BUNNYBOBBALI/ALGERIAN-FOREST--REGRESSION-MODEL

The goal of this project is to develop a regression model that can accurately estimate the FWI, w0hich plays a crucial role in forest fire risk assessment, prevention, and resource allocation.The dataset consists of records collected from two distinct forest regions in Algeria: Bejaia (in the northeast) and Sidi Bel-abbes (in the northwest). # ALGERIAN-FOREST--REGRESSION-MODEL 🌲 Algerian Forest Regression Model This project builds a machine learning regression model to predict the Fire Weather Index (FWI) in Algerian forests using meteorological data. The FWI is an important indicator used in forest fire risk assessment and management. 📂 Dataset Name: Algerian Forest Fires Dataset (UCI Repository) Regions Covered: Bejaia (Northeast Algeria) Sidi Bel-abbes (Northwest Algeria) Attributes: Temperature (°C) Relative Humidity (%) Wind Speed (km/h) Rain (mm) Fire Weather Index (FWI) 🎯 Objective The goal is to build a regression model that predicts the Fire Weather Index (FWI) based on weather conditions. This helps in: Early fire detection and prevention Supporting forest fire management and resource allocation Assisting environmental monitoring authorities ⚙️ Methodology Data Preprocessing Handling missing values Encoding categorical features Feature scaling (Standardization / Normalization) Exploratory Data Analysis (EDA) Visualizing correlations between weather conditions and FWI Comparing regions (Bejaia vs Sidi Bel-abbes) Modeling Linear Regression Decision Tree Regression Random Forest Regression Gradient Boosting Regressor Evaluation Metrics Mean Absolute Error (MAE) Mean Squared Error (MSE) Root Mean Squared Error (RMSE) R² Score 📊 Results The model provides continuous predictions of FWI. Random Forest and Gradient Boosting showed the best performance in capturing complex weather-fire relationships. 🚀 Applications 🔥 Fire Prevention: Assists in predicting risky conditions before fires occur. 🌍 Environmental Protection: Supports sustainable forest management. 📡 Early Warning Systems: Can be integrated into automated fire detection platforms. 🛠️ Tech Stack Python 🐍 Pandas, NumPy, Matplotlib, Seaborn (for data analysis & visualization) Scikit-learn (for ML models) …