PORTFOLIO
I create results-driven predictive models across people analytics, healthcare, finance, and even March Madness, delivering data-driven insights and measurable outcomes.
Pay Equity Analysis
Developed a comprehensive pay equity analysis model using Python and Tableau, identifying and visualizing compensation disparities across various employee groups and demographics. This project delivered actionable insights, resulting in improved pay transparency, reduced compliance risk, and strengthened trust in equitable compensation practices.
Employee Turnover Prediction
Leveraging Python and Tableau, I built an employee turnover prediction model that identified key attrition drivers and accurately predicted at-risk employees. The insights generated from this model facilitated the implementation of targeted retention strategies, significantly improving workforce retention and reducing turnover costs.
Heart Disease Prediction
Utilizing Python, I developed a robust heart disease prediction model capable of accurately assessing cardiovascular risk based on a range of health indicators. This project demonstrated my expertise in applying predictive modeling techniques within the healthcare sector and yielded meaningful insights for risk assessment and potentially preventative care.
March Madness (Men’s and Women’s)
Designed and implemented a predictive model using Python to forecast NCAA March Madness tournament outcomes using historical data. The model's improved prediction accuracy showcased my proficiency in data manipulation, predictive modeling, and feature engineering.