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.