kartavyaverma/Algerian_Fire_Forest

# Algerian Forest Fire Prediction 🔥 This project is an **end-to-end Machine Learning web application** that predicts the **Fire Weather Index (FWI)** using environmental features such as temperature, humidity, wind speed, and rainfall. The model is deployed using **Flask** and allows users to input values through a web interface to obtain predictions. --- ## 📌 Project Overview The goal of this project is to build a machine learning model that predicts the **Fire Weather Index (FWI)** for Algerian forest regions. The system takes multiple environmental parameters as input and returns a predicted FWI value using a **Ridge Regression model**. --- ## ⚙️ Technologies Used * Python * Flask * Scikit-Learn * NumPy * Pandas * HTML * Jinja2 --- ## 📊 Features Used for Prediction * Temperature * Relative Humidity (RH) * Wind Speed (Ws) * Rain * FFMC * DMC * ISI * Classes * Region --- ## 🧠 Machine Learning Model The trained model used in this project: **Ridge Regression** Steps performed: 1. Data preprocessing 2. Feature scaling using **StandardScaler** 3. Model training 4. Model serialization using **pickle** 5. Deployment with **Flask** --- ## 📂 Project Structure ``` Algerian_Fire-Prediction │ ├── application.py ├── requirements.txt ├── README.md │ ├── models │ ├── ridge.pkl │ └── scaler.pkl │ ├── templates │ ├── index.html │ └── home.html │ └── Algerian.ipynb ``` --- ## 🚀 How to Run the Project ### 1. Clone the repository ``` git clone ``` ### 2. Install dependencies ``` pip install -r requirements.txt ``` ### 3. Run the Flask application ``` python application.py ``` ### 4. Open in browser ``` 127.0.0.1 ``` --- ## 📈 Future Improvements * Improve UI using Bootstrap * Add model evaluation metrics * Deploy on cloud platforms (Render / AWS / Heroku) --- ## 👨‍💻 Author Kartavya Verma