Ridge and Lasso Regularization Tuner for Financial Modeling

Financial datasets are full of correlated, noisy features, and ordinary regression happily overfits them. Ridge and Lasso regularization are the standard defenses, but choosing between them — and tuning their strength — requires seeing how each one reshapes a model. This project builds a tool that makes those effects directly observable. The intern develops a web tool where users upload a dataset, apply feature scaling, and train Ridge and Lasso models from scikit-learn side by side, with GridSearchCV sweeping regularization strengths under cross-validation. Coefficient visualizations show the two techniques' signature behaviors — Ridge shrinking weights smoothly toward zero while Lasso zeroes some out entirely, performing implicit feature selection — alongside evaluation metrics for each configuration. The workflow is prototyped in Jupyter with pandas and NumPy, then deployed as a FastAPI microservice so tuned models become reachable over REST. The intern gains a working command of regularization, cross-validated tuning, and the reasoning behind sparse versus dense solutions, plus deployment experience packaging an analytical workflow as a service — a combination directly relevant to quantitative finance teams.