Skip to main content

The notebooks for the competition Data Science Bowl 2019.

Project description


The notebooks for the competition Data Science Bowl 2019

I join this competition data-science-bowl-2019, which ends on January 15, 2020. For the data feature, I do some work on the series features, using word2vec, LDA and node2vec.

  1. wide and deep
  2. node2vec
  3. LDA

The baseline feature engineering I forked from Hosseinali (2019). However, it helps me focus on series features. Also, I use LTSM model to elaborate series features, I forked from Grecnik (2019).

Grecnik. 2019. “Bowl Lstm Prediction | Kaggle.” Kaggle. 2019.

Hosseinali, Massoud. 2019. “A New Baseline for Dsb 2019 - Catboost Model.” Kaggle. 2019.


pip install data_science_bowl_2019

How to use

See demo.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for data-science-bowl-2019, version 1.0.1
Filename, size File type Python version Upload date Hashes
Filename, size data_science_bowl_2019-1.0.1.tar.gz (3.4 kB) File type Source Python version None Upload date Hashes View
Filename, size data_science_bowl_2019-1.0.1-py3-none-any.whl (8.2 kB) File type Wheel Python version py3 Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page