Rai59/Forest-Fire-Prediction-Algeria

CS-165 Data Science Project # Forest Fire Prediction — Algeria A machine learning project to predict forest fire occurrence using meteorological data and fire weather indices. ## Overview This project develops a binary classification system to predict whether a forest fire will occur based on weather conditions. Three models are compared — Logistic Regression, k-Nearest Neighbours, and Random Forest — with Random Forest achieving the best performance at **95.9% accuracy**. ## Dataset - **Size:** 243 observations from two Algerian regions (Bejaia and Sidi-Bel Abbes) - **Period:** June – September (fire season) - **Features:** Temperature, humidity, wind speed, rainfall, and Fire Weather Index components (FFMC, DMC, DC, ISI, BUI, FWI) ## Results | Model | CV Accuracy | Test Accuracy | |-------|-------------|---------------| | Logistic Regression | 92.8% | 93.9% | | k-Nearest Neighbours | 87.6% | 93.9% | | Random Forest | 97.4% | 95.9% | ## Key Findings - **FWI and Drought Code (DC)** are the strongest predictors — sustained fuel moisture depletion matters more than single-day weather - **August** has the highest fire occurrence; **June** the lowest - Random Forest outperforms linear models by capturing non-linear feature interactions ## Tech Stack - Python 3 - pandas, NumPy - scikit-learn - Matplotlib, Seaborn ## Future Improvements - Hyperparameter tuning (GridSearchCV) - Time-based cross-validation for realistic evaluation - Add region as a categorical feature - Deploy as a real-time risk scoring API ## Author Rai — Computer Science, Swansea University