naveennn2924/Algerian_forest_fire_prediction

# 🌲 Algerian Forest Fire Prediction 🔥 ## 📌 Project Overview Forest fires pose a significant threat to ecosystems and human life. This project focuses on predicting forest fires in Algeria using multiple regression techniques to analyze environmental factors affecting fire intensity. ## 🚀 Features - Implemented **Linear Regression, Lasso, Ridge Regression, and ElasticNet** models. - Evaluated models based on **Mean Squared Error (MSE) and R² Score**. - Achieved high accuracy in predicting fire intensity. - Utilized **Algerian Forest Fire Dataset** for training and testing. ## 📊 Results | Model | MSE | R² Score | |----------------|------|----------| | **Linear Regression** | 1.0276 | 0.9704 | | **Lasso Regression** | 2.1640 | 0.9377 | | **Ridge Regression** | 1.0690 | 0.9692 | | **ElasticNet** | 4.9449 | 0.8575 | ## 🔧 Technologies Used - **Python** (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn) - **Jupyter Notebook** - **Machine Learning Models (Linear, Ridge, Lasso, ElasticNet Regression)** ## 📂 Dataset The **Algerian Forest Fire Dataset** was used, which contains meteorological and environmental attributes like: - Temperature - Relative Humidity - Wind Speed - Rainfall - Fine Fuel Moisture Code (FFMC) - Duff Moisture Code (DMC) - Initial Spread Index (ISI) - Fire Weather Index (FWI) ## 📖 How to Run the Project 1. Clone the repository: ```bash git clone github.com 2. Navigate to the project directory: cd Algerian-Forest-Fire-Prediction 3. Install dependencies: pip install -r requirements.txt 4. Run the Jupyter Notebook or Python script: ## Notebook Open Algerian_Forest_Fire_Prediction.ipynb and execute the cells. 📈 **Model Evaluation** **Mean Squared Error (MSE)**: Measures how close predictions are to actual values. **R² Score**: Indicates the goodness of fit. ## 🏆 Key Takeaways Linear Regression and Ridge Regression provided the most accurate predictions. Lasso Regression penali …