Synthetic Caffeine Intake Tracker Dataset

Dataset Features

• caffeine_mg: Total caffeine consumed by an individual per day (in milligrams).
• age: Age of the individual (in years).
• focus_level: Self-reported focus level on a standardised scale (e.g., 1 to 10).
• sleep_quality: Self-reported sleep quality score (e.g., 1 to 10).
• sleep_impacted: Whether caffeine negatively impacted sleep (Yes/No).
• beverage_coffee: Indicates whether coffee was consumed (True/False).
• beverage_energy_drink: Indicates whether energy drinks were consumed (True/False).
• beverage_tea: Indicates whether tea was consumed (True/False).
• time_of_day_morning: Caffeine consumed during morning hours (True/False).
• time_of_day_afternoon: Caffeine consumed during afternoon hours (True/False).
• time_of_day_evening: Caffeine consumed during evening hours (True/False).
• gender_female: Gender indicator for female (True/False).
• gender_male: Gender indicator for male (True/False).

Distribution

Usage

• Health Research: Analyse how caffeine affects focus, sleep, and other lifestyle factors.
• Behavioral Modeling: Predict individual outcomes like sleep disruption or productivity based on caffeine intake patterns.
• Consumer Insights: Understand beverage preferences by demographics and time of day.
• Educational Use: Provide a rich dataset for learning regression analysis, classification, and data visualisation in health and behavioural sciences.

Coverage

License

Who Can Use It

• Health Researchers and Dietitians: To study caffeine intake trends and effects.
• Data Scientists and Analysts: For developing behavioral prediction models and insights.
• Educators and Students: As a realistic dataset for projects in health data science, behavioural analysis, and machine learning.