Improving Software Cost Estimation Process through Classifcation Data Mining Algorithms using WEKA Tool

Abstract

Today, the Software Cost Estimation (SCE) is one of the hot research areas among the researchers. Cost estimation is a method of the achievable cost of a product, thing, program, or an undertaking, enrolled in light of available information. It attempts to data predict good results to combined both of the field software engineering and data mining in this work. It generates the accurate cost of the projects with the help of different datasets whose cost and effort is unpredicted and investigate the common cost factors. In this paper, the most popular data mining algorithms such as Support Vector Machine (SVM) and Naïve Bayes (NB) have been used to estimate the cost of the projects and the accuracy of machine learning algorithms.