Details

This project features a custom-built ElasticNet Regression implemented entirely from scratch using NumPy and pandas, without external libraries like scikit-learn or TensorFlow. Optimized with gradient descent, it provides a hands-on understanding of ElasticNet’s mechanics, which blend L1 (Lasso) and L2 (Ridge) regularization for effective feature selection and handling correlated predictors. This implementation showcases fine-tuned optimization techniques, making it a valuable resource for mastering regression from first principles.