beepbopskeet/Algerian-Forest-Fires-ML

Data analysis of Algerian Forest Fires, exploring meteorological factors and wildfire risk indicators through visualization and statistical techniques. # 🔥 Algerian Forest Fires Analysis ## 📌 Project Overview This project explores the Algerian Forest Fires Dataset to understand how weather conditions and fire weather indices relate to wildfire occurrence. The analysis includes: * Data Cleaning * Missing Value Handling * Data Type Conversion * Exploratory Data Analysis (EDA) * Regional Analysis * Fire Risk Indicator Investigation --- ## 📊 Dataset Information The dataset contains meteorological observations collected from two regions in Algeria. Features include: * Temperature * Relative Humidity (RH) * Wind Speed (Ws) * Rainfall * FFMC (Fine Fuel Moisture Code) * DMC (Duff Moisture Code) * DC (Drought Code) * ISI (Initial Spread Index) * BUI (Build Up Index) * FWI (Fire Weather Index) Target Variable: * Classes (Fire / Not Fire) --- ## 🛠 Technologies Used * Python * Pandas * NumPy * Matplotlib * Seaborn * Jupyter Notebook --- ## 🔍 Data Cleaning Steps The following preprocessing tasks were performed: * Removed invalid records * Handled missing values * Reset indices * Converted numerical columns to proper data types * Standardized column names * Added regional identifiers --- ## 📈 Key Insights * Higher temperatures tend to be associated with increased fire activity. * Low rainfall periods show elevated fire risk. * Fire Weather Index (FWI) is strongly related to fire occurrence. * Regional differences can be observed in weather patterns and fire behavior. --- ## 🚀 How to Run ```bash git clone github.com cd algerian-forest-fires-analysis pip install -r requirements.txt jupyter notebook ``` --- ## 🎯 Future Improvements * Fire Prediction Models * Logistic Regression * Random Forest Classification * Feature Importance Analysis * Wildfire Risk Prediction Dashboard