SiniyaMC363/Algeria-forest-fire-prediction-using-ML

This project aims to predict the occurrence of forest fires in Algeria using machine learning techniques. The model is developed using the Algerian Forest Fires dataset, which contains meteorological and environmental data collected from two regions of Algeria: Bejaia and Sidi Bel-Abbes. # 🔥 Algerian Forest Fire Prediction using Machine Learning This project predicts the occurrence of forest fires in Algeria using machine learning techniques. The model is trained on meteorological and environmental features to classify fire and non-fire events. --- ## 📌 Project Overview Forest fires cause severe environmental and economic damage. This project aims to: - Analyze the Algerian Forest Fires dataset - Build a machine learning classification model - Predict forest fire occurrence based on weather conditions --- ## 📊 Dataset - **Source**: Algerian Forest Fires Dataset - **Regions**: Bejaia and Sidi Bel-Abbes - **Features include**: - Temperature - Relative Humidity (RH) - Wind Speed - Rain - Fire Weather Index (FWI) - ISI, DMC, DC, BUI - **Target**: Fire / No Fire --- ## 🛠️ Tools & Technologies - Python - Pandas, NumPy - Matplotlib, Seaborn - Scikit-learn - Flask (for deployment) - Git & GitHub --- ## ⚙️ Project Workflow 1. Data loading and exploration 2. Data cleaning and preprocessing 3. Feature selection 4. Model training and evaluation 5. Model serialization using Pickle 6. (Optional) Web deployment using Flask --- ## 🤖 Machine Learning Models Used - Logistic Regression - Random Forest Classifier - Support Vector Machine (SVM) Evaluation metrics: - Accuracy - Precision - Recall - Confusion Matrix ---