| Affiliations: | STEM Research Leadership |
| Project Leader: | Vishnudas Kulangara Veettil
vishnudasveettil@tamu.edu Animal Science |
| Faculty Mentor: | Karun Kaniyamattam, Ph.D. |
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Meeting Times:
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TBA |
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Team Size:
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5
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| Open Spots: | 0 |
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Special Opportunities:
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Earn co-authorship on publications upon successful completion of the project, present findings at campus conferences or symposiums, gain hands-on experience with real-world agricultural economic data, learn advanced Python programming and machine learning applications
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Team Needs:
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Skills preferred include: Proficiency in Python (mandatory), familiarity with libraries such as Pandas, NumPy, Scikit-learn, Statsmodels, and Matplotlib, experience in web scraping and data collection and commitment to weekly meetings with regular updates. |
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Description:
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Project focuses on advancing economic modeling within the beef value chain by analyzing cow-calf, stocker, and feedlot operations. The research involves the creation of synthetic datasets, web scraping to gather real-world economic data, and efficient data sorting to build robust analytical frameworks. Using Python, the project applies a combination of Univariate time-series models, including ARIMA, SARIMA, and SARIMAX, and Multivariate machine learning models such as Random Forest, Adaboost, and Support Vector Regression. The primary objectives include forecasting key economic indicators like costs, revenues, and profits, and identifying critical performance drivers across the beef value chain. A significant focus will be placed on developing probabilistic models to account for uncertainties in predictions and enhance decision-making. Students will work on tasks such as preprocessing datasets, feature selection, and implementing hyperparameter tuning to optimize model performance. This research aims to create a scalable and reliable machine learning pipeline for modeling complex economic dynamics in the beef industry. |