rss_feedEugene Yan ·20.06.2015 open_in_newОригинал

DataScience SG Meetup - How we got top 3% in Kaggle

One Saturday afternoon, I volunteered to share about my recent effort in Kaggle’s Otto competition where I placed 85th / 3514 with my fellow competitor Weimin.

Given that it was a lazy Saturday afternoon, I did not expect the lecture room at SMU to be fully packed. The data science meetup scene in Singapore was more vibrant and hotter than I thought.

In approximately 45 minutes, we shared about how we thought about and had an in-depth discussion with the audience on the topics below:

The evaluation metric (multi-class log loss)

Validation approaches

Feature engineering and selection

Feature transformation (e.g., standardization, log-transformation, tf-idf)

Creating aggregate and t-sne features

Machine learning techniques (trees and neural nets)

Ensembling techniques

Top solutions and architectures

A suggested framework for Kaggle competitions

More details can be found in the slides below.

Questions? Want to follow my journey? Reach out on Twitter @eugeneyan!

If you found this useful, please cite this write-up as:

Yan, Ziyou. (Jun 2015). DataScience SG Meetup - How we got top 3% in Kaggle. eugeneyan.com. https://eugeneyan.com/speaking/dssg-kaggle-top-3-percent-talk/.

@article{yan2015kaggle, title = {DataScience SG Meetup - How we got top 3% in Kaggle}, author = {Yan, Ziyou}, journal = {eugeneyan.com}, year = {2015}, month = {Jun}, url = {https://eugeneyan.com/speaking/dssg-kaggle-top-3-percent-talk/} }

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