This study explores the application of predictive modeling to the TensorFlow project, a prominent open-source machine learning library. Our research aims to develop and validate predictive models that can accurately forecast issue closures, thereby enhancing project management decisions. We systematically collect data from the TensorFlow GitHub repository through data crawling using a self-built python script, focusing on key metrics such as commit history, issue tracking, and contributor metrics. The data comprises 120,000 commit messages and 25,053 issue tickets. We apply descriptive analytics to identify insights into the project's development dynamics and implement nineteen models using various machine learning techniques, including linear regression, ridge regression, and ensemble methods. The final model, based on Lasso regression, achieves high accuracy with the lowest Mean Squared Error (MSE), the Average Cross-Validation MSE and highest R-squared value compared to other models. The final model also utilizes advanced feature engineering techniques, such as lagged features and cyclical time transformations, which help enhance the model's performance. Applied time-aware validation shows the need to further refine the evaluation design to enhance temporal modeling.

Predictive modeling, Software project management, Issue closure forecasting, TensorFlow, Machine learning