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Elizabeth Stein
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Case study

fullstack2025

RepRise

Premium fitness trainer matching platform that intelligently pairs users with their ideal trainer using a multi-factor weighted scoring algorithm (Jaccard similarity, budget constraints, schedule overlap, personality compatibility, and 0–100 confidence scoring). Demonstrates sophisticated algorithm design and thoughtful UX in a focused portfolio piece.

End-to-end solutions from database to deployment

Field notes

Role

Creator

Read order

Evidence first, then constraint → build → proof. Use the chapter rail to jump sections; orbit to adjacent work from the bottom of the case study.

Full-Stack Nebula·✦II-Class Star
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Evidence

Interface & evidence

A real surface area—not a mock. Scroll the story below for constraint, build, and outcome.

Signature viewreprise-tau.vercel.app
RepRise application interface
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reprise-tau.vercel.app

Story arc

How this shipped

End-to-end solutions from database to deploymentThree beats: what pressed against the work, how the stack answered, and what changed once it was live.

I · Constraint

The brief

Matching people with fitness trainers requires weighing incommensurable factors — schedule, budget, personality, goals — in a way that feels trustworthy rather than arbitrary.

II · Build

The craft

Multi-factor weighted scoring with Jaccard similarity for goal overlap, budget constraint logic as a hard filter, schedule conflict detection, and a 0–100 confidence score driven by profile completeness.

III · Proof

What moved after launch

Matching algorithm handles edge cases gracefully. Confidence scoring gives users a clear signal of match quality. Clean UX makes a complex algorithm feel effortless.

Signals

◆Signals & scale

Jaccard
Algorithm
0–100
Score Range
0
Factors

Inventory

Stack & signals

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