Data Scientist with a Master's in Applied Data Science and a strong foundation in Python, SQL, and machine learning. Experienced in analyzing complex datasets, identifying trends, and delivering actionable insights to support strategic decision-making. Proven ability to build data-driven solutions that drive business impact.
๐ Data Science Projects ๐งช Predicting Metabolic Syndrome Using NHANES Capstone Project โ University of San Diego Developed machine learning models (Logistic Regression, Random Forest, XGBoost) to predict metabolic syndrome based on behavioral and clinical health data Used SMOTE for class balancing, applied standardization, and evaluated with 5-fold cross-validation Achieved 685% test accuracy; Random Forest model offered best performance and insight into medication impact Applied SHAP for interpretability and feature importance analysis Delivered final project report and presentation with visual dashboards and findings ๐๏ธ Retail Sales Forecasting โ Walmart Time Series Project Forecasted department-level sales using Ridge Regression, ARIMA, and SARIMA models Included external regressors such as unemployment and temperature to boost model performance Compared model RMSE scores and used visualization tools to explain trends and seasonality