Predictive Modeling on Admissions

Context: Academic research at IIM Indore analyzing student admission data to predict enrollment likelihood.

Challenge: Needed to identify key features influencing enrollment decisions and build a reliable predictive model to support admissions planning and strategy.

Solution: Built a logistic regression model on student admission data that:

• Identified the most influential features driving enrollment decisions
• Provided interpretable odds ratios for each predictor
• Enabled the admissions team to forecast enrollment likelihood with confidence

Outcome: Delivered a robust, interpretable model that helped the institution prioritize outreach and optimize admissions strategy. The feature analysis provided actionable insights on what factors most influence student enrollment decisions.