Esrekt/Algerian-forest-fire-prediction

Machine learning project for forest fire risk analysis using structured climate data. # Forest Fire Prediction - Machine Learning This project focuses on predicting forest fire occurrences using structured environmental data from the Algerian Forest Fires dataset. --- ## Project Overview The objective is to analyze environmental features and build a machine learning model to predict fire occurrence or fire risk levels. Dataset: - `Algerian_forest_fires_dataset.csv` Features may include: - Temperature - Relative Humidity - Wind Speed - Rain - Fire Weather Index (FWI) - Other meteorological indicators Target: - Fire occurrence / risk classification --- ## Approach - Data cleaning and preprocessing - Handling missing values - Feature scaling (if required) - Train/Test split - Classification or regression modeling - Performance evaluation --- ## Why This Project? Forest fire prediction is an important environmental and risk management problem. This dataset provides a practical example of applying machine learning techniques to real-world environmental data. --- ## Technologies Used - Python - Pandas - NumPy - Scikit-learn - Matplotlib / Seaborn --- ## Purpose This project demonstrates an end-to-end machine learning workflow for environmental risk prediction, including preprocessing, modeling, and evaluation.