Predictive Analytics
Dr Bhatia MDS Predictor
A data-driven predictor using historical admission data to forecast MDS counselling outcomes.
Overview
Problem
MDS aspirants going through counselling had no reliable, data-backed way to estimate their admission chances at a given rank and category — decisions were being made on guesswork and word-of-mouth.
Solution
A predictor tool that models historical counselling round data — rank, category, and college outcomes — to give aspirants a data-backed estimate of where they're likely to be placed.
Architecture
Client
Next.js form for rank/category input and results visualization.
API
Prediction endpoint querying historical outcome data via Prisma.
Data
MongoDB storing structured historical counselling round outcomes.
Analytics
Ranking/probability model built over historical rank-to-outcome data.
- Prisma provides a typed data layer over the historical counselling dataset, making the prediction queries straightforward to reason about and extend as more rounds of data become available.
- Predictions are computed from actual historical outcomes rather than a black-box model, so results can be explained in terms of comparable past ranks — important for a decision aspirants are trusting.
Feature Breakdown
- Rank and category-based prediction of likely counselling outcomes
- Historical outcome data as the basis for every prediction, not a black box
- Clear results view for a non-technical audience under time pressure during counselling
Implementation Detail
async function predictOutcome(rank: number, category: Category) {
const comparable = await prisma.counsellingRound.findMany({
where: { category, rank: { gte: rank - 500, lte: rank + 500 } },
});
return summarizeOutcomes(comparable);
}Challenges & Solutions
Predictions needed to be trustworthy and explainable to users making real admission decisions, not just statistically plausible.
Grounded predictions in comparable historical outcomes rather than an opaque model, so a result can be traced back to similar past ranks and categories.
Historical counselling data was inconsistent across sources and rounds, and needed to be normalized before it was usable.
Built a structured schema via Prisma that normalizes rank, category, and outcome fields at ingestion, so the prediction logic operates on clean, consistent data.
Results
- Aspirants get a rank-and-category estimate grounded in real counselling-round outcomes, instead of relying on forum threads and word-of-mouth during a narrow decision window.
- Every prediction can be traced back to the comparable historical ranks behind it, rather than presented as an unexplained number.
- Data that was previously inconsistent across sources and rounds is now normalized into one schema, making it usable for prediction at all.
Lessons Learned
- Resisting the pull toward a more sophisticated model was the right call here — for a decision this consequential, aspirants trusted 'here's what happened to similar ranks before' more than a score they couldn't interrogate.
Gallery
Screenshots to be added.