Summary of Predicting the Impact Of Scope Changes on Project Cost and Schedule Using Machine Learning Techniques, by Soheila Sadeghi
Predicting the Impact of Scope Changes on Project Cost and Schedule Using Machine Learning Techniques
by Soheila Sadeghi
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning-based approach is proposed to develop predictive models for estimating the impact of scope changes on project cost and schedule in the context of project management. The study utilizes a comprehensive dataset containing detailed information on project tasks, including the Work Breakdown Structure (WBS), task type, productivity rate, estimated cost, actual cost, duration, task dependencies, scope change magnitude, and scope change timing. Multiple machine learning models are developed and evaluated to predict the impact of scope changes on project cost and schedule, including Linear Regression, Decision Tree, Ridge Regression, Random Forest, Gradient Boosting, and XGBoost. The performance of these models is assessed using Mean Squared Error (MSE) and R2, with residual plots generated to evaluate goodness of fit and identify patterns or outliers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers developed predictive models to help project managers predict how changes to a project’s scope will affect its cost and schedule. They used machine learning techniques and a big dataset about different types of tasks within a project. The models they tested included linear regression, decision trees, and others. To see how well the models worked, they looked at metrics like mean squared error and R2. This could be helpful for people who manage projects and want to make informed decisions about changes to their scope. |
Keywords
» Artificial intelligence » Boosting » Decision tree » Linear regression » Machine learning » Mse » Random forest » Regression » Xgboost
