· Built Multinomial Logistic Regression models on employee data to predict retirement age and their annuity preferences using Python

· Applied univariate feature selection on employee data to utilize influential characteristics in the model and achieved an A/E ratio of nearly 100%

· Facilitated company with forecasting disbursements, making pricing assumptions and cost-savings

· Generated business insights by creating interactive visualizations and dashboard via Tableau