Project Teamwork Assessment and Success Rate Prediction Through Meta-Heuristic Algorithms

In this chapter, machine learning algorithms along with association rule analysis are applied to measure how the project teamwork success rate depends on various technical and soft skill factors of a software project. A real-life dataset is taken form UCI archive on project teamwork, which comprises of 84 features or attributes with 64 samples. The most effective feature set is therefore selected using meta-heuristic algorithms (i.e., particle swarm optimization [PSO] and simulated annealing [SA]) and then the data are given to support vector machine (SVM) and k-nearest neighbor (KNN) classifier for classification. Association rule mining is also used for rule generation among the different features of software project team to determine support and confidence. This chapter deals with how the project-based learning helps to manifest the students towards professionalism. Project Teamwork Assessment and Success Rate Prediction Through Meta-Heuristic Algorithms

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