TechWithAkash/algerian_forest_fire_prediction_app

🔥 Algerian Forest Fire Prediction App A clean, interactive, and production-ready Machine Learning web app that predicts Fire Weather Index (FWI) using real Algerian forest climate data. 🟢 View Live App --- ## 🎥 Demo Video github.com > 📌 *Click the video link above to watch the full demo!* --- ## 🚀 About the Project This project leverages a trained **Ridge Regression ML model** to predict the **Fire Weather Index (FWI)** based on various weather and climate features. The app is built with Flask for the backend and a clean, responsive Tailwind CSS-based frontend. It’s designed for: - 🔥 Early wildfire detection systems - 🛰️ Environmental research and risk management --- ## 🌐 Live Deployed App **🖥️ Click here to use the app →** > ⚠️ *Note: Free Render plan may take 30–60 seconds to wake up from sleep.* --- ## 💡 How It Works 1. 🌡️ User enters weather values (Temperature, RH, Rain, etc.) 2. 📦 Inputs are scaled and fed to a trained ML model 3. 🧠 Ridge Regression predicts the Fire Weather Index 4. 💬 Prediction appears instantly in a styled popup modal --- ## 🧪 Features - ✅ Ridge Regression Model (trained on real Algerian data) - 🧠 Scikit-learn preprocessor + model pickle files - 🌐 Live Flask app deployed on Render - 💻 Beautiful responsive UI using Tailwind CSS - 📱 Mobile-friendly form with modern input UX - 🔮 Prediction popup instead of redirecting to new page - 📂 Clean folder structure for easy collaboration --- ## 📂 Project Structure ```bash ├── app.py # Flask backend ├── models/ │ ├── ridge.pkl # Trained ML model │ └── scaler.pkl # StandardScaler ├── templates/ │ ├── index.html # Landing page │ ├── predict.html # Form page │ ├── footer.html # Reusable footer │ └── prediction.html # For modal result injection ├── static/ # Assets (optional) ├── requirements.txt # Depende …