This research proposes a presents a semi-autonomous framework to train machine learning models that identifies more optimized configurations across different contexts. Several case studies were conducted to determine the feasibility and extensibility of the proposed framework. This dataset contains all datasets used in development, data collected to produce results, configuration files for each experiment, and code used to count lines