A study examining the relationship between B-cell levels and metastatic breast cancer outcomes. I explored potential correlations between immune markers and disease progression using publicly available clinical datasets. This project was part of my undergraduate capstone and sparked my long-term interest in medical research.

Built and tested classification models to identify patterns in fraudulent transactions. Focused on balancing accuracy with interpretability, and included feature selection, model comparison, and evaluation metrics. I created clear documentation to explain the approach and results to non-technical stakeholders.

Analyzed the California housing dataset to predict median house values based on location and socioeconomic indicators. Cleaned and visualized the data, built regression models, and shared insights in a web-based dashboard. This project helped strengthen my understanding of data preparation and model performance.

Wrote a Naive Bayes classifier from scratch based on a journal article. Focused on understanding the math behind probabilistic classification and how it can be applied in real-world settings. This project helped me become more confident in the mechanics behind statistical modeling.

Analyzed survey data to explore how family-related stress, support, education, and experience affect job satisfaction. Developed and tested hypotheses, cleaned data, and interpreted results with both statistical and practical meaning in mind. This work combined research design, theory, and technical skill.

This project examines how fetal cells remain in a mother’s body long after pregnancy and how they may influence long-term health. I reviewed existing studies on microchimerism, focusing on links to autoimmune conditions, maternal well-being, and child development. The work reflects my interest in the biological and emotional complexity of maternal health.