Devsahu7/algerian_forest_fires-

# algerian_forest_fires- # 🌲 Algerian Forest Fire Prediction using Multiple Linear Regression This project applies **Multiple Linear Regression** to predict forest fire risk using the Algerian Forest Fires dataset. The model analyzes multiple environmental and weather-related features to estimate fire weather index and related fire indicators. --- ## 📌 Project Objective To build a regression model that predicts fire-related outcomes using meteorological data such as: - Temperature - Relative Humidity (RH) - Wind Speed - Rain - FFMC - DMC - DC - ISI - BUI - FWI --- ## 🛠️ Technologies Used - Python - NumPy - Pandas - Matplotlib - Seaborn - Scikit-learn - Jupyter Notebook --- ## 📊 Steps Performed 1. Data Cleaning and Preprocessing 2. Exploratory Data Analysis (EDA) 3. Feature Selection 4. Train-Test Split 5. Model Training (Multiple Linear Regression) 6. Model Evaluation (R² Score, MSE, MAE) 7. Visualization of Predictions --- ## 📉 Model Evaluation Metrics - Mean Squared Error (MSE) - Mean Absolute Error (MAE) - R² Score These metrics are used to evaluate the performance of the regression model. --- ## 📈 Learning Outcomes - Understanding Multiple Linear Regression - Handling real-world datasets - Data visualization techniques - Model evaluation and interpretation - Feature importance analysis --- ## 🚀 Future Improvements - Apply Regularization (Ridge & Lasso Regression) - Hyperparameter tuning - Compare with Random Forest Regressor - Deploy model using Flask or Streamlit - Add interactive prediction interface --- ## 👨‍💻 Author Dev Sahu GitHub: github.com