Amazon-ML-Challenge

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Amazon-ML-Challenge

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Product Browse Node Classification

Amazon-ML-Challenge 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# Amazon-ML-Challenge
Product Browse Node Classification

## About Amazon ML Challenge
![](https://he-s3.s3.amazonaws.com/media/cache/0a/be/0abe8c0908dcb9e67941600739f6d651.png)
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.

## ML Challenge Stages
![](https://s3-ap-southeast-1.amazonaws.com/he-public-data/ML%20Challenge%20Stages_Hackerearth%201a6b4fa1.JPG)

## Datset Link🔗
[Click Here for the Datset](https://he-s3.s3.ap-southeast-1.amazonaws.com/media/hackathon/amazon-ml-challenge/product-browse-node-classification-2-7ff04e5a/546b594ee0a211eb.zip?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=16c8b3b0a2fcd83db7ed5437de70012b5b4651ce5f543c1fe62512904d5f0ce7&X-Amz-Date=20210802T090306Z&X-Amz-Credential=AKIA6I2ISGOYH7WWS3G5%2F20210802%2Fap-southeast-1%2Fs3%2Faws4_request)

## Problem
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!!

## Data Description

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

## Team Members
1. [Akshat Jain](https://github.com/akshatprogrammer)
2. [Anjali Pathak](https://github.com/anjalipathak13)
3. [Aman Gupta](https://github.com/Aman-Gupta-Ji)
4. [Himank Kandpal](https://github.com/Himank0)

## Team Ranking
![](https://github.com/akshatprogrammer/Amazon-ML-Challenge/blob/main/Leaderboard%20-%20Amazon%20ML%20Challenge%20_%20HackerEarth%20-%20Google%20Chrome%208_2_2021%202_37_28%20PM.png)
 Out of 3290 Teams we secured 296th Position.

## Contributors
<a href = "https://github.com/akshatprogrammer/Amazon-ML-Challenge/graphs/contributors">
 <img src = "https://contrib.rocks/image?repo=akshatprogrammer/Amazon-ML-Challenge"/>
</a>

# Connect With Me
LinkedIn : https://www.linkedin.com/in/akshatjaingeu/ 
Email : akshat.kodia@gmail.com 
Twitter : www.twitter.com/akki_aj89
Website : https://akshatprogrammer.github.io/portfolio/ 
# Personal
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

# Gratitude
Thank You, if you like it please leave a Star.

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