游戏用户活跃度提升分析数据集合

数据清洗规则：采用IQR四分位法剔除在线时长、登录频次等指标中的极端异常值，对缺失的任务参与数据采用“同玩法偏好+同注册时长”均值填充，保障数据完整性；活跃度预测算法：构建“基础属性-行为特征-外部因素”三维特征体系，改良XGBoost模型引入时间衰减系数，解决短期行为干扰问题，预测准确率达90%以上；低活跃分层规则：基于“登录间隔、互动降幅、玩法参与度”3类核心指标，通过K-Means模型将低活跃用户划分为“体验流失型”“社交缺失型”等4类，精准匹配优化方向；数据更新规则：用户行为数据实时采集，日度生成活跃度快照，周度更新分层用户画像及影响因素关联表。

Data cleaning rules: The Interquartile Range (IQR) method is adopted to eliminate extreme outliers from indicators such as online duration and login frequency. For missing task participation data, mean imputation based on 'same game preference and same registration duration' is applied to ensure data integrity; Activity prediction algorithm: A three-dimensional feature system covering basic attributes, behavioral characteristics and external factors is constructed. The modified XGBoost model with introduced time decay coefficient is used to mitigate the interference of short-term behaviors, achieving a prediction accuracy of over 90%; Low-activity user stratification rules: Based on three core indicators including 'login interval, interaction decline rate and game participation degree', the K-Means model is used to divide low-activity users into four categories such as 'experience loss type' and 'social deficiency type', so as to accurately match the corresponding optimization directions; Data update rules: User behavior data is collected in real time. Daily activity snapshots are generated, and stratified user profiles and correlation tables of influencing factors are updated weekly.

搜集汇总

背景与挑战

背景概述

该数据集名为'游戏用户活跃度提升分析数据集合'，规模为2G，每日更新，由企业自行产生，主要用于游戏用户活跃度提升分析，包括定位活跃瓶颈、分层运营和预警干预等场景。数据集采用XGBoost等算法进行预测和分层，准确率达90%以上，但当前无具体数据结构详情。