A Multidimensional Dataset of Student Gaming Behavior and Academic Correlates in Bangladesh

This dataset comprises anonymized survey responses from 1,061 university students in Bangladesh, collected between March and April 2023. The igd_responses_raw file contains all 1,061 original entries, while the igd_data file includes a cleaned subset of 989 samples. The train.csv file contains 791 samples, while the test.csv file includes 198 samples. It captures: Gaming Habits: Platform preference (Mobile/PC), average daily gaming time, age of gaming initiation, stress relief usage, and emotional responses to gaming. Academic Metrics: Current CGPA, Higher Secondary results, morning class attendance. Demographics & Lifestyle: Age, gender, sleep duration, newspaper reading habits, eyewear usage, and social/family time. Emotional/Physical Responses: Feelings when unable to play, fatigue levels. Collection Methodology: Survey Design: Validated by psychology and gaming experts. Recruitment: Convenience sampling via email/social media across nine Bangladeshi universities. Processing: Cleaning: Missing values imputed (mean for numerical, mode for categorical), outliers capped at the 95th percentile. Transformation: Normalization (min-max), standardization (z-score), and encoding (label/one-hot) for machine learning compatibility. Usage: Applications: Study gaming-academic correlations, mental health impacts, or cultural trends in the Global South. Limitations: Self-reported data; excludes validated IGD psychometric scales.