mldsqc/alicerun

Ingests multimodal data from ActivityWatch, Telegram, Toggl, Microsoft ToDo, Miband4, and Android devices into a PostgreSQL backend, then applies time-series forecasting and KNN clustering to correlate digital activity patterns with mood/habit logs across eight life domains. Serves predictions and metrics via Streamlit/Dash dashboards with a radial chart for visualizing balanced growth across financial, career, emotional, physical, and social dimensions. Operates on cumulative emotion/habit windows rather than instantaneous sensor reads, acknowledging the limitation of establishing causal links between behavioral patterns and task outcomes.

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