View in EDS

Project Development and Evaluation of Project Risk Analysis Model Using Machine Learning Algorithms.

Saved in:
Bibliographic Details
Title: Project Development and Evaluation of Project Risk Analysis Model Using Machine Learning Algorithms.
Authors: Kumar, K. Tulasi Krishna, Priya, P. T. S.
Source: International Scientific Journal of Engineering & Management; Sep2025, Vol. 4 Issue 9, p1-10, 10p
Subject Terms: MACHINE learning, RANDOM forest algorithms, RISK assessment, MODEL validation, LOGISTIC regression analysis, PROJECT managers, PROJECT management
Abstract: Projects are undertaken by the contemporary organizations to experience significant growth, deal with industry competition and achieve sustainability. The increase in size, complexity and duration of the projects resulted in facing several risks and this project is undertaken to develop a project risk analysis model with the help of machine learning algorithms. Identifying that machine learning is feasible for making risk analysis process autonomous, random forest and logistic regression were the two machine learning algorithms utilized to develop the model and then train it. Evaluation of the project risk analysis model showed its effectiveness in recording high number of true positives and true negatives during the detection activity of project risks. From evaluation, random forest is identified as the effective algorithm due to high prediction accuracy, robustness and avoiding overfitting problem. [ABSTRACT FROM AUTHOR]
Copyright of International Scientific Journal of Engineering & Management is the property of International Scientific Journal of Engineering & Management and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Complementary Index
Description
Abstract:Projects are undertaken by the contemporary organizations to experience significant growth, deal with industry competition and achieve sustainability. The increase in size, complexity and duration of the projects resulted in facing several risks and this project is undertaken to develop a project risk analysis model with the help of machine learning algorithms. Identifying that machine learning is feasible for making risk analysis process autonomous, random forest and logistic regression were the two machine learning algorithms utilized to develop the model and then train it. Evaluation of the project risk analysis model showed its effectiveness in recording high number of true positives and true negatives during the detection activity of project risks. From evaluation, random forest is identified as the effective algorithm due to high prediction accuracy, robustness and avoiding overfitting problem. [ABSTRACT FROM AUTHOR]
ISSN:25836129
DOI:10.55041/ISJEM05026