sanjeev-kumar-patel/Forest-Fire

A machine learning web application that predicts the Fire Weather Index (FWI) for Algerian forest regions. Built using Ridge Regression and deployed on Microsoft Azure, it helps assess the risk of forest fires based on real-time environmental inputs. # 🔥 Forest Fire Weather Index (FWI) Predictor --- ## 🌐 Live Demo > 🔗 **forestfire-hhdtd2d4f3dbdnf5… --- ## ✨ Features - 🔮 **ML-Powered Prediction** — Ridge Regression model trained on the Algerian Forest Fires dataset - 🌡️ **9-Parameter Input** — Temperature, Humidity, Wind Speed, Rain, FFMC, DMC, ISI, Class, Region - 📊 **Risk Classification** — Color-coded risk level (🟢 Low / 🟡 Moderate / 🟠 High / 🔴 Extreme) - 🎨 **Premium Dark UI** — Glassmorphism card, animated ember particles, fire gradient theme - ☁️ **Azure Deployed** — Hosted on Microsoft Azure App Service with Gunicorn WSGI server - 📱 **Responsive Design** — Works on desktop and mobile --- ## 🛠️ Tech Stack | Layer | Technology | |---|---| | **Backend** | Python, Flask | | **ML Model** | Ridge Regression (scikit-learn) | | **Preprocessing** | StandardScaler (scikit-learn) | | **Frontend** | HTML5, Vanilla CSS, Jinja2 | | **Font** | Google Fonts — Outfit | | **WSGI Server** | Gunicorn | | **Cloud Platform** | Microsoft Azure App Service | --- ## 📁 Project Structure ``` Forest Fire/ │ ├── application.py # Flask app — routes & prediction logic ├── requirements.txt # Python dependencies ├── README.md # Project documentation │ ├── templates/ │ ├── home.html # Prediction form UI (main page) │ └── index.html # Legacy landing page │ ├── models/ │ ├── ridge.pkl # Trained Ridge Regression model │ └── scaler.pkl # Fitted StandardScaler │ ├── dataset/ │ └── Algerian_forest_fires_cleaned_dataset.csv # Cleaned training dataset │ └── notebooks/ ├── 2.0-EDA And FE Algerian Forest Fires.ipynb # Exploratory Data Analysis & Feature Engineering └── 3.0-Model Training.ipynb # Model …