地球资源数据云——数据资源详情
游戏时间与学业和工作表现
该数据集《Gaming Hours vs Academic & Work Performance》主要用于多分类任务，数据形态以表格为主。 题目说明：Analyzing the Impact of Gaming Habits on Productivity, Focus, and Performance. 任务类型：表格多分类。 建议流程：先做缺失值/异常值处理与特征编码，再比较逻辑回归、随机森林、XGBoost。 评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。 可用文件：Gaming_Hours_vs_Performance_1000_Rows.csv。 This dataset explores the relationship between daily and weekly gaming habits and their impact on academic or workplace performance. It captures key behavioral factors such as gaming duration, preferred gaming time, sleep hours, stress levels, focus levels, and productivity scores. The data is designed to help analysts, students, and researchers understand how different gaming patterns may positively, negatively, or neutrally influence performance outcomes. It is suitable for exploratory data analysis (EDA), correlation studies, data visualization, and machine learning tasks such as classification and regression.
摘要概览
该数据集《Gaming Hours vs Academic & Work Performance》主要用于多分类任务，数据形态以表格为主。 题目说明：Analyzing the Impact of Gaming Habits on Productivity, Focus, and Performance.
任务类型：表格多分类。
建议流程：先做缺失值/异常值处理与特征编码，再比较逻辑回归、随机森林、XGBoost。
评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。
可用文件：Gaming_Hours_vs_Performance_1000_Rows.csv。
This dataset explores the relationship between daily and weekly gaming habits and their impact on academic or workplace performance. It captures key behavioral factors such as gaming duration, preferred gaming time, sleep hours, stress levels, focus levels, and productivity scores.
The data is designed to help analysts, students, and researchers understand how different gaming patterns may positively, negatively, or neutrally influence performance outcomes. It is suitable for exploratory data analysis (EDA), correlation studies, data visualization, and machine learning tasks such as classification and regression.