AmirhosseinHonardoust/AI-Productivity-Tracker

Analyze and predict daily productivity using SQL, machine learning, and psychology. This project combines behavioral data, circadian rhythm analysis, and ElasticNet regression to model focus, stress, and performance, transforming work patterns into actionable insights.

26
/ 100
Experimental

**Technical Summary:** Uses SQLite for psychology-informed feature engineering—deriving metrics like Yerkes–Dodson arousal (stress×performance curve), circadian alignment, and sleep deficit directly via SQL views—then feeds them into ElasticNet regression for interpretable coefficient analysis. The pipeline processes behavioral telemetry (sleep, meetings, context switches, stress scores) through standardized sklearn transformations, producing both predictions and diagnostic visualizations (residual plots, feature importance rankings) that validate the model generalizes without systematic bias across unlabeled candidate days.

No Package No Dependents
Maintenance 6 / 25
Adoption 7 / 25
Maturity 13 / 25
Community 0 / 25

How are scores calculated?

Stars

35

Forks

Language

Python

License

MIT

Last pushed

Oct 24, 2025

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/AmirhosseinHonardoust/AI-Productivity-Tracker"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.