Product Browse Node Classification
Amazon ML Challenge is a two-stage competition where students from all engineering campuses across India will get a unique opportunity to work on Amazon’s dataset to bring in fresh ideas and build innovative solutions for a real-world problem statement. The top three winning teams will receive pre-placement interviews (PPIs) for ML roles at Amazon along with cash prizes and certificates.
Amazon catalog consists of billions of products that belong to thousands of browse nodes (each browse node represents a collection of items for sale). Browse nodes are used to help customer navigate through our website and classify products to product type groups. Hence, it is important to predict the node assignment at the time of listing of the product or when the browse node information is absent.
As part of this hackathon, you will use product metadata to classify products into browse nodes. You will have access to product title, description, bullet points etc. and labels for ~3MM products to train and test your submissions. Note that there is some noise in the data - real world data looks like this!!
Full Train/Test dataset details:
Key column – PRODUCT_ID
Input features – TITLE, DESCRIPTION, BULLET_POINTS, BRAND
Target column – BROWSE_NODE_ID
Train dataset size – 2,903,024
Number of classes in Train – 9,919
Overall Test dataset size – 110,775
Out of 3290 Teams we secured 296th Position.
LinkedIn : https://www.linkedin.com/in/akshatjaingeu/
Email : [email protected]
Twitter : www.twitter.com/akki_aj89
Website : https://akshatprogrammer.github.io/portfolio/
Name : Akshat Jain
University : Graphic Era University, Dehradun(UK)
If any problem with this program reach me at Telegram
Here is the link -> https://t.me/akki_aj89
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