TY - GEN
T1 - Agile project status prediction using interpretable machine learning
AU - Forouzesh Nejad, Ali Akbar
AU - Arabikhan, Farzad
AU - Williams, Nigel Leroy
AU - Gegov, Alexander
AU - Sari, Omer Faruk
AU - Bader-El-Den, Mohamed
PY - 2024/10/9
Y1 - 2024/10/9
N2 - Monitoring and forecasting the progress of information technology projects stands as a significant challenge in project management. Over the past two decades, agile project management has become a crucial factor influencing project success. Despite this, existing research has not presented a comprehensive model capable of predicting project outcomes based on agile features. In light of this, this study aims to develop a predictive model for information technology project outcomes using agility metrics. The results indicate that metrics related to teamwork and the team's capabilities, along with their collective experience, have the most significant impact on project success. The study employs the Decision Tree as an interpretable model to establish rules and predict project success. The accuracy of the model designed in this study is an impressive 97%, surpassing the accuracy of SVM at 71% and KNN at 82%.
AB - Monitoring and forecasting the progress of information technology projects stands as a significant challenge in project management. Over the past two decades, agile project management has become a crucial factor influencing project success. Despite this, existing research has not presented a comprehensive model capable of predicting project outcomes based on agile features. In light of this, this study aims to develop a predictive model for information technology project outcomes using agility metrics. The results indicate that metrics related to teamwork and the team's capabilities, along with their collective experience, have the most significant impact on project success. The study employs the Decision Tree as an interpretable model to establish rules and predict project success. The accuracy of the model designed in this study is an impressive 97%, surpassing the accuracy of SVM at 71% and KNN at 82%.
KW - Interpretable Machine Learning
KW - Decision Tree
KW - Agile Project Management
KW - Project Status Prediction
UR - https://www.ieee-is.org/important-deadlines/
U2 - 10.1109/IS61756.2024.10705197
DO - 10.1109/IS61756.2024.10705197
M3 - Conference contribution
SN - 9798350350999
T3 - 2024 IEEE 12th International Conference on Intelligent Systems (IS)
BT - Proceedings of 2024 IEEE 12th International Conference on Intelligent Systems (IS)
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE International Conference on Intelligent Systems
Y2 - 29 August 2024 through 31 August 2024
ER -