🤖 ML Project
Personalized Health Plan Recommendation Engine
Collaborative filtering engine for personalized plan suggestions.
Project Overview
Internal Project
Machine Learning / Healthcare
3 months (2024)
ML Engineer
Built a recommendation system that suggests optimal health plans based on member profiles, behavior patterns, and similar user preferences.
The Challenge
Members struggled to find the right health plans:
Too many plan options causing decision paralysis
Generic recommendations not personalized
High plan switch rate due to poor matches
No data-driven plan optimization
Manual recommendation process
They needed intelligent, personalized recommendations.
My Solution
I built a hybrid recommendation system:
Collaborative Filtering
Matrix factorization to find similar member preferences.
Content-Based Filtering
Plan feature matching based on member profiles.
Behavior Clustering
K-means clustering to group similar members.
Real-Time Scoring
Redis-cached recommendations with fast retrieval.
Key Features
Personalized
Recommendations tailored to each member.
Hybrid Model
Combines collaborative and content-based filtering.
Real-Time
Instant recommendations with caching.
A/B Testing
Built-in experimentation framework.
Tech Stack
ML
Backend
Data
Infrastructure
Screenshots
Recommendations UI
Performance Analytics
Model Architecture
Results & Impact
40%
Higher
Conversion
25%
Less
Plan Switches
<50ms
Latency
Per Request
85%
Precision
@10
Key Achievements
Increased plan conversion by 40%
Reduced plan switch rate by 25%
Sub-50ms recommendation latency
85% precision@10 in offline evaluation
Interested in Something Similar?
I help businesses build robust backend systems, membership platforms, and automation tools.
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