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Software programmes may have flaws resulting from requirements study, definition, and other software development operations. Applications' management and quality regulation would benefit immensely from programmers being able to predict the quality of early-stage apps. MCQP and MCLP(Multiple Criteria Quadratic Programming and Multiple Criteria Linear Programming) were the two techniques employed in early studies to determine the programme quality. By utilising pertinent information from a sizable dataset, we attempted to increase estimation accuracy in this paper.Random Decision Forest, Gradient Boosting, Decision Tree Classifier, Naive Bayes Classifier, and other machine learning algorithms are used to analyse the data and forecast software quality andto demonstrate the connection between the improvement and quality attributes. The outcomes of the experiment demonstrate the level of software quality.