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.
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.
1 maintainer
Release files for data-science-bowl-2019 1.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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| data_science_bowl_2019-1.0.1.tar.gz | 3.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| data_science_bowl_2019-1.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 11.6 kB
Release files / data_science_bowl_2019-1.0.1.tar.gz
| Download URL | data_science_bowl_2019-1.0.1.tar.gz |
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| Size | 3.4 kB |
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Release files / data_science_bowl_2019-1.0.1-py3-none-any.whl
| Download URL | data_science_bowl_2019-1.0.1-py3-none-any.whl |
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| Size | 8.2 kB |
| Tags | Python 3 |
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twine/3.1.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.2 requests-toolbelt/0.9.1 tqdm/4.39.0 CPython/3.7.3
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