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16- Data Mining / LLM Tabular Preprocessing with Dictionary Groups

Dictionary-Based Feature Grouping for LLM/AI Pipelines

Institution: Pontifical Catholic University of São Paulo (PUC-SP)
School: Faculty of Interdisciplinary Studies
Program: Humanistic AI and Data Science Semester: 2nd Semester 2025
Professor: Professor Doctor in Mathematics Daniel Rodrigues da Silva

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Important

⚠️ Heads Up

• Projects and deliverables may be made publicly available whenever possible.
• The course emphasizes practical, hands-on experience with real datasets to simulate professional consulting scenarios in the fields of Data Analysis and Data Mining for partner organizations and institutions affiliated with the university.
• All activities comply with the academic and ethical guidelines of PUC-SP.
• Any content not authorized for public disclosure will remain confidential and securely stored in private repositories.

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Tip

This repository is a review of the Statistics course from the undergraduate program Humanities, AI and Data Science at PUC-SP.

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References

• Chen, X., et al. (2024). LLM-based feature generation from text for interpretable machine learning. arXiv preprint. Retrieved from arxiv.org/html/2409.07132v2

• DataCamp. (2024). Pandas GroupBy Explained: Syntax, Examples, and Tips. Retrieved from datacamp.com/tutorial/pandas-groupby

• GeeksforGeeks. (2024). Pandas dataframe.groupby() Method. Retrieved from geeksforgeeks.org/pandas/python-pandas-dataframe-groupby

• Machine Learning Mastery. (2024). Feature Engineering with LLM Embeddings: Enhancing Scikit-learn Models. Retrieved from machinelearningmastery.com

• McKinney, W. (2017). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython (2nd ed.). O’Reilly Media.

• Pandas Documentation. (2024). Group by: split-apply-combine. Retrieved from pandas.pydata.org/docs/user_guide/groupby.html

• VanderPlas, J. (2016). Python Data Science Handbook: Essential Tools for Working with Data. O’Reilly Media.

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