SiddarthaK16/Algerian-fire-regression-ml

ML project preidicting forest fire FWI using regression models. # 🔥 Algerian Forest Fire Prediction (ML Project) ## 📌 Overview This project focuses on analyzing and predicting patterns in the **Algerian Forest Fires dataset** using multiple machine learning models. The goal was to explore how different regression techniques perform on the same dataset and understand the impact of regularization. --- ## 📊 Dataset * Algerian Forest Fires Dataset --- ## 🔍 What I Did * Performed **Exploratory Data Analysis (EDA)** * Cleaned and preprocessed the dataset * Applied **feature scaling** * Trained multiple regression models: * Linear Regression * Ridge Regression * Lasso Regression * ElasticNet Regression * Evaluated models using: * R² Score * Mean Absolute Error (MAE) * Compared model performance * Saved trained models using **pickle** --- ## 🤖 Models Used * Linear Regression * Ridge Regression (L2 Regularization) * Lasso Regression (L1 Regularization) * ElasticNet Regression (L1+L2) --- ## 📈 Results * Regularization helped improve model stability * Ridge and Lasso reduced overfitting compared to Linear Regression * Model performance was evaluated using R² and MAE *(Detailed results and plots are available in the notebooks)* --- ## 🧠 Key Learning This project helped in understanding: * The importance of regularization (L1 vs L2) * Model comparison on real-world data * End-to-end ML workflow (EDA → Training → Evaluation → Saving) --- ## 📬 Connect If you found this useful or have suggestions, feel free to connect or contribute! --- ⭐ If you like this project, consider giving it a star!