Advanced machine learning model trained on thousands of profiles. Get accurate, data-driven premium category predictions with confidence scores.
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Our model analyzes multiple risk factors to deliver precise premium category predictions.
Submit personal data — age, occupation, city, income and health metrics for a complete risk profile.
Automatic computation of BMI, age group, lifestyle risk score, and city tier from your inputs.
Random Forest Classifier with 92%+ accuracy delivers your premium category with probability scores.
Get Low, Medium, or High premium category with full confidence breakdown in under 3 seconds.
BMI and smoking status are primary health risk indicators
City tier and job type influence risk exposure and healthcare access
Financial capacity and life stage significantly affect premium brackets
Fill in your details. All fields are required for accurate prediction.
Explore trends, distributions, and risk patterns from our dataset.
Volume trend over the past 12 months
Dataset label distribution
Risk breakdown by occupation type
Population breakdown across age bands
% of high-risk profiles per factor
A production-grade ML system powering insurance premium prediction.
Random Forest Classifier trained with scikit-learn's Pipeline API. Uses OneHotEncoding for categorical features and passthrough for numeric. Achieves 92%+ accuracy on the test split.
FastAPI with Pydantic v2 computed fields for auto-derivation of BMI, age group, lifestyle risk and city tier. Served via Uvicorn ASGI on localhost:8000.
Vanilla HTML/CSS/JS — Three.js particle systems, GSAP animations, Chart.js analytics, glassmorphism UI, real-time BMI computation. Served independently via Live Server.
| Feature | Type | Source | Description |
|---|---|---|---|
| bmi | float | Computed | weight / height² |
| age_group | categorical | Computed | young / adult / middle_aged / senior |
| lifestyle_risk | categorical | Computed | low / medium / high (BMI + smoking) |
| city_tier | integer | Computed | 1 metro / 2 tier-2 / 3 others |
| income_lpa | float | User Input | Annual income in lakhs per annum |
| occupation | categorical | User Input | 7 classes (student, retired, etc.) |
http://localhost:8000/health
System health check
/predict
Returns premium category + confidence scores
/analytics
Aggregated analytics and risk distribution data
{
"age": 32,
"height": 1.72,
"weight": 75,
"occupation": "private_job",
"smoker": false,
"income_lpa": 8.5,
"city": "Bangalore"
}