Optimalizace strojového učení pro predikci KPI

This thesis aims to optimize the machine learning algorithms for predicting KPI metrics for an organization. The organization is predicting whether projects meet planned deadlines of the last phase of development process using machine learning. The work focuses on the analysis of prediction models and sets the goal of selecting new candidate models for the prediction system. We have implemented a system that automatically selects the best feature variables for learning. Trained models were evaluated by several performance metrics and the best candidates were chosen for the prediction. Candidate models achieved higher accuracy, which means, that the prediction system provides more reliable responses. We suggested other improvements that could increase the accuracy of the forecast.

software development, KPI, NPI, machine learning, preprocessing, cross-validation, classification, prediction, boosting, neural networks, ensemble, boosting, support vector machines, decision tree, random forest, naive bayes, performance metrics, ROC