P7: Selling Laptops: Smart Marketing Corrections/Clarifications
You are the owner of a retail website. You’re planning on running a promotion on a laptop and you want to send emails out about it. However, you only want to send it to people that may be interested in it so as not to annoy people that aren’t interested. You’re looking to use your data about who clicked on similar emails in 2020 to help you predict which users may be interested in the promotion.
You can decide what features to consider and how to create your classifier. Your grade will correspond to the accuracy of your predictions. 50% accuracy and below will give a grade of 0%, whereas accuracy of 75% and above will give a grade of 100%; any accuracy between 50 and 75 will be rescaled to a 0-100% grade. Some models can get better than 90% accuracy, so we encourage you to keep improving your model beyond what is necessary for full credit if you have time.
You should create your classifier in a main.py file, which will be the only thing you’ll submit.
You can collaborate with your team on the entire project (no individual part!). As usual, you cannot work
with anybody outside of your team and can only get help from 320 staff.
We have generated 3 datasets:
3. test2(secret)
You’ll fit your classifier to the training data, then estimate your accuracy using the test1 data. We’ll compute your final grade based on test2 data, which is similar but not identical to test1 data.
Each of the 3 datasets consist of 3 files (so there are 9 files total, of which we give you 6):
1. ???_users.csv : information about each user
2. ???_logs.csv : details about webpages visited by each user
3. ???_y.csv : y=1 means they clicked the email; y=0 means they did not
Here is some data in train_users.csv :
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Here is the corresponding data from train_y.csv :
After matching the IDs and considering the y column, we can see Adriana (user_id 0) did not click the ad (y=False).
Here is some data in train_logs.csv :
Each row represents a visit to a webpage on the site. Each row contains the date, the id of the user who visited the page, the url that they visited, and the number of minutes spent on the page. We see Adriana visited 4 pages on the website for varying amounts of time (including one for laptop.html).
User browsing history has the potential to be a good indicator for user clicks, but to make use of it with a machine learning model, you’ll need to find ways to compute summary stats per user based on multiple page visits (or sometimes no page visits).
UserPredictor class
The goal of this project is to build a classifier that, given user and log data, can predict whether those users will be interested in our product. There are a number of ways that you can go about this and a number of ways that you can use the data (or not use portions of the data); the freedom is yours.
You’ll write your classifier as a class called UserPredictor in your main.py file. We require two methods (feel free to add more as useful): fit and predict .
We recommend reading tester.py to understand how we expect your class to work. We intentionally made tester.py very simple this time — only 80 lines of code!
Method 1: fit
Somebody using your class (and the tester) will call fit something like this:
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from main import UserPredictor
model = UserPredictor()
train_users = pd.read_csv(“data/train_users.csv”)
train_logs = pd.read_csv(“data/train_logs.csv”)
train_y = pd.read_csv(“data/train_y.csv”)
model.fit(train_users, train_logs, train_y)
We don’t have any requirements on what fit does (no need to output or return anything), but you’ll probably want to fit an underlying sklearn model (for example, a LogisticRegression ) to the data for purposes of later prediction.
Method 2: predict
Somebody using your class (and the tester) will call predict something like this:
predict should return a numpy array. If y_pred[n] is True, that means you predict the user at index n will click the email; otherwise, you’re predicting they will not.
Hints and Suggestions
you can implement a custom classifier from scratch if you like, but we imagine most of you will have an sklearn pipeline as an attribute in your class, with your fit method fitting the pipeline and your
predict method using the pipeline for predictions
start simple, then add complexity. For example, you only need to get >50% accuracy to get some points, and a simple LogisticRegression based only on past_purchase_amt should have an accuracy score around 60%.
as you consider features for your model, it’s easier to start with just the ???_users.csv file (ignoring the logs data), as each user row corresponds to exactly one outcome/prediction. We were able to train a simple model just based on the user data that achieved an accuracy of 72%, completely ignoring the log data.
to reach a grade of 100%, you’ll probably need to use the logs data that describes how long each user spent on various pages. You can decide how to use the logs data, but the simplest way might be to write a method that adds feature columns to the user DataFrame, based on per-user stats of your choosing calculated from the logs data. For example, you could add a column describing how many total minutes each user spent on the site. Using a separate method to add such information can help you avoid repeat work between your fit and predict methods (you’ll presumably need the same additional columns for both training and predicting).
some users may not have any rows in logs if they didn’t visit the website at all. Consider how you’ll deal with this.
test_users = pd.read_csv(“data/test1_users.csv”)
test_logs = pd.read_csv(“data/test1_logs.csv”)
y_pred = model.predict(test_users, test_logs)
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you’re welcome to learn new classification models in sklearn and use them for this project, but we imagine most of you will build pipelines based on LogisticRegression, the classifier we learned in class. Remember that in many scenarios LogisticRegression does best in a pipeline where there is a preceding StandardScaler. LogisticRegression doesn’t like large/small numbers or non-centered data, so StandardScaler transforms the data to work better with LogisticRegression.
running python3 tester.py defaults to python3 tester.py main test1 . This will give you a general estimate of your final grade, but we’ll run python3 tester.py main test2 to actually compute that; you cannot run on test2, as we are not releasing test2_users.csv , etc. Based on noise, your grade may be better or worse for test1 vs. test2. Here are some tips to avoid surprises:
don’t read any CSVs directly in your main.py ; just use the DataFrames passed to your methods to avoid inadvertantly hardcoding paths
accuracy of 75 corresponds to a grade of 100%. We suggest you work to overshoot 75% by a bit in case you do slightly worse on test2 than test1
some models are more vulnerable than others to noise in accuracy scores for different train/test datasets. You may want to do cross validation in your fit method and prints some stats. How will depend on other design choices you make, but your instructor’s model has a debug print something like the following in fit (a high standard deviation would indicate I’m more likely to be unlucky with getting a very different score on test2 than test1):
if you plan to work with your team members (not required, as usual), consider how you may want to divide the work. One possibility: each team member could try a different model; after comparing them, all team members could submit the one model that scores best. Alternatively, each team member could be responsible for computing different per-user stats to use as features for training/predicting. Of course, avoid getting in a situation where you are dependent on any one person to complete the project; this project should be a reasonable amount of work, even for somebody working individually.
1 scores=cross_val_score(model,train_users[self.xcols],train_y[“y”]) 2 print(f”AVG:{scores.mean()},STD:{scores.std()}\n”)
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