Download Kaggle Display Advertising Challenge Dataset [EXCLUSIVE]

The Criteo dataset supporting this study was obtained from -display-ad-challenge/data, accessed on 10 September 2022. The Avazu dataset was obtained from -ctr-prediction/data, accessed on 18 September 2022.

Criteo is a well-known real-world display advertising dataset with impression information and corresponding user click logs for each ad. This dataset is widely used in the evaluation of various CTR prediction models. It contains user click logs for 45 million samples each of which has 39 features, including 13 continuous numerical features and 26 categorical features.

Download Kaggle Display Advertising Challenge Dataset

DOWNLOAD 🔥 https://blltly.com/2y3iCv 🔥

In this example we will showcase how pai4sk-snapml can be used to train a logistic regression classifier for the task of click through rate prediction. For this purpose we use the publicly available criteo-kaggle dataset which can be downloaded from the Display Advertising Challenge.

For our experiments we used Criteo's traffic over a period of 7 days. The dataset is also available on kaggle for download: -display-ad-challenge We pre-trained DLRM model using 39M Ads from Criteo dataset. From feature importance calculation perspective we used a small fraction of preprocessed data.

I'm trying to download the kaggle imagenet object localization challenge data into google colab so that I can use it to train my model. Kaggle uses an API for easy and fast access to their datasets. ( -api) However, when calling the command "kaggle competitions download -c imagenet-object-localization-challenge" in google colab, it can't find the kaggle.json file which contains my username and api-key.

I haven't had this problem on my mac when running a jupyter notebook, but since I want to use google's gpu for my model, I started using google colab. Because the kaggle API expects the username and api-key to be in a kaggle.json file located in a .kaggle directory, I first created the directory .kaggle and then the file kaggle.json, into which I wrote my username and api-key (The example below doesn't display my username and api-key). I then tried to configure the path to my json file for kaggle to use when calling the kaggle download command.

The Kaggle community is very generous on shared insights and approaches, even during the competitions. One of the Kagglers shared a data leak he had discovered. That leak, based on the page_views.csv dataset, revealed the actually clicked ads for about 4% user visits (display_ids) of test set. For a machine learning competition, sharing the data leak was kind of a fair-play, and created a new baseline for competitors. My Approach #2 was to use the Approach #1 predictions and only adjust ads ranking for leaked clicked ads, putting them on top of other ads. My LB score then jumped up to 0.65317, and the data leak was used in all my remaining submissions in the competition, as well as the other competitors did.

The dataset used in the Kaggle competition are publicly available in the NOMAD Repository ( -1), the Kaggle competition website ( -predict-transparent-conductors), and the labeled training and test set can be found on github ( _2018_kaggle_dataset). The three winning models are available at -toolkit.nomad-coe.eu. ff782bc1db