Atul Kashyap

Predictive Modeling: Implement advanced techniques, such as XGBoost, Ordered Probit Model, and Logistic Regression, to optimize yield conversion across the entire enrollment funnel for incoming students. Build Predictive Models with a focus on maximizing college net tuition revenue and enhancing enrollment yield for higher education clients. Developed retention models using Random Forest and XGBoost algorithms to accurately predict college students' likelihood of continuing their education, contributing to improved student retention rates. Collaborated effectively with academic advisors to interpret model results and design personalized intervention plans for at-risk students, leading to enhanced student success and support. Market Mix Modeling: Optimize Multi-Million dollar marketing expenditure of clients across healthcare, CPG, and Retail. Specializes in developing Marketing Mix Models, analyzing MROI (Marketing Return on Investment), and evaluating how effective marketing strategies are for each client. Provides clients with insightful results and media optimization solutions using advanced statistical and machine learning algorithms such as Multiple Linear Regression and gradient Descent.