rnrahate/Algerian-Forest-Fire-Prediction-Model

Implementing Multiple Linear Regression and its different Algorithms for regression model of Algerian Forest Fire Prediction. Used different MLA for accuracy measurements and hyperparameter tuning of model. # 🔥 Algerian Forest Fire FWI Predictor > **Advanced Machine Learning for Fire Weather Index Prediction** > Protecting Algerian Forests with AI-Powered Environmental Intelligence 🎈 **🚀 Try Live Demo** --- ## 📋 Table of Contents - Overview - Problem Statement - Dataset - Features & Input Parameters - Model Architecture - Project Structure - Installation & Setup - Usage - Model Performance - FWI Risk Scale - Key Innovations - Technologies & Libraries - Data Pipeline - Results & Insights - Future Enhancements - Developer - License --- ## 🚀 Quick Links | Link | Description | |------|-------------| | 🎈 **Live Demo** | Try the application online (no installation required) | | 🐙 **GitHub Repository** | Source code & documentation | | 📊 **Dataset** | Download cleaned dataset | | 📓 **Notebooks** | Jupyter notebooks for data exploration & training | | 💼 **LinkedIn** | Connect with the developer | --- ## 🎯 Overview **Algerian Forest Fire FWI Predictor** is a machine learning application that predicts the **Fire Weather Index (FWI)** for Algerian forests with exceptional accuracy. This tool leverages advanced regression techniques to forecast fire risk based on meteorological and fire danger indices data. The application combines: - **RidgeCV Regression** for robust predictions - **9 optimized features** (meteorological and fire indices) - **StandardScaler preprocessing** for model consistency - **Interactive Streamlit UI** for real-time predictions - **Automated Backend BUI Computation** for seamless user experience **Key Achievement:** Achieves **98.42% accuracy** (R² = 0.9842) with minimal prediction error (MAE = 0.89, RMSE = 1.23). --- ## 🔍 Problem Statement Forest fires pose a significant threat to ecosystems, communities, and economies, particularly in regions like Algeria with challenging climate conditions. Early and accurate prediction of fire weather conditions is critical for: - **Proactive Fire Prevention**: Enable authorities to implement preventive …