Description

This study proposed Decision tree-based models to predict the software effort. A decision tree is a simple yet powerful approach and it can be taken as the base for Random Forest, which is known as an ensemble of decision trees. This project exposes that ensembles of decision trees outperform single decision trees in terms of the mean magnitude of the relative error and prediction rate and also presents decision tree Technique, for effort evaluation in the field of software development.