Digital habits → wellbeing risk (binary): calibrated risk scoring + cost-aware thresholding with deployable artifacts (LogReg/RF/XGBoost).
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Updated
Feb 8, 2026 - Jupyter Notebook
Digital habits → wellbeing risk (binary): calibrated risk scoring + cost-aware thresholding with deployable artifacts (LogReg/RF/XGBoost).
Composition-first analytics for daily screen time. Computes the Digital Balance Index (DBI) using normalized entropy, plus dominance, tiers, and a “high-load & skewed” flag. Exports scored rows, segment summaries, daily trends, and figures, and ships a Streamlit dashboard to explore patterns by age group, primary device, and internet type.
The goal of this project is to develop a predictive model that analyzes children's physical activity and fitness data to identify early signs of problematic internet use. Identifying these patterns can help trigger interventions to encourage healthier digital habits.
📊 Analyze children's activity data to predict and address problematic internet use, fostering healthier digital habits through early intervention.
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