• 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.

There are 45840617 training instances.

• 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.

We are not disclosing the semantics of the features, but I can tell you that they fall in the following categories:

• Publisher features, such as the domain of the url where the ad was displayed;
• Advertiser features (advertiser id, type of products,...)
• User features, for instance browser type;
• Interaction of the user with the advertiser, such as the number of the times the user visited the advertiser website.