YashRank579/Project2_algerian_forest_prediction

A machine learning project on the Algerian Forest Fires dataset involving data cleaning, visualization, and regression modeling using Lasso, Ridge, and ElasticNet. # Project2_algerian_forest_prediction A machine learning project on the Algerian Forest Fires dataset involving data cleaning, visualization, and regression modeling using Lasso, Ridge, and ElasticNet. # Algerian Forest Fires Regression Analysis 🌲🔥 This project analyzes the **Algerian Forest Fires dataset** using machine learning techniques to model and predict fire occurrences based on meteorological and regional features. The analysis includes **data cleaning**, **visualization**, and **regression modeling** using **Lasso**, **Ridge**, and **ElasticNet**—along with their **cross-validated versions**. --- ## 🔍 Project Objectives - Clean and preprocess the Algerian Forest Fires dataset - Visualize key patterns and relationships in the data - Apply and compare multiple regression models: - Lasso Regression - Ridge Regression - ElasticNet Regression - LassoCV, RidgeCV, ElasticNetCV (cross-validation) - Evaluate models using performance metrics like MAE and R² Score --- ## 🛠️ Technologies Used - Python 3.x - NumPy, Pandas - Matplotlib, Seaborn - Scikit-learn --- ## 📊 Visualizations Exploratory Data Analysis (EDA) includes: - Correlation heatmaps - Pairplots - Feature distributions - Fire occurrence patterns --- ## 🤖 Machine Learning Models | Model | Regularization | Cross-Validated Version | |-------------------|----------------|--------------------------| | Lasso | ✅ | ✅ (`LassoCV`) | | Ridge | ✅ | ✅ (`RidgeCV`) | | ElasticNet | ✅ | ✅ (`ElasticNetCV`) | Each model is evaluated using: - **R² Score** - **Mean Absolute Error (MAE)** ---