Your goal is to create a decision tree which can predict class membership of the “Revenue” variable. Clearly, the better the tree prediction, the happier management will be… and the higher your mark!

INFT216 – Data Science Assignment – Decision Trees: Will an online shopper make a purchase?

This assignment uses a sample of real data from an online store.

In physical retailing, a salesperson can offer a range of customized alternatives to shoppers based on experience or customer behaviour. This has an important influence on the effective use of time, purchase conversion rates, and sales figures.

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 objective is to predict whether a customer will make a purchase in this session. Every time a web page is served to the user, the online store will make a prediction of how likely it is that the customer will purchase. The store can use this probability to customize buying offers, provide discounts, offer purchase advice, etc.

I have loaded this data into an SQL database (to make sure you get some practice using SQL!). The data is located at BRUCEDBA.ONLINEPURCHASES, and you should access it directly from R following the same procedure we used in workshops.

Feature Description –

Notes on Selected Features –

ADMINISTRATIVE, ADMINISTRATIVE_DURATION, INFORMATIONAL, INFORMATIONAL_DURATION, PRODUCT_RELATED, PRODUCT_RELATED_DURATION

i. These features represent the number of different types of pages visited by the visitor in that session and the 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

BOUNCE_RATE, EXIT_RATE, PAGE_VALUE

i. These features represent the metrics measured by “Google Analytics” for each page in the ecommerce 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.

SPECIAL_DAY

i. This 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 ecommerce such as the duration between the order date and delivery date. For example, for Valentine’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 1on February 8.

OPERATINGSYSTEMS, BROWSER, REGION, TRAFFICTYPE, VISITORTYPE

i. Attributes of specific user session.

Task – Your goal is to create a decision tree which can predict class membership of the “Revenue” variable. Clearly, the better the tree prediction, the happier management will be… and the higher your mark!

Deliverables: Your final deliverables will be 2 PDF files, both produced by the same .Rmd script (with different code chunk options).

I – All code and results shown (like you would share with a colleague on the Data Science team)

II – Only show those things necessary to help support management decision making (this is the one you send to management!)

Please submit only these two PDF’s through iLearn. Please don’t submit R code or your model.

Helpful hints –

i. Read the document on how to create a management report.

ii. It’s a good idea (at least to start off with) to use the template – customize it as much as you wish.