Online Shoppers Purchasing Intention Dataset

The dataset consists of feature vectors belonging to 12,330 sessions. The dataset was formed so that each session would belong to a different user in a 1-year period to avoid any tendency to a specific campaign, special day, user profile, or period. Of the 12,330 sessions in the dataset, 84.5% (10,422) were negative class samples that did not end with shopping, and the rest (1908) were positive class samples ending with shopping.The dataset consists of 10 numerical and 8 categorical attributes. The 'Revenue' attribute can be used as the class label.The dataset contains 18 columns, each representing specific attributes of online shopping behavior:Administrative and Administrative_Duration: Number of pages visited and time spent on administrative pages.Informational and Informational_Duration: Number of pages visited and time spent on informational pages.ProductRelated and ProductRelated_Duration: Number of pages visited and time spent on product-related pages.BounceRates and ExitRates: Metrics indicating user behavior during the session.PageValues: Value of the page based on e-commerce metrics.SpecialDay: Likelihood of shopping based on special days.Month: Month of the session.OperatingSystems, Browser, Region, TrafficType: Technical and geographical attributes.VisitorType: Categorizes users as returning, new, or others.Weekend: Indicates if the session occurred on a weekend.Revenue: Target variable indicating whether a transaction was completed (True or False).The original dataset has been picked up from the UCI Machine Learning Repository, the link to which is as follows :https://archive.ics.uci.edu/dataset/468/online+shoppers+purchasing+intention+datasetAdditional Variable InformationThe dataset consists of 10 numerical and 8 categorical attributes. The 'Revenue' attribute can be used as the class label. "Administrative", "Administrative Duration", "Informational", "Informational Duration", "Product Related" and "Product Related Duration" represent the number of different types of pages visited by the visitor in that session and total time spent in each of these page categories. The values of these features are derived from the URL information of the pages visited by the user and updated in real time when a user takes an action, e.g. moving from one page to another. The "Bounce Rate", "Exit Rate" and "Page Value" features represent the metrics measured by "Google Analytics" for each page in the e-commerce site. The value of "Bounce Rate" feature for a web page refers to the percentage of visitors who enter the site from that page and then leave ("bounce") without triggering any other requests to the analytics server during that session. The value of "Exit Rate" feature for a specific web page is calculated as for all pageviews to the page, the percentage that were the last in the session. The "Page Value" feature represents the average value for a web page that a user visited before completing an e-commerce transaction. The "Special Day" feature indicates the closeness of the site visiting time to a specific special day (e.g. Mother’s Day, Valentine's Day) in which the sessions are more likely to be finalized with transaction. The value of this attribute is determined by considering the dynamics of e-commerce such as the duration between the order date and delivery date. For example, for Valentina’s day, this value takes a nonzero value between February 2 and February 12, zero before and after this date unless it is close to another special day, and its maximum value of 1 on February 8. The dataset also includes operating system, browser, region, traffic type, visitor type as returning or new visitor, a Boolean value indicating whether the date of the visit is weekend, and month of the year.

本数据集包含12330条会话（session）对应的特征向量。为避免模型偏向特定营销活动、特殊日期、用户画像或时段，数据集在构建时确保每条会话均来自1年内的不同用户。
在全部12330条会话中，84.5%（10422条）为负类样本，即未达成购物的会话；剩余1908条为正类样本，即完成购物的会话。
本数据集共包含10个数值型特征与8个分类型特征，其中"Revenue"可作为类别标签。数据集共18列，每列对应在线购物行为的特定属性：
1. Administrative（管理页）与Administrative_Duration（管理页停留时长）：访客在会话期间访问的管理类页面数量及停留总时长。
2. Informational（信息页）与Informational_Duration（信息页停留时长）：访客访问的信息类页面数量及停留总时长。
3. ProductRelated（商品相关页）与ProductRelated_Duration（商品相关页停留时长）：访客访问的商品类页面数量及停留总时长。
4. BounceRates（跳出率）与ExitRates（退出率）：表征会话期间用户行为的指标。
5. PageValues（页面价值）：基于电商指标计算的页面价值。
6. SpecialDay（特殊节日关联度）：基于特殊节日的购物可能性评分。
7. Month（会话月份）：会话发生的月份。
8. OperatingSystems（操作系统）、Browser（浏览器）、Region（地区）、TrafficType（流量类型）：技术与地理属性特征。
9. VisitorType（访客类型）：将访客划分为回访访客、新访客或其他类型。
10. Weekend（是否周末）：标识会话是否发生在周末。
11. Revenue（目标变量）：表征交易是否完成的布尔型标签（True/False）。

本数据集源自UCI机器学习仓库（UCI Machine Learning Repository），数据集链接如下：https://archive.ics.uci.edu/dataset/468/online+shoppers+purchasing+intention+dataset

### 附加变量信息
本数据集共包含10个数值型特征与8个分类型特征，其中"Revenue"可作为类别标签。"Administrative""Administrative Duration""Informational""Informational Duration""Product Related"及"Product Related Duration"分别代表访客在本次会话中访问的不同类型页面数量，以及在各类页面中花费的总时长。这些特征的值源自用户访问页面的URL信息，并会在用户进行页面跳转等操作时实时更新。
"Bounce Rate""Exit Rate"与"Page Value"特征为谷歌分析（Google Analytics）针对电商网站各页面统计得到的指标。其中，页面的"跳出率（Bounce Rate）"指访客从该页面进入网站后，未向分析服务器发起任何其他请求即离开（即"跳出"）的访客占比。页面的"退出率（Exit Rate）"指所有访问该页面的会话中，以该页面作为会话最后一页的占比。"页面价值（Page Values）"代表用户在完成电商交易前访问某页面的平均价值。
"SpecialDay"特征用于标识会话访问时间距离特定特殊节日（如母亲节、情人节）的紧密程度，此类节日期间会话更易达成交易。该特征的值需结合电商场景动态因素计算，例如订单日期与配送日期的间隔时长。以情人节为例，该特征在2月2日至2月12日期间取0到1之间的非零值，在该时段前后若无其他临近特殊节日则取值为0，在2月8日达到最大值1。
此外，数据集还包含操作系统、浏览器、地区、流量类型、访客类型（回访访客或新访客）、标识访问日期是否为周末的布尔值，以及会话发生的月份等特征。

搜集汇总

背景与挑战

背景概述

该数据集包含12,330个独立用户会话，用于预测在线购物意图，其中84.5%的会话未产生购买。数据集包含18个特征（10个数值型和8个分类型），涵盖页面浏览行为、技术指标和时间因素，目标变量为'Revenue'，适用于分类任务如购物行为分析。