860ehtashamul/Algerian_Forest_Fires_Prediction

Algerian Forest Fires Prediction (AI/ML Project) 📌 Overview This project focuses on predicting forest fires in Algeria using machine learning techniques. The model is trained on historical environmental and weather data to determine the likelihood of fire occurrences. The goal is to help in early detection and prevention of forest fires using data-driven insights. 📂 Dataset Name: Algerian Forest Fires Dataset Description: Contains weather and environmental parameters such as temperature, humidity, wind speed, and fire indices. Regions Covered: Bejaia & Sidi Bel-Abbes Target Variable: Fire occurrence (Yes/No or Classes) ⚙️ Features Used Temperature Relative Humidity (RH) Wind Speed (Ws) Rain FFMC (Fine Fuel Moisture Code) DMC (Duff Moisture Code) DC (Drought Code) ISI (Initial Spread Index) BUI (Build Up Index) FWI (Fire Weather Index) 🧠 Machine Learning Models Some of the models used in this project: Logistic Regression Decision Tree Random Forest Support Vector Machine (SVM) K-Nearest Neighbors (KNN) 🔧 Tech Stack Programming Language: Python Libraries Used: NumPy Pandas Matplotlib / Seaborn Scikit-learn 📊 Project Workflow Data Collection Data Cleaning & Preprocessing Exploratory Data Analysis (EDA) Feature Selection Model Training Model Evaluation Prediction 📈 Results Achieved good accuracy in predicting fire occurrence Random Forest / Decision Tree performed best (depending on your implementation)