Online Shoppers Purchasing Intention Dataset
About
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 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.
Subject Area
Business
Instances
12,330
Features
18
Data Types
Multivariate
Tasks
Classification, Clustering
Feature Types
Integer, Continuous
Features
| Name | Role | Type | Units | Missing Values |
|---|---|---|---|---|
| Administrative | Feature | Integer | - | No |
| Administrative_Duration | Feature | Integer | - | No |
| Informational | Feature | Integer | - | No |
| Informational_Duration | Feature | Integer | - | No |
| ProductRelated | Feature | Integer | - | No |
| ProductRelated_Duration | Feature | Continuous | - | No |
| BounceRates | Feature | Continuous | - | No |
| ExitRates | Feature | Continuous | - | No |
| PageValues | Feature | Integer | - | No |
| SpecialDay | Feature | Integer | - | No |
| Month | Feature | Categorical | - | No |
| OperatingSystems | Feature | Integer | - | No |
| Browser | Feature | Integer | - | No |
| Region | Feature | Integer | - | No |
| TrafficType | Feature | Integer | - | No |
| VisitorType | Feature | Categorical | - | No |
| Weekend | Feature | Binary | - | No |
| Revenue | Target | Binary | - | No |
Introductory Paper
Real-time prediction of online shoppers’ purchasing intention using multilayer perceptron and LSTM recurrent neural networks
C. O. Sakar, S. Polat, Mete Katircioglu, Yomi Kastro. 2019.
Neural computing & applications (Print)