游戏用户行为数据游戏平衡优化数据集

数据清洗规则：采用IQR四分位法剔除对战数据、道具消耗等指标中的极端异常值，对缺失的职业技能使用数据采用“同熟练度+同等级”均值填充，保障数据完整性；职业平衡归因算法：改良随机森林模型，引入“技能交互权重系数”，解决传统单一指标（如胜率）误判平衡问题的缺陷，精准定位失衡技能模块；经济预测规则：构建“资源产出-交易流转-消耗沉淀”三维特征体系，通过LSTM模型捕捉时序变化规律，平衡风险预测准确率达93%以上；数据更新规则：用户行为数据实时采集，日度生成平衡指标快照，周度更新玩家反馈关联表及竞品平衡数据。

Data Cleaning Rules: The Interquartile Range (IQR) method is adopted to eliminate extreme outliers from indicators such as battle data and prop consumption. For missing class skill usage data, mean imputation based on 'same proficiency and same level' is applied to ensure data integrity; Occupation Balance Attribution Algorithm: An improved random forest model is utilized, with the introduction of the 'skill interaction weight coefficient' to address the shortcomings of traditional single-indicator (e.g., win rate) misjudgment of balance issues and accurately pinpoint imbalanced skill modules; Economic Forecasting Rules: A three-dimensional feature system of 'resource output - transaction circulation - consumption precipitation' is constructed. The Long Short-Term Memory (LSTM) model is employed to capture temporal variation patterns, with the balance risk prediction accuracy exceeding 93%; Data Update Rules: User behavior data is collected in real time. Balance indicator snapshots are generated daily, and player feedback association tables as well as competitor balance data are updated weekly.