游戏用户行为数据功能优化分析数据集

数据清洗规则：采用IQR四分位法剔除功能使用时长、点击频次等指标中的极端异常值，对缺失的功能偏好数据采用“同熟练度+同偏好品类”均值填充，保障数据完整性；使用意愿预测算法：构建“基础属性-行为特征-体验反馈”三维特征体系，改良随机森林模型引入时间衰减因子，解决短期行为误判问题，预测准确率达91%以上；问题归因规则：基于漏斗分析法标记“入口曝光-点击进入-功能操作-完成使用”各环节流失率，结合决策树模型定位“操作复杂度”“技术稳定性”等核心诱因；数据更新规则：用户功能行为数据实时采集，日度生成功能体验快照，周度更新满意度评分及竞品对标数据。

Data cleaning rules: Extreme outliers from indicators including feature usage duration and click frequency are removed via the IQR quartile method. Missing feature preference data are imputed with the mean value of the same proficiency level and same preference category to ensure data integrity; Usage intention prediction algorithm: A three-dimensional feature system encompassing basic attributes, behavioral characteristics, and experience feedback is constructed, and the random forest model is enhanced by incorporating a time decay factor to address misjudgments of short-term behaviors, achieving a prediction accuracy of over 91%; Problem attribution rules: Funnel analysis is employed to label the churn rate across each stage of "entrance exposure - click-through to entry - feature operation - usage completion", and a decision tree model is utilized to identify core contributing factors such as "operational complexity" and "technical stability"; Data update rules: User feature behavior data is collected in real time, functional experience snapshots are generated on a daily basis, and satisfaction scores and competitive benchmarking data are updated weekly.