Shopping

Write an AI to predict whether online shopping customers will complete a purchase.

$ python shopping.py shopping.csv
Correct: 4088
Incorrect: 844
True Positive Rate: 41.02%
True Negative Rate: 90.55%

Background

When users are shopping online, not all will end up purchasing something. Most visitors to an online shopping website, in fact, likely don’t end up going through with a purchase during that web browsing session. It might be useful, though, for a shopping website to be able to predict whether a user intends to make a purchase or not: perhaps displaying different content to the user, like showing the user a discount offer if the website believes the user isn’t planning to complete the purchase. How could a website determine a user’s purchasing intent? That’s where machine learning will come in.

Your task in this problem is to build a nearest-neighbor classifier to solve this problem. Given information about a user — how many pages they’ve visited, whether they’re shopping on a weekend, what web browser they’re using, etc. — your classifier will predict whether or not the user will make a purchase. Your classifier won’t be perfectly accurate — perfectly modeling human behavior is a task well beyond the scope of this class — but it should be better than guessing randomly. To train your classifier, we’ll provide you with some data from a shopping website from about 12,000 users sessions.

How do we measure the accuracy of a system like this? If we have a testing data set, we could run our classifier on the data, and compute what proportion of the time we correctly classify the user’s intent. This would give us a single accuracy percentage. But that number might be a little misleading. Imagine, for example, if about 15% of all users end up going through with a purchase. A classifier that always predicted that the user would not go through with a purchase, then, we would measure as being 85% accurate: the only users it classifies incorrectly are the 15% of users who do go through with a purchase. And while 85% accuracy sounds pretty good, that doesn’t seem like a very useful classifier.

Instead, we’ll measure two values: sensitivity (also known as the “true positive rate”) and specificity (also known as the “true negative rate”). Sensitivity refers to the proportion of positive examples that were correctly identified: in other words, the proportion of users who did go through with a purchase who were correctly identified. Specificity refers to the proportion of negative examples that were correctly identified: in this case, the proportion of users who did not go through with a purchase who were correctly identified. So our “always guess no” classifier from before would have perfect specificity (1.0) but no sensitivity (0.0). Our goal is to build a classifier that performs reasonably on both metrics.

Getting Started

Understanding

First, open up shopping.csv, the data set provided to you for this project. You can open it in a text editor, but you may find it easier to understand visually in a spreadsheet application like Microsoft Excel, Apple Numbers, or Google Sheets.

There are about 12,000 user sessions represented in this spreadsheet: represented as one row for each user session. The first six columns measure the different types of pages users have visited in the session: the Administrative, Informational, and ProductRelated columns measure how many of those types of pages the user visited, and their corresponding _Duration columns measure how much time the user spent on any of those pages. The BounceRates, ExitRates, and PageValues columns measure information from Google Analytics about the page the user visited. SpecialDay is a value that measures how closer the date of the user’s session is to a special day (like Valentine’s Day or Mother’s Day). Month is an abbreviation of the month the user visited. OperatingSystems, Browser, Region, and TrafficType are all integers describing information about the user themself. VisitorType will take on the value Returning_Visitor for returning visitors and some other string value for non-returning visitors. Weekend is TRUE or FALSE depending on whether or not the user is visiting on a weekend.

Perhaps the most important column, though, is the last one: the Revenue column. This is the column that indicates whether the user ultimately made a purchase or not: TRUE if they did, FALSE if they didn’t. This is the column that we’d like to learn to predict (the “label”), based on the values for all of the other columns (the “evidence”).

Next, take a look at shopping.py. The main function loads data from a CSV spreadsheet by calling the load_data function and splits the data into a training and testing set. The train_model function is then called to train a machine learning model on the training data. Then, the model is used to make predictions on the testing data set. Finally, the evaluate function determines the sensitivity and specificity of the model, before the results are ultimately printed to the terminal.

The functions load_data, train_model, and evaluate are left blank. That’s where you come in!

Specification

Complete the implementation of load_data, train_model, and evaluate in shopping.py.

The load_data function should accept a CSV filename as its argument, open that file, and return a tuple (evidence, labels). evidence should be a list of all of the evidence for each of the data points, and labels should be a list of all of the labels for each data point.

  • Since you’ll have one piece of evidence and one label for each row of the spreadsheet, the length of the evidence list and the length of the labels list should ultimately be equal to the number of rows in the CSV spreadsheet (excluding the header row). The lists should be ordered according to the order the users appear in the spreadsheet. That is to say, evidence[0] should be the evidence for the first user, and labels[0] should be the label for the first user.
  • Each element in the evidence list should itself be a list. The list should be of length 17: the number of columns in the spreadsheet excluding the final column (the label column).
  • The values in each evidence list should be in the same order as the columns that appear in the evidence spreadsheet. You may assume that the order of columns in shopping.csv will always be presented in that order.
  • Note that, to build a nearest-neighbor classifier, all of our data needs to be numeric. Be sure that your values have the following types:
    • Administrative, Informational, ProductRelated, Month, OperatingSystems, Browser, Region, TrafficType, VisitorType, and Weekend should all be of type int
    • Administrative_Duration, Informational_Duration, ProductRelated_Duration, BounceRates, ExitRates, PageValues, and SpecialDay should all be of type float.
    • Month should be 0 for January, 1 for February, 2 for March, etc. up to 11 for December.
    • VisitorType should be 1 for returning visitors and 0 for non-returning visitors.
    • Weekend should be 1 if the user visited on a weekend and 0 otherwise.
  • Each value of labels should either be the integer 1, if the user did go through with a purchase, or 0 otherwise.
  • For example, the value of the first evidence list should be [0, 0.0, 0, 0.0, 1, 0.0, 0.2, 0.2, 0.0, 0.0, 1, 1, 1, 1, 1, 1, 0] and the value of the first label should be 0.

The train_model function should accept a list of evidence and a list of labels, and return a scikit-learn nearest-neighbor classifier (a k-nearest-neighbor classifier where k = 1) fitted on that training data.

  • Notice that we’ve already imported for you from sklearn.neighbors import KNeighborsClassifier. You’ll want to use a KNeighborsClassifier in this function.

The evaluate function should accept a list of labels (the true labels for the users in the testing set) and a list of predictions (the labels predicted by your classifier), and return two floating-point values (sensitivity, specificity).

  • sensitivity should be a floating-point value from 0 to 1 representing the “true positive rate”: the proportion of actual positive labels that were accurately identified.
  • specificity should be a floating-point value from 0 to 1 representing the “true negative rate”: the proportion of actual negative labels that were accurately identified.
  • You may assume each label will be 1 for positive results (users who did go through with a purchase) or 0 for negative results (users who did not go through with a purchase).

You should not modify anything else in shopping.py other than the functions the specification calls for you to implement, though you may write additional functions and/or import other Python standard library modules. You may also import numpy or pandas or anything from scikit-learn, if familiar with them, but you should not use any other third-party Python modules. You should not modify shopping.csv.

Hints

  • For information and examples about how to load data from a CSV file, see Python’s CSV documentation.

How to Submit

If you don’t already have it installed, install submit50 by running pip3 install submit50. Then, execute the below, logging in with your GitHub username and password when prompted. For security, you’ll see asterisks (*) instead of the actual characters in your password.

submit50 ai50/problems/2020/spring/shopping

Acknowledgements

Data set provided by Sakar, C.O., Polat, S.O., Katircioglu, M. et al. Neural Comput & Applic (2018)