# Overview The repository is designed for the course "IS-477: Data Management, Curation, and Reproducibility" for the Fall 2023 semester. I'm tasked with understanding and practicing data management and appreciating the essence of reproducibility. My primary assignment is to reproduce specific machine learning outcomes from a research paper (Citation: Ding, F., Hardt, M., Miller, J., & Schmidt, L. (2022). Retiring Adult: New Datasets for Fair Machine Learning. arXiv:2108.04884. Retrieved from https://doi.org/10.48550/arXiv.2108.04884), using a logistic regression model on the UCI Adult dataset. I'll be using Git, GitHub, and Python to achieve this. Throughout this journey, I'll delve deep into data rights, ensure the quality of the data I work with, and automate my data workflows. Additionally, I'll learn the importance of documenting my work and preserving research. This project is a testament to the importance of transparency and precision in research.