Online Shoppers Purchasing Intention


Online Shoppers Purchasing Intention

Using external ML framework on BangDB to train the model

Problem statement

The main objective revolved around the identification of key metrics which contributes the most towards predicting a shopper's behavior and to suggest prioritized critical recommendations and performance improvements on the same. Revenue is the attribute of interest which identifies if a purchase was made or not.
Here is the problem statement in detail

Help doc

Download and follow the help doc to complete the task



Build a prediction model that will classify if a customer will end up not shopping.

Data description

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. The dataset consists of 10 numerical and 8 categorical attributes.The 'Revenue' attribute can be used as the class label.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.

"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. 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.


This is a classification problem and we are going to solve this problem by using the Custom External algorithm “XGBoost” that is by uploading external python code.


We can train model using algorithm which are not in built by uploading training and prediction python code it has to follow some basic protocol