ManunjayBhardwaj/Algerian_Dataset_Linear_regression_Project

# 🔥 Algerian Forest Fire FWI Prediction App This project predicts the **Fire Weather Index (FWI)** using meteorological and fire-related data from the **Algerian Forest Fires Dataset**. The app is built using **Streamlit** and deployed on **Streamlit Cloud**. --- ## 🚀 Live Demo 👉 algeriandatasetlinearregres… > Replace this link with your actual Streamlit Cloud deployment link after deploying the app. --- ## 📊 Project Overview Wildfires can have a devastating impact on the environment and human life. This app uses machine learning to predict the **Fire Weather Index (FWI)**, which indicates the potential risk of a forest fire. The prediction is based on various weather and environmental features from real-world Algerian forest fire data. --- ## 🧠 Machine Learning Pipeline - **Dataset**: Algerian Forest Fires Dataset (UCI ML Repository) - **Features Used**: - Temperature - Relative Humidity (RH) - Wind Speed (Ws) - Rain - FFMC, DMC, ISI - Class (Fire / Not Fire) - **Target Variable**: Fire Weather Index (FWI) - **Final Model**: `ElasticNetCV` with cross-validation - **Preprocessing**: StandardScaler --- ## 🛠 Technologies Used - Python - Streamlit - Pandas, NumPy - Seaborn, Matplotlib - Scikit-learn - Pickle (for model serialization) --- ## 📁 Project Structure