NPROS: A Not So Pure Random Orthogonal Search Algorithm –A Suite of Random Optimization Algorithms Driven by Reinforcement Learning

an AntennaCAT compatable sweep optimizer

This project predicts credit scores ('Good', 'Standard', 'Poor') using a streamlined ML pipeline. It includes data extraction, cleaning, and preprocessing. Key techniques are Mutual Information for feature selection, PCA for dimensionality reduction, and XGBoost for accurate and efficient model training, ensuring reliable and robust predictions.

Solving The Travelling salesman problem using some Random Search Algorithms

DEPTs: Parameter tuning for software fault prediction with different variants of differential evolution *** Parameter tuners for software analytics problems ***