Ridge, LASSO or Elastic Net Regression — Regularised Model Build

I will build a regularised regression model using Ridge, LASSO, or Elastic Net — with cross-validated lambda selection, coefficient path analysis, variable selection interpretation for LASSO, and model performance comparison against OLS baseline — delivering a Python or R implementation with a production-ready prediction function. Regularised regression is appropriate when OLS fails due to multicollinearity, high-dimensional predictors exceeding sample size, or the need for automatic variable selection; the regularisation parameter must be selected through cross-validation rather than arbitrarily for the model to be valid.

The analysis covers standardisation of predictors, cross-validated lambda selection (LassoCV, RidgeCV, or ElasticNetCV), coefficient path plot, selected variable list for LASSO, alpha optimisation for Elastic Net, holdout test set performance (RMSE, R-squared), comparison with OLS baseline, and a Python or R prediction function for new observations.

Designed for data scientists and analysts building high-dimensional predictive models, economists modelling with many correlated indicators, and genomics or bioinformatics researchers requiring automatic feature selection.