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Project # 25
Machine Learning Applications for Predictive Modeling and Data-Driven Decision Making in Industrial Engineering
| Team Lead: | Mohammed Abu-Saqer |
| Other Team Members: | Suleiman Malik | Daniel Garza | Alexandra Mata Aguilar | Mahima Babu | Omar Nagem | Daniel Doss | Nelly Jimenez | Daniel Gomez | Arjun Nair | John Tran |
| Faculty Advisor(s): | Dr. Aera Leboulluec |
| Department: | IMSE |
| Project Type: | Other Undergraduate |
| Title: | Machine Learning Applications for Predictive Modeling and Data-Driven Decision Making in Industrial Engineering |
| Abstract: | This project applies machine learning techniques to real-world datasets containing multiple input variables to develop predictive models and analyze relationships between system factors and performance outcomes. The objective is to estimate a selected target variable and generate insights that support data-driven decision-making within engineering contexts. The study includes data preprocessing, feature selection, model development, and performance evaluation using Python. Multiple machine learning algorithms, including regression and classification approaches, will be implemented and compared using appropriate evaluation metrics to determine the most effective modeling strategy. The results will demonstrate how machine learning methods can extract meaningful information from data and provide practical insights for engineering and applied systems. This work highlights the importance of predictive analytics, model selection, and validation in improving understanding of complex systems and supporting informed decision-making. |
| Poster: | View Poster #25 (PDF) |
| Judges Scoring: | Submit Evaluation (judges only) |