Ott

The Otto Group is one of the world's biggest e-commerce companies, with subsidiaries
in more than 20 countries, including Crate & Barrel (USA), Otto.de (Germany) and 3
Suisses (France). We are selling millions of products worldwide every day, with several
thousand products being added to our product line.

A consistent analysis of the performance of our products is crucial. However, due to our
diverse global infrastructure, many identical products get classified differently.
Therefore, the quality of our product analysis depends heavily on the ability to accurately
cluster similar products. The better the classification, the more insights we can generate
about our product range.

This is the original descritipon from the Kaggle competition.

"
Dataset Description
See, fork, and run a random forest benchmark model through Kaggle Scripts

Each row corresponds to a single product. There are a total of 93 numerical features, which represent counts of different events. All features have been obfuscated and will not be defined any further.

There are nine categories for all products. Each target category represents one of our most important product categories (like fashion, electronics, etc.). The products for the training and testing sets are selected randomly.

File descriptions
trainData.csv - the training set
testData.csv - the test set
sampleSubmission.csv - a sample submission file in the correct format
Data fields
id - an anonymous id unique to a product
feat_1, feat_2, ..., feat_93 - the various features of a product
target - the class of a product

"

https://www.kaggle.com/competitions/otto-group-product-classification-challenge/data