![]() ![]() Data science used two distinct languages Python and R to visualize big data undeviatingly. The premature data visualization system met some difficulties and there has some solution for handle this kind of big quantity of data. ![]() Data visualization techniques are authenticated scientifically as thousand times reliable rather than textual representation. Decent use of the persistent information can assist to overcome provocations and support to establish further sophisticated judgment. On the other hand, in ISBSG dataset and in PROMISE dataset, the ensemble methods Soft Voting & Stacking and Gradient Boosting & Bagging generated the best results respectively with the values 96.31% and 94.59%.Ī tremendous amount of data comes with a vast amount of knowledge. In EBSPM dataset, logistic regression produced the best accuracy value as 96.67%. Ensemble methods, which enable the use of different machine learning algorithms together, contributed to obtaining results with higher accuracy rates in software quality prediction. The obtained accuracy values were compared with the existing ones in the literature. Algorithms were applied on three datasets that include software metrics. The study shows that data pre-processing, feature extraction and machine learning algorithms provide more accurate results in predicting the quality of the software. The aim of this study is to predict software quality with higher accuracy than previous studies. ![]() In this study, defect density is used as the feature that represents the quality. There are several metrics that provide the quality measure with respect to different types of software. Software quality prediction is used at various stages of projects. ![]()
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