adeputr4/mental-health-gaming-dataset

这是一个合成数据集，包含游戏行为与心理健康指标，用于通过模型链预测抑郁评分（0-10）。数据集大小为968,287行×39列，来源于Kaggle的合成数据，目标变量为depression_score（连续值，范围0-10）。关键列包括daily_gaming_hours（日均游戏小时数）、competitive_rank（竞争排名百分位）、addiction_level（游戏成瘾评分）和depression_score（抑郁评分）。该数据集专为教育目的设计，不涉及真实个体数据，应用于模型链架构：首先使用输入器从游戏小时数和排名预测成瘾评分，然后将缩放特征输入预测器（如线性回归或随机森林）以输出抑郁评分预测。Synthetic dataset of gaming behavior and mental health indicators used to predict depression_score (0–10) through model chaining. The dataset contains 968,287 rows and 39 columns, sourced from Kaggle as synthetic data, with the target variable being depression_score (continuous, 0–10). Key columns include daily_gaming_hours (average hours spent gaming per day), competitive_rank (competitive rank percentile), addiction_level (gaming addiction score), and depression_score (depression score). It is intended for educational purposes and does not represent real individuals, used in a model chaining architecture where an imputer predicts addiction_level from gaming hours and rank, and scaled features are fed to a predictor (e.g., Linear Regression or Random Forest) to output depression_score predictions.