yuvraj-singh047/Algerian-Forest-Fire-Regression

# 🔥 Algerian Forest Fire – Regression Models with Cross Validation This project applies multiple **regression techniques** to predict the **Fire Weather Index (FWI)** using the Algerian Forest Fires dataset. ## 📌 Models Implemented - Linear Regression - Ridge Regression - Lasso Regression - Elastic Net - RidgeCV - LassoCV - ElasticNetCV ## 🧠 Key Concepts Used - Train–Test Split - Feature Correlation Analysis - Multicollinearity Reduction - Feature Scaling (StandardScaler) - Regularization (L1, L2) - Cross-Validation for Hyperparameter Tuning ## 📊 Evaluation Metric - R² Score ## 🚀 Why Cross-Validation? Cross-validation automatically selects the best regularization parameter (alpha), improving model generalization and preventing overfitting. ## 🛠️ Tech Stack - Python - NumPy - Pandas - Matplotlib - Seaborn - Scikit-learn ## 📁 Dataset Algerian Forest Fires Dataset (Cleaned) --- ## 📋Project Sturcture ``` Algerian-Forest-Fire-Regression/ │ ├── data/ │ └── Algerian_forest_fires_cleaned_dataset.csv │ ├── notebook/ │ └── Algerian_forest_fires_model.ipynb │ ├── README.md ├── requirements.txt ``` --- ⭐ If you like this project, consider starring the repo!