Model Selection and Final Project

Perform hyperparameter tuning for a given model using grid search and random search with cross-validation.

Develop a decision-making framework to select an appropriate machine learning algorithm based on problem type and data characteristics.

Execute a full preprocessing and feature engineering pipeline on a new, raw dataset.

Train, evaluate, and compare multiple candidate models on the prepared dataset.

Select and justify the choice of a final model based on performance metrics and problem requirements.