用户信息特征分析数据集
收藏资源简介：
1.数据采集：从电子卖场平台采集用户基础信息，包括：账号、性别、年龄、学历、学制、民族、籍贯、登录时间、电话； 2.数据处理：1）按性别、年龄、学历、民族、籍贯五个维度分别统计用户数量及占比，生成“性别分布表”“年龄分层占比表”“学历层次统计表”“民族构成表”“籍贯地域分布表”；2）结合登录时间开展时间序列分析，提取用户每日活跃高峰时段、每周活跃天数、每月活跃频次，形成“用户活跃时段规律数据集”；3）关联学历与学制信息，挖掘用户教育背景特征，挖掘不同学历对应的学制时长分布，同时交叉分析民族、籍贯与性别、年龄的关联规律，最终整合1）的基础属性数据与2）的行为特征数，形成包含“用户基础属性分布数据集”“用户活跃特征数据集”“教育背景与多属性关联数据集”的综合分析数据集； 3.数据应用：用于用户画像构建，辅助精准营销、服务优化等决策，如针对不同学历用户推送差异化内容，依据活跃高峰时段增配客服人员，提升用户体验与运营效率。
1. Data Collection: Basic user information is collected from electronic marketplace platforms, including account, gender, age, educational background, academic system, ethnicity, native place, login time, and phone number; 2. Data Processing: 1) Count the number and proportion of users across five dimensions including gender, age, educational background, ethnicity, and native place, and generate five tables namely "Gender Distribution Table", "Age Stratified Proportion Table", "Educational Level Statistics Table", "Ethnic Composition Table", and "Native Place Regional Distribution Table"; 2) Conduct time series analysis based on login time data, extract the daily active peak hours, weekly active days, and monthly active frequency of users, and establish the "User Active Period Rule Dataset"; 3) Correlate educational background and academic system information to explore the characteristics of users' educational backgrounds, extract the distribution of academic system durations corresponding to different educational levels, and perform cross-analysis on the association rules among ethnicity, native place, gender, and age. Finally, integrate the basic attribute data from step 1) and the behavioral characteristic data from step 2) to form a comprehensive analysis dataset encompassing "User Basic Attribute Distribution Dataset", "User Active Characteristic Dataset", and "Educational Background and Multi-attribute Association Dataset"; 3. Data Application: This dataset is utilized for user portrait construction to support decision-making scenarios such as precise marketing and service optimization. For instance, push differentiated content to users with different educational backgrounds, increase the staffing of customer service personnel based on active peak hours, thereby enhancing user experience and operational efficiency.
