HetalBagal/Ridge-Lasso-Elasticnet-Regression-

Machine learning project implementing Ridge, Lasso, and ElasticNet regression using the Algerian Forest Fire dataset with data cleaning and model comparison. # 🔥 Ridge, Lasso & ElasticNet Regression ## 📌 Project Overview This project demonstrates the implementation of **regularization techniques** in machine learning using the **Algerian Forest Fire Dataset**. The main goal is to understand how Ridge, Lasso, and ElasticNet help in reducing overfitting and improving model performance. --- ## 📁 Repository Structure This repository contains: * `Algerian_forest_fires_dataset_UPDATE.csv` → Raw dataset * `Algerian_forest_fires_cleaned_dataset.csv` → Cleaned dataset * `Model Training.ipynb` → Data preprocessing and model training * `Ridge, Lasso Regression.ipynb` → Implementation of Ridge, Lasso & ElasticNet --- ## ⚙️ Workflow 1. Data Cleaning 2. Handling Missing Values 3. Feature Scaling 4. Model Training 5. Model Evaluation --- ## 🤖 Models Used * Ridge Regression (L2 Regularization) * Lasso Regression (L1 Regularization) * ElasticNet Regression (Combination of L1 & L2) --- ## 📊 Results The performance of all three models is compared using test data to understand their effectiveness. --- ## 🧠 Key Learnings * Difference between L1 and L2 regularization * How Lasso performs feature selection * Why ElasticNet can outperform individual methods * Importance of scaling in regression models --- ## 📌 Dataset Algerian Forest Fire Dataset ---