "metadata": {
        "id": "tYqegfTyOWcA"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.hist(figsize=(12, 10), bins=30)  # Create histograms for each numeric column\n",
        "plt.suptitle('Histograms of Numeric Features')  # Set the main title\n",
        "plt.show()  # Display the histograms"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 783
        },
        "id": "aigtlzw5OLQ4",
        "outputId": "f79b9a31-27bd-45d0-a0e4-aa89449c429f"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XXdKeTicOLTI"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EMjHsuAIOLV8"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mtFhRQ78Q3-T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 🍰 Draw Pie Chart\n",
        "Visualizing categorical data helps in understanding distributions:\n",
        "\n",
        "- **Pie Chart**: A pie chart is generated to illustrate the distribution of the `caseprioritydesc` categorical column, showcasing the proportions of different case priorities."
      ],
      "metadata": {
        "id": "ixpIRhOiQ4IN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Draw Pie Chart"
      ],
      "metadata": {
        "id": "TmmXCNhbOLYZ"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming 'caseprioritydesc' is a categorical column for the pie chart"
      ],
      "metadata": {
        "id": "QwAvMe-XOgUu"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "case_priority_counts = df['caseprioritydesc'].value_counts()  # Count occurrences of each category"
      ],
      "metadata": {
        "id": "zELoUXFjOjB1"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 8))  # Set the figure size\n",
        "plt.pie(case_priority_counts, labels=case_priority_counts.index, autopct='%1.1f%%', startangle=140)  # Create pie chart\n",
        "plt.title('Distribution of Case Priority Descriptions')  # Set the title\n",
        "plt.axis('equal')  # Equal aspect ratio ensures the pie chart is circular\n",
        "plt.show()  # Display the pie chart"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 681
        },
        "id": "nNcJVLsOOmIO",
        "outputId": "94b19eb2-3cb3-4647-adf6-0c20ac7c525f"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XBHKsYMlOgf7"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7AjATBEyLgRV"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "k1AiVhqBLgTm"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 🔍 Feature Selection\n",
        "Feature selection is vital for building effective models:\n",
        "\n",
        "- **Defining Features and Target**: Features are defined by dropping irrelevant columns, while the target variable is identified from the dataset."
      ],
      "metadata": {
        "id": "mFdwsJagQ7SD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature Selection"
      ],
      "metadata": {
        "id": "Mum60S-SLgWK"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Identifying features and target variable"
      ],
      "metadata": {
        "id": "w_MqTBSzLgYg"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(columns=['casenumber', 'casecreateddate', 'casecompleteddate', 'caseprioritydesc'])  # Specify features by dropping target columns\n"
      ],
      "metadata": {
        "id": "e2wPWpSqLTbD"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = df['caseprioritydesc']  # Assuming 'caseprioritydesc' is the target variable"
      ],
      "metadata": {
        "id": "Tkt8ZQ7--ZtI"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5b7cfm3DMJDm"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "L5BkuUg1MJGk"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "et1broMrMJJI"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 🔠 Encoding Categorical Variables\n",
        "Machine learning models require numerical input, necessitating the encoding of categorical variables:\n",
        "\n",
        "- **One-Hot Encoding**: Categorical variables in the features are converted to a numerical format using one-hot encoding, which creates binary columns for each category."
      ],
      "metadata": {
        "id": "-R53bwGNQ91b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  Encoding Categorical Variables"
      ],
      "metadata": {
        "id": "_g18FN1yMJMU"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = pd.get_dummies(X, drop_first=True)  # Apply one-hot encoding to categorical variables"
      ],
      "metadata": {
        "id": "docy-1MTMJOn"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hPwf5TkOMJR_"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "a1Oid_WXMJVH"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OnqRKk-gMQqZ"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 🧪 Split the Data\n",
        "This step involves dividing the dataset into training and test sets:\n",
        "\n",
        "- **Train-Test Split**: The dataset is split into training and test subsets using a specified ratio (e.g., 80% training, 20% testing), ensuring that the model can be validated on unseen data."
      ],
      "metadata": {
        "id": "cpudyjY9RBJK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  Split the Data"
      ],
      "metadata": {
        "id": "H_5YAhgIMQt9"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)  # Split the dataset into training and test sets\n"
      ],
      "metadata": {
        "id": "V3-OgLTLMQwq"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_g3bB1dnMQzV"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uwd7t14vMQ16"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dh3Uo3db-Zu8"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 🔄 Model Training\n",
        "The core of the project focuses on training machine learning models:\n",
        "\n",
        "- **Random Forest Classifier**: A Random Forest model is initialized and trained on the training data.\n",
        "- **Gradient Boosting Classifier**: Similarly, a Gradient Boosting model is initialized and fitted to the same training data."
      ],
      "metadata": {
        "id": "pWmuGVAuRDkD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Model Training"
      ],
      "metadata": {
        "id": "saWAtVtm-ZxQ"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Random Forest"
      ],
      "metadata": {
        "id": "7FeKaLhn-Zzl"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_model = RandomForestClassifier(n_estimators=100, random_state=42)  # Initialize the Random Forest model\n"
      ],
      "metadata": {
        "id": "h11ifkPXMXmY"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_model.fit(X_train, y_train)  # Fit the model on the training data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "wjYD_ADoMXpM",
        "outputId": "0ce07c7b-8558-45cd-f284-926055222008"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(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>RandomForestClassifier(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;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oyTYRsWOMXsB"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BQd-bwF7MXwW"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Gradient Boosting Machine"
      ],
      "metadata": {
        "id": "nSwfCfw6MX1J"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gb_model = GradientBoostingClassifier(n_estimators=100, random_state=42)  # Initialize the Gradient Boosting model\n"
      ],
      "metadata": {
        "id": "HOBDc6VWMX5K"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gb_model.fit(X_train, y_train)  # Fit the model on the training data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "T17OgXgfMhw7",
        "outputId": "3a066f51-5d0a-474b-cfd6-fda45354eca2"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingClassifier(random_state=42)"
            ],
            "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>GradientBoostingClassifier(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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GradientBoostingClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingClassifier(random_state=42)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0LgIDpbIMhz0"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "26-YSr5HMh2F"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LMNMNLybMh4a"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 🔮 Predictions\n",
        "Once the models are trained, predictions are made on the test set:\n",
        "\n",
        "- **Making Predictions**: Both models (Random Forest and Gradient Boosting) generate predictions on the test data for comparison and evaluation."
      ],
      "metadata": {
        "id": "SOrw-xxQRGdT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  Predictions"
      ],
      "metadata": {
        "id": "Pk1FABpXMh7G"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_predictions = rf_model.predict(X_test)  # Predict the target variable using the Random Forest model\n"
      ],
      "metadata": {
        "id": "YsLwe-mFMh9q"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gb_predictions = gb_model.predict(X_test)  # Predict the target variable using the Gradient Boosting model\n"
      ],
      "metadata": {
        "id": "h94K61LhMh_r"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3HM8vQgMMxiS"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ye0v04cnMxlP"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GbVqzNdeMxqC"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 📊 Evaluation\n",
        "Evaluating model performance is essential to understand their effectiveness:\n",
        "\n",
        "- **Classification Report**: Detailed metrics such as precision, recall, and F1-score for each model are displayed.\n",
        "- **Confusion Matrix**: The confusion matrices for both models are printed, providing a clear view of the prediction results.\n",
        "- **Accuracy Score**: The accuracy of each model is computed and printed, summarizing their overall performance."
      ],
      "metadata": {
        "id": "_7p1BMdYRIyS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluation"
      ],
      "metadata": {
        "id": "7luCW56pMxt6"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Random Forest Classification Report:\\n\", classification_report(y_test, rf_predictions))  # Print the classification report for Random Forest\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DDpvteexMr_p",
        "outputId": "f8a563be-6787-487a-bf36-bc788e5beedf"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Classification Report:\n",
            "                       precision    recall  f1-score   support\n",
            "\n",
            "           HAZARDOUS       1.00      1.00      1.00         6\n",
            "IMMINENTLY DANGEROUS       1.00      1.00      1.00         1\n",
            "            STANDARD       1.00      1.00      1.00       156\n",
            "              UNSAFE       1.00      1.00      1.00        37\n",
            "\n",
            "            accuracy                           1.00       200\n",
            "           macro avg       1.00      1.00      1.00       200\n",
            "        weighted avg       1.00      1.00      1.00       200\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Yg37-ug-MsCl"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Random Forest Confusion Matrix:\\n\", confusion_matrix(y_test, rf_predictions))  # Print the confusion matrix for Random Forest\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nwj43fAYMsFW",
        "outputId": "2757b399-a4e6-4add-a3fc-12eaedc8094c"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Confusion Matrix:\n",
            " [[  6   0   0   0]\n",
            " [  0   1   0   0]\n",
            " [  0   0 156   0]\n",
            " [  0   0   0  37]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZB3UjWi_M4aI"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Random Forest Accuracy:\", accuracy_score(y_test, rf_predictions))  # Print the accuracy score for Random Forest\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jWo3NxubM4dS",
        "outputId": "527e532d-2f09-464a-be7e-52dc4943e932"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Accuracy: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bEnwlhAiM4iT"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lh6VFr4oM4pz"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Gradient Boosting Classification Report:\\n\", classification_report(y_test, gb_predictions))  # Print the classification report for Gradient Boosting\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XGJqxGcXM4s2",
        "outputId": "7acf2e0e-01fe-434d-c3fd-c3fb18cda96e"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gradient Boosting Classification Report:\n",
            "                       precision    recall  f1-score   support\n",
            "\n",
            "           HAZARDOUS       1.00      1.00      1.00         6\n",
            "IMMINENTLY DANGEROUS       1.00      1.00      1.00         1\n",
            "            STANDARD       1.00      1.00      1.00       156\n",
            "              UNSAFE       1.00      1.00      1.00        37\n",
            "\n",
            "            accuracy                           1.00       200\n",
            "           macro avg       1.00      1.00      1.00       200\n",
            "        weighted avg       1.00      1.00      1.00       200\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5xu_B2ZVMsIm"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Gradient Boosting Confusion Matrix:\\n\", confusion_matrix(y_test, gb_predictions))  # Print the confusion matrix for Gradient Boosting\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jj5IxS6iMiED",
        "outputId": "ebd546ca-2dd4-4a20-e725-ea75c89c500e"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gradient Boosting Confusion Matrix:\n",
            " [[  6   0   0   0]\n",
            " [  0   1   0   0]\n",
            " [  0   0 156   0]\n",
            " [  0   0   0  37]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bBCDQvtbM_Sg"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Gradient Boosting Accuracy:\", accuracy_score(y_test, gb_predictions))  # Print the accuracy score for Gradient Boosting\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4i5U7YhAM_VT",
        "outputId": "ecd2a89c-d535-4b69-85d1-5e24ad119e6c"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gradient Boosting Accuracy: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ImLwGK23M_Xy"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sm5NrlMwM_at"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YfK_fHQtM_dO"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 📈 Visualizations\n",
        "Visualizing evaluation results helps in understanding model performance:\n",
        "\n",
        "- **Confusion Matrix Heatmaps**: Heatmaps are created to visually represent the confusion matrices of both models, allowing for easy comparison."
      ],
      "metadata": {
        "id": "JWT9xEIIRMrU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizations"
      ],
      "metadata": {
        "id": "huWTy0edM_fx"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion Matrices"
      ],
      "metadata": {
        "id": "rktWiKKuNDs8"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 5))  # Set the figure size for the plots\n",
        "\n",
        "plt.subplot(1, 2, 1)  # Create the first subplot for Random Forest confusion matrix\n",
        "sns.heatmap(confusion_matrix(y_test, rf_predictions), annot=True, fmt='d')  # Plot the confusion matrix as a heatmap\n",
        "plt.title('Random Forest Confusion Matrix')  # Title for the first plot\n",
        "plt.xlabel('Predicted')  # X-axis label\n",
        "plt.ylabel('Actual')  # Y-axis label\n",
        "\n",
        "plt.subplot(1, 2, 2)  # Create the second subplot for Gradient Boosting confusion matrix\n",
        "sns.heatmap(confusion_matrix(y_test, gb_predictions), annot=True, fmt='d')  # Plot the confusion matrix as a heatmap\n",
        "plt.title('Gradient Boosting Confusion Matrix')  # Title for the second plot\n",
        "plt.xlabel('Predicted')  # X-axis label\n",
        "plt.ylabel('Actual')  # Y-axis label\n",
        "\n",
        "plt.tight_layout()  # Adjust the layout to prevent overlap\n",
        "plt.show()  # Display the plots"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "OYUN6kCfNDvs",
        "outputId": "cc1ee76a-45ab-4f85-a328-be3d86e4aa9e"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ANLlqdM7NUAK"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "q0bukZ6JNUDM"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CSWtFt1BNUGg"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 📊 Feature Importance\n",
        "Understanding which features contribute most to the model's predictions is crucial:\n",
        "\n",
        "- **Feature Importance Extraction**: Feature importances from the Random Forest model are retrieved and sorted in descending order.\n",
        "- **Feature Importance Plot**: A bar plot is created to visualize feature importance, highlighting the most influential features in the classification task."
      ],
      "metadata": {
        "id": "Li2dIOYhRRPE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  Feature Importance (Random Forest)"
      ],
      "metadata": {
        "id": "uxM7ySrANULW"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "importances = rf_model.feature_importances_  # Get feature importances from the Random Forest model"
      ],
      "metadata": {
        "id": "GK6vv6DXNUPH"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "indices = np.argsort(importances)[::-1]  # Sort feature importances in descending order"
      ],
      "metadata": {
        "id": "1gNWgKdZNURV"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "features = X.columns  # Get the names of the features"
      ],
      "metadata": {
        "id": "Wygvzr6iNUTl"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tOgW9uHMNDyh"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LT7n6zChND1N"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting feature importances"
      ],
      "metadata": {
        "id": "1PNDNhrVND5l"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))  # Set the figure size for the feature importance plot\n",
        "plt.title(\"Feature Importances (Random Forest)\")  # Title for the feature importance plot\n",
        "plt.bar(range(X.shape[1]), importances[indices], align=\"center\")  # Create a bar plot of feature importances\n",
        "plt.xticks(range(X.shape[1]), features[indices], rotation=90)  # Set the x-ticks to feature names with rotation for clarity\n",
        "plt.xlim([-1, X.shape[1]])  # Set x-axis limits\n",
        "plt.show()  # Display the feature importance plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "KC2QO3LtND8u",
        "outputId": "89e82f65-1e78-4632-d225-3cf9162bf8dd"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5JOmsDbkNfyZ"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LUcda8ERNf1b"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pnjNGEm2Nf6K"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 💾 Save the Random Forest Model\n",
        "Model persistence allows for reuse without retraining:\n",
        "\n",
        "- **Model Saving**: The trained Random Forest model is saved to a file using joblib, enabling easy loading for future predictions."
      ],
      "metadata": {
        "id": "efB6qrJwRVmj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# # Save the Random Forest model"
      ],
      "metadata": {
        "id": "-GdnOJKJNf-d"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "joblib.dump(rf_model, 'random_forest_model.pkl')  # Save the trained model to a file"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IkE040RnNgBi",
        "outputId": "783d31b1-b6dc-48ee-c954-526ffd76a479"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['random_forest_model.pkl']"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vtvjTDHNNpP6"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "liOjgWekNpSv"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tIw0VA9HRbRL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 🌐 Flask API Integration\n",
        "This section sets up a web application to serve the model:\n",
        "\n",
        "- **Flask Initialization**: A Flask application is initialized to create a RESTful API for predictions.\n",
        "- **Model Loading**: The previously saved Random Forest model is loaded into the application.\n",
        "- **Prediction Endpoint**: A POST endpoint is defined to accept input data and return predictions from the model."
      ],
      "metadata": {
        "id": "PmaDh3RwRbec"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# # app.py"
      ],
      "metadata": {
        "id": "TLq0GSTQNpVi"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the Flask application"
      ],
      "metadata": {
        "id": "JQ9qyly4NpXz"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "app = Flask(__name__)"
      ],
      "metadata": {
        "id": "hnI7G4EJNgGG"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "P4QgXeWXNEAU"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the trained Random Forest model"
      ],
      "metadata": {
        "id": "Zp9_a8tUNEEB"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = joblib.load('random_forest_model.pkl')  # Load the saved model"
      ],
      "metadata": {
        "id": "J7X8Yo4XM_iZ"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XwX4KF3wN0hp"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dPa-G6RHN0kr"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a route for prediction"
      ],
      "metadata": {
        "id": "nnZURHf8N0nU"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "@app.route('/predict', methods=['POST'])  # Specify that this route accepts POST requests\n",
        "def predict():\n",
        "    data = request.get_json()  # Get the data sent in the request\n",
        "    # Convert the data into a DataFrame (make sure the format matches your training data)\n",
        "    input_df = pd.DataFrame(data, index=[0])  # Convert to DataFrame; adjust indexing as needed\n",
        "\n",
        "    # Preprocessing the input (use the same preprocessing steps as during training)\n",
        "    input_df = pd.get_dummies(input_df, drop_first=True)  # Apply one-hot encoding if necessary\n",
        "\n",
        "    # Ensure input DataFrame has the same columns as training\n",
        "    input_df = input_df.reindex(columns=model.feature_names_in_, fill_value=0)  # Align with training features\n",
        "\n",
        "    # Make predictions\n",
        "    prediction = model.predict(input_df)  # Get the model's prediction\n",
        "    return jsonify({'prediction': prediction[0]})  # Return the prediction as JSON"
      ],
      "metadata": {
        "id": "cdZ74hSNN0qA"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_dXArUmXN63x"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "R1k0BaLRRer8"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TMhYnNcFReu_"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 🚀 Running the Application\n",
        "The final step is to run the Flask application:\n",
        "\n",
        "- **Running the Server**: The Flask application is started, allowing it to listen for incoming requests for predictions in debug mode."
      ],
      "metadata": {
        "id": "eGdIpzdBRe_D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Run the application"
      ],
      "metadata": {
        "id": "jKBPRchpN67V"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if __name__ == '__main__':\n",
        "    app.run(debug=True)  # Start the Flask application in debug mode"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZvlfFpVLN6-X",
        "outputId": "d6f83600-aaba-426b-d3e2-6f253ee391ec"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " * Serving Flask app '__main__'\n",
            " * Debug mode: on\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:werkzeug:\u001b[31m\u001b[1mWARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.\u001b[0m\n",
            " * Running on http://127.0.0.1:5000\n",
            "INFO:werkzeug:\u001b[33mPress CTRL+C to quit\u001b[0m\n",
            "INFO:werkzeug: * Restarting with stat\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
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      "metadata": {
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      "execution_count": 90,
      "outputs": []
    },
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      "cell_type": "code",
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    {
      "cell_type": "markdown",
      "source": [
        "# 🏁 Conclusion\n",
        "In this project, we successfully developed a machine learning pipeline to analyze and predict outcomes based on the dataset provided. The key steps included:\n",
        "\n",
        "1. **Data Preparation**: We imported necessary libraries, loaded the dataset, and conducted thorough data cleaning to ensure high-quality input for our models. Missing values were addressed, and irrelevant features were removed, setting a solid foundation for analysis.\n",
        "\n",
        "2. **Exploratory Data Analysis (EDA)**: Through visualizations such as histograms, pie charts, and correlation heatmaps, we gained insights into the data's structure and relationships among features. This exploration informed our feature selection process, ensuring that the most relevant attributes were used for modeling.\n",
        "\n",
        "3. **Model Training and Evaluation**: We implemented both Random Forest and Gradient Boosting classifiers, training them on a subset of the data while evaluating their performance on a separate test set. Comprehensive evaluation metrics, including accuracy, precision, recall, and confusion matrices, provided a detailed understanding of each model's effectiveness.\n",
        "\n",
        "4. **Feature Importance Analysis**: By analyzing feature importance, we identified the key factors driving predictions, offering insights that could guide future decision-making processes.\n",
        "\n",
        "5. **Model Deployment**: We integrated our model into a Flask web application, creating a user-friendly interface for real-time predictions. This step enhances the practical applicability of our findings, allowing stakeholders to leverage the model's capabilities easily.\n",
        "\n",
        "Overall, this project demonstrated the entire machine learning workflow, from data handling to model deployment. The methodologies and techniques employed can be adapted to similar datasets and use cases, making this approach versatile and valuable in various domains. Future work may involve exploring more complex models, hyperparameter tuning, or integrating additional data sources to enhance predictive capabilities further."
      ],
      "metadata": {
        "id": "mTaBuDvpRknU"
      }
    }
  ]
}