地球资源数据云——数据资源详情
顾客购物行为分析
该数据集《Customer Shopping Behaviour Analysis》主要用于监督学习任务，数据形态以表格为主，应用场景偏向环保分类。 题目说明：Exploring purchasing patterns, demographics, and payment preferences 任务类型：表格监督学习。 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：shopping_behavior_updated (1).csv。 Description A thorough understanding of consumer purchasing behavior in a retail environment is offered by this dataset. It gathers useful insights into how consumers shop, spend, and make decisions by combining demographic data with purchase information. Variables including client age and gender, items purchased, purchase amount, payment methods, use of discounts, and frequency of shopping are all included in the dataset. Patterns in consumer preferences, spending patterns, and the influence of promotions on purchasing behavior can be found by examining these characteristics. For projects including retail analytics, customer segmentation, and corporate decision - making, this dataset is perfect for exploratory data analysis (EDA), data visualization, and data science. It is particularly helpful for analysts who want to practice generating insights and using data to tell stories, as well as for novices who want practical experience with real - world consumer data.
摘要概览
该数据集《Customer Shopping Behaviour Analysis》主要用于监督学习任务，数据形态以表格为主，应用场景偏向环保分类。 题目说明：Exploring purchasing patterns, demographics, and payment preferences
任务类型：表格监督学习。
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：shopping_behavior_updated (1).csv。
Description
A thorough understanding of consumer purchasing behavior in a retail environment is offered by this dataset. It gathers useful insights into how consumers shop, spend, and make decisions by combining demographic data with purchase information.
Variables including client age and gender, items purchased, purchase amount, payment methods, use of discounts, and frequency of shopping are all included in the dataset. Patterns in consumer preferences, spending patterns, and the influence of promotions on purchasing behavior can be found by examining these characteristics.
For projects including retail analytics, customer segmentation, and corporate decision - making, this dataset is perfect for exploratory data analysis (EDA), data visualization, and data science.
It is particularly helpful for analysts who want to practice generating insights and using data to tell stories, as well as for novices who want practical experience with real - world consumer data.