Predict click-through rates on display ads
Display advertising is a billion dollar effort and one of the central uses of machine learning on the Internet. However, its data and methods are usually kept under lock and key. For a research competition on Kaggle, CriteoLabs is sharing a week’s worth of data for you to develop models predicting ad click-through rate (CTR). Given a user and the page he is visiting, what is the probability that he will click on a given ad?
The goal of this project is to design ML algorithms for CTR estimation. The training/testing data used are the traffic logs from Criteo that include various undisclosed features along with the click labels.
File descriptions:
train.csv - The training set consists of a portion of Criteo's traffic over a period of 7 days. Each row corresponds to a display ad served by Criteo. Positive (clicked) and negatives (non-clicked) examples have both been subsampled at different rates in order to reduce the dataset size. The examples are chronologically ordered.
test.csv - The test set is computed in the same way as the training set but for events on the day following the training period.
Data fields:
Label - Target variable that indicates if an ad was clicked (1) or not (0).
I1-I13 - A total of 13 columns of integer features (mostly count features).
C1-C26 - A total of 26 columns of categorical features. The values of these features have been hashed onto 32 bits for anonymization purposes.
The semantic of the features is undisclosed. When a value is missing, the field is empty.