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The notebooks for the competition Data Science Bowl 2019.

Project description

data_science_bowl_2019

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. https://www.kaggle.com/nikitagrec/bowl-lstm-prediction.

Hosseinali, Massoud. 2019. “A New Baseline for Dsb 2019 - Catboost Model.” Kaggle. 2019. https://www.kaggle.com/mhviraf/a-new-baseline-for-dsb-2019-catboost-model.

Install

pip install data_science_bowl_2019

How to use

See demo.

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