Ganglet/Algerian-Forest-Fire-Model

A Python-based model forecasting forest fires in Algeria using historical weather and fire data, achieving 98% accuracy with regression techniques and offering real-time predictions via a Flask web app. # Algerian Forest Fire Prediction Project ## Project Overview **Objective:** Develop a predictive model to forecast forest fires in Algeria using historical weather and fire data. The project focuses on two regions – **Bejaia** (Northeast) and **Sidi Bel-Abbes** (Northwest) – with data collected from June 2012 to September 2012. **Key Achievements:** - Achieved **98% accuracy** in predicting forest fire occurrences. - Deployed a **Flask**-based web application for real-time predictions. - Utilized multiple regression techniques for robust model performance. --- ## Technologies & Tools - **Programming Language:** Python - **Data Processing:** Pandas, NumPy - **Data Visualization:** Matplotlib, Seaborn - **Machine Learning Models:** Linear Regression, Ridge, Lasso, ElasticNet (using Scikit-learn) - **Web Development:** Flask - **Model Serialization:** Pickle --- ## Dataset Overview - **Total Instances:** 244 - **Fire:** 138 instances - **Not Fire:** 106 instances - **Data Collection Period:** June 2012 – September 2012 - **Key Attributes:** - **Date:** Day, month, and year information. - **Temp:** Temperature at noon (22°C to 42°C). - **RH:** Relative Humidity (21% to 90%). - **Ws:** Wind Speed (6 km/h to 29 km/h). - **Rain:** Total rainfall in mm (0 to 16.8 mm). - **FWI Components:** Includes metrics like Fine Fuel Moisture, Duff Moisture, Drought Code, etc. --- ## Methodology & Workflow 1. **Data Cleaning & Preprocessing** - Handled missing values and standardized features. - Transformed raw data for effective modeling. 2. **Exploratory Data Analysis (EDA)** - Visualized attribute distributions and correlations using Seaborn and Matplotlib. - Identified key predictors affecting fire occurrences. 3. **Feature Engineering & Selection** - Extracted and selected features to enhance model performance. - Applied statistical techniques to validate feature importance. 4. **Model Training & Evaluation** - Implemented and compared several regression models: - **Lin …