4 datasets found

Data from: ieeecis-fraud-detection
kaggle.com
Updated Mar 3, 2020
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Mohamed NIANG (2020). ieeecis-fraud-detection [Dataset]. https://www.kaggle.com/niangmohamed/ieeecis-fraud-detection/discussion
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CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Mar 3, 2020
Dataset provided by
Kagglehttp://kaggle.com/
Authors
Mohamed NIANG
License
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
Description
Dataset

This dataset was created by Mohamed NIANG

Released under CC0: Public Domain

Contents
ieee-data-preprocessing
kaggle.com
Updated Mar 19, 2020
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Mohamed NIANG (2020). ieee-data-preprocessing [Dataset]. https://www.kaggle.com/datasets/niangmohamed/ieeedatapreprocessing
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CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Mar 19, 2020
Dataset provided by
Kagglehttp://kaggle.com/
Authors
Mohamed NIANG
License
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
Description
Dataset

This dataset was created by Mohamed NIANG

Released under CC0: Public Domain

Contents
Users IDs
kaggle.com
zip
Updated Oct 6, 2019
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Arturo Garcia (2019). Users IDs [Dataset]. https://www.kaggle.com/artmatician/users-ids
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zip(5724215 bytes)Available download formats
Dataset updated
Oct 6, 2019
Authors
Arturo Garcia
Description
Dataset

This dataset was created by Arturo Garcia

Contents
Feature Engineering Data
kaggle.com
Updated Jul 23, 2019
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Mat Leonard (2019). Feature Engineering Data [Dataset]. https://www.kaggle.com/matleonard/feature-engineering-data/metadata
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CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Jul 23, 2019
Dataset provided by
Kagglehttp://kaggle.com/
Authors
Mat Leonard
Description
This dataset is a sample from the TalkingData AdTracking competition. I kept all the positive examples (where is_attributed == 1), while discarding 99% of the negative samples. The sample has roughly 20% positive examples.

For this competition, your objective was to predict whether a user will download an app after clicking a mobile app advertisement.

File descriptions

train_sample.csv - Sampled data

Data fields

Each row of the training data contains a click record, with the following features.

ip: ip address of click.

app: app id for marketing.

device: device type id of user mobile phone (e.g., iphone 6 plus, iphone 7, huawei mate 7, etc.)

os: os version id of user mobile phone

channel: channel id of mobile ad publisher

click_time: timestamp of click (UTC)

attributed_time: if user download the app for after clicking an ad, this is the time of the app download

is_attributed: the target that is to be predicted, indicating the app was downloaded

Note that ip, app, device, os, and channel are encoded.

I'm also including Parquet files with various features for use within the course.
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