Incorporating Machine Learning Techniques to Predict Risk Assessment in Project Timeline Management

Abstract

Numerous project management applications have made extensive use of machine learning algorithms wherein risk assessment module plays an important role in project timeline management. In order to increase the likelihood that software projects will be successfully developed, research into project management applications that involve risk assessment has become increasingly popular in recent years, especially with the use of machine learning techniques to pinpoint project risk indicators before the project’s development even begins. In this research, we have applied five cutting-edge machine learning techniques namely Artificial Neural Network (ANN), Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), and K-Nearest Neighbors (K-NN) on two different risk datasets. Moreover, hyper-parameter tuning is applied using different parameters in each machine learning technique and find the optimal risk predictions in each of them. On the basis of comprehensive literature review, a methodology was proposed which made it easier for the project manager and subject matter experts to plan and reduce risks at an early stage by determining the level of risk involved. Moreover, different experiments are performed on benchmark datasets to compare the models performance based on the applied parameters. This research will assist domain experts, data analysts, developers, and researchers to better develop ML models and make effective predictions which provide guidance towards making effective decisions to meet the project’s deadline and organizational goals.