• Oversaw daily operations across multiple family-owned restaurant locations to ensure efficient management and smooth coordination across all locations.• Implemented efficient inventory tracking systems reducing waste and optimizing stock levels to meet customer demand across all locations• Coordinated off-site inventory storage by transporting large quantities of stock to storage units and retrieving supplies

• Developed a machine learning model to predict stroke history using a dataset of 5,110 individuals with categorical and numerical features (e.g., age, BMI, hypertension)• Addressed challenges including class imbalance, feature skewness, and underrepresented risks factors by applying techniques using Yeo-Johnson transformation, SMOTE for oversampling, and feature importance analysis• Evaluated various models (Logistic Regression, KNN, SVM, Decision Tree) using repeated stratified cross-validation and determined Logistic Regression is the most effective model

• Conducted comprehensive data analysis on global CO2 emissions and sea level rise using datasets from Our World in Data and Climate.gov• Applied linear regression and correlation analysis to assess the relationship between greenhouse gas emissions and rising sea levels, achieving a correlation coefficient of 0.97• Performed hypothesis testing (two-sided t-test, p-value < 0.05) to reject the null hypothesis and confirm the significant relationship between CO2 emissions and sea level rise• Explored future analysis opportunities with time series and machine learning models to capture non-linear relationships and predict long-term trends