Capstone: A Comparative Study of Algorithms

Implement early stopping based on validation set performance to prevent overfitting.

Structure your code to create a reusable training and evaluation pipeline for any implemented model.

Select a suitable benchmark dataset (e.g., from Scikit-learn or Kaggle) for classification and regression.

Systematically train and evaluate every algorithm you have built on the benchmark dataset(s).

Tune at least one key hyperparameter for each model using your validation set logic.

Generate a comparative report analyzing the performance, training time, and characteristics of each algorithm.