HanniKanchap/Algerian-Forest-Fire-Project

The Algerian Forest Fire ML Project aims to predict the likelihood and severity of forest fires in Algeria using machine learning models. The dataset consists of 244 instances collected from two regions: Bejaia (northeast Algeria) and Sidi Bel-abbes (northwest Algeria), spanning June to September 2012 # **Algerian Forest Fire Prediction using Ridge Regression** This project applies **Ridge Regression** to predict forest fire occurrence in **Bejaia** and **Sidi Bel-abbes, Algeria** using meteorological data. By mitigating multicollinearity, Ridge Regression enhances model stability and generalization. ## 📂 **Dataset Overview** - **Source:** Algerian Forest Fires Datas… - **Instances:** 244 (138 fire cases, 106 no-fire cases). - **Features:** - Temperature (°C) - Relative Humidity (%) - Wind Speed (km/h) - Rainfall (mm) - Fire Weather Index (FWI) ## 🎯 **Objective** - Predict **fire occurrence** based on environmental conditions. - Improve model performance by **reducing overfitting** using **Ridge Regression**. - Aid authorities in fire prevention and resource allocation. ## 🛠 **Approach** 1. **Data Preprocessing:** Handling missing values and scaling features. 2. **Exploratory Data Analysis (EDA):** Identifying feature correlations. 3. **Feature Engineering:** Selecting impactful attributes. 4. **Model Training:** Implementing **Ridge Regression** for prediction. 5. **Evaluation:** Comparing Ridge Regression with **Linear Regression**, **Lasso**, and **Random Forest**. ## 🏆 **Results & Insights** - Ridge Regression effectively handles **multicollinearity** and provides **stable predictions**. - **Fire Weather Index (FWI)** is a strong predictor of fire risk. - Model optimizations improve early detection and emergency planning. ## 🚀 **How to Run the Project** 1. Clone the repository: ```bash git clone github.com ``` 2. Install dependencies: ```bash pip install -r requirements.txt ``` 3. Run the analysis and model training: ```bash python ridge_regression.py ```