ShrmaDhruv/RidgeCV-ALgerian-forest

This project is a Machine Learning–powered Flask web application that predicts the likelihood of forest fires based on environmental conditions such as temperature, humidity, wind speed, and more. It uses a trained RidgeCV regression model and a StandardScaler for preprocessing, with a clean web interface for user interaction. # Forest Fire Prediction 🔥🌲 ## 📌 Overview This project is a **Machine Learning--powered Flask web application** that predicts the **likelihood of forest fires** based on environmental conditions such as temperature, humidity, wind speed, and more.\ It uses a **trained RidgeCV regression model** and a **StandardScaler** for preprocessing, with a clean **web interface** for user interaction. ------------------------------------------------------------------------ ## 🚀 Features - 🌐 **Web App (Flask)** -- User-friendly interface for input and results.\ - 📊 **ML Model (RidgeCV)** -- Predicts fire risk based on multiple features.\ - ⚡ **Data Preprocessing** -- StandardScaler ensures normalized input.\ - 🎨 **Styled Frontend** -- Clean HTML/CSS interface with **loading spinner**.\ - 📝 **Input Parameters**: - Temperature\ - Relative Humidity (RH)\ - Wind Speed (Ws)\ - Rain\ - Fire Weather Indices (FFMC, DMC, ISI)\ - Classes (0/1)\ - Region ------------------------------------------------------------------------ ## 🛠️ Tech Stack - **Backend**: Python (Flask)\ - **Frontend**: HTML, CSS, JavaScript\ - **Machine Learning**: scikit-learn (RidgeCV, StandardScaler)\ - **Deployment Ready**: Flask app structured for hosting ------------------------------------------------------------------------ ## 📂 Project Structure ├── models/ │ ├── ridgeCV.pkl # Trained ML model │ ├── scaler.pkl # Scaler for preprocessing ├── static/ │ └── style.css # Custom CSS styles ├── templates/ │ ├── index.html # Input form page │ ├── home.html # Prediction result page ├── app.py # Flask application └── README.md # Project documentation