A machine-learning web project that pairs a classification model trained on structured CSV behavioral data with a guided questionnaire, providing an estimated stress indicator and wellness suggestions.
Developed strictly as an informational wellness self-check tool, combining data science model evaluation in Python with a clean Next.js frontend experience.
StressIQ explores how behavioral patterns—such as sleep regularity, physical activity, screen exposure, and self-reported work pressures—correlate with overall stress indicators. Users answer a focused series of questions to receive an estimated stress-level rating.
The machine learning pipeline began with a tabular CSV dataset of behavioral and lifestyle survey records.
Upon completing the questionnaire, users receive an estimated categorization (e.g. Low, Moderate, or Elevated stress indicators) along with tailored lifestyle suggestions.
StressIQ is explicitly designed as an educational self-check and does not offer medical diagnoses or clinical advice. The interface displays clear informational notices emphasizing that results reflect statistical model estimations, not formal healthcare evaluations.
Scikit-learn
Tabular CSV
FastAPI / Python
Informational
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