shopee-ml-kaggle-competition

This repository contains codes for the Shopee-IET Machine Learning Kaggle Competition (Achieved rank 2 out of 51). The work was done during my internship at the Nanyang Technological University, Singapore in Spring 2018.

Contest page link: https://www.kaggle.com/c/shopee-iet-machine-learning-competition#description (Competition page have been removed.)

Leaderboard link (team name - hesl): https://www.kaggle.com/c/shopee-iet-machine-learning-competition/leaderboard (Competition page have been removed.)

Used a deep learning based ensemble model and a few data augmentation techniques to obtain top performance in the above challenge. Approach is explained in the presentation.pptx file

Prerequisites:

1. Python 3
2. Pytorch 0.3

Main scripts:

1. train.py - Trains a model on the given data
2. train_top5.py - Writes the top 5 predictions with their confidence for trained model on the training data.
3. test.py - tests the model and outputs predictions
4. test_top5.py - Predicts the top 5 classes along with their confidence on test data.
5. ensemble_optimal.py - Uses the top 5 predictions of each model to give final prediction

Competition Snap:

Contributors:

1. Punit Bhatt (Microsoft, India)
2. Rydel Dcosta (Morgan Stanley, India)
3. Omkar Damle (New York University, United States)