Authors - Maen Hammad, Hamza Hanandeh, Ahmed Fawzi Otoom Abstract - For software development processes, software project management plays significant role in accomplishing successful projects. One major activity in project management is the forecasting of project expenses, time duration and resources that need to be determined and allocated in advance. This paper proposes an automated prediction model to predict the effort, in terms of months, required to complete a software project. The model applies different machine learning algorithms to predict the required effort in terms of months. A set of experiments has been applied on the ISBSG dataset. The result shows that both X-Gradient Boosting and Gradient Boosting algorithms produce the best classification results. While the Logistic Regression and SVM produce the lowest accuracy results. The results also show the positive impact of the feature selection process on the classifier’s accuracy. The goal is to minimize the project’s features to the most influential ones in the prediction process.
