AI-Enabled Risk Prediction Models For Project Failure Mitigation
DOI:
https://doi.org/10.29284/kqmvg594Keywords:
Project Failure Prediction, Artificial Intelligence, Machine Learning, Risk Management, Predictive Analytics.Abstract
The failure of projects remains one of the significant issues in all industries because projects have become more complex, uncertain, and risky. The conventional methods of project risk management are based usually on qualitative measures and fixed risk lists, and thus such methods do not allow them to make early and precise predictions of failure. This study aims at developing an AI-driven risk forecasting predictive model that would be used to mitigate risks of project failure using machine learning tools on past project information. The model development and evaluation was done using a structured dataset of 1, 200 successful projects and several risk-based characteristics (such as cost variance, schedule deviation, team experience, and requirement volatility). Four machine learning algorithms namely, Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine were put to practice and compared. The outcomes of the experiments are able to show that AI-based models can increase predictive accuracy and the early identification of risks by great margins. The Random Forest model demonstrated the best performance with the highest accuracy of 88.9 and the precipitation of 87.5, recall of 86.2 and F1-score of 0.87. Another aspect that the Support Vector Machine performed well with an accuracy of 86.7 was compared to Logistic regression and decision tree whose accuracy of 81.4 and 83.6. The analysis of feature importance has determined cost variance, schedule delay as the most highly influential factors of project failure. The results prove that the AI-based risk prediction models can offer a powerful, data-driven strategy of proactive mitigation of project failures, which can help to make informed decisions and enhance project success rates.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
