Futuristic fitness intelligence

Immersive frontend shell, unchanged Flask prediction core.

Realtime scoring Story-driven analytics Progress memory
Machine learning engine
Evidence layer Credibility story Same analytics endpoint

Model evidence presented like a premium intelligence report.

Dataset scale, model comparison, feature importance, distributions, and clustering stay tied to the same analytics endpoint while gaining a clearer narrative structure.

Analytics feed
Feature story
Model credibility
Evidence depthTrusted
Metric graphMulti-layer
Dataset scope Model context before chart overload

Key record and performance stats now lead the page as a premium summary band.

Feature lens Importance, distribution, and correlation in one visual system

Charts retain the same data source while gaining stronger hierarchy and clearer contrast.

Side-by-side confidence, accuracy, and performance spread.

Linear Regression
R2 -
RMSE -
RandomForest
R2 -
RMSE -
Logistic Regression
Accuracy -
CV Mean -
Analysis flow The page moves from trust signals to deep evidence

Comparison cards, ranked features, distributions, and cluster summaries now read like an intelligence deck instead of a loose chart gallery.

Endpoint safety No analytics contract changes

Everything still renders from `/api/analytics` with the same fields and chart logic.

Feature Importance

R2 Comparison

Fitness Score Distribution

Calories Distribution

A readable map of feature relationships.

Lifestyle Clusters

Fitness Level Distribution