aryann13/Algerian-Forest-Fire-Predictor

End-to-End Machine Learning Web App. # 🌲 Algerian Forest Fire Predictor A Flask-based Machine Learning Web Application that predicts the **Fire Weather Index (FWI)** based on weather conditions. This project implements a **Ridge Regression** model to accurately estimate fire risk, helping in early warning systems for forest preservation. ## 🚀 Features - **Accurate Predictions:** Uses a trained Ridge Regression model for precise FWI estimation. - **Interactive UI:** A user-friendly, lavender-themed web interface for easy data input. - **Smart Feedback:** Visual cues (Safe 🍃 / Danger 🔥) based on the predicted risk level. - **Full-Stack Implementation:** Built with Python, Flask, HTML, and CSS. ## 🛠️ Tech Stack - **Frontend:** HTML, CSS (Lavender Theme), Jinja2 Templating - **Backend:** Flask (Python) - **Machine Learning:** Scikit-Learn (Ridge Regression), Pandas, NumPy - **Deployment:** Ready for deployment (Docker/Render/AWS) ## 📂 Project Structure ```text ALGERIAN_FOREST_FIRE_PROJECT/ ├── dataset/ # Raw and cleaned datasets ├── models/ # Serialized models (.pkl files) ├── notebooks/ # Jupyter notebooks for EDA and Model Training ├── templates/ # HTML files (home.html) ├── app.py # Main Flask application ├── requirements.txt # Project dependencies └── README.md # Project documentation ## ⚙️ Installation & Usage ### 1. Clone the Repository ```bash git clone github.com cd Algerian-Forest-Fire-Predictor