Metadata-Version: 2.1
Name: data_science_bowl_2019
Version: 1.0.1
Summary: The notebooks for the competition Data Science Bowl 2019.
Home-page: https://github.com/JiaxiangBU/data-science-bowl-2019EX
Author: Jiaxiang Li and Jiatao Li
Author-email: alex.lijiaxiang@foxmail.com
License: Apache Software License 2.0
Description: 
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        # data_science_bowl_2019
        
        > The notebooks for the competition Data Science Bowl 2019
        
        
        
        I join this competition
        [data-science-bowl-2019](https://www.kaggle.com/c/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](https://github.com/JiaxiangBU/data-science-bowl-2019EX/blob/master/wide_and_deep.ipynb)
        2.  [node2vec](https://github.com/JiaxiangBU/data-science-bowl-2019EX/blob/master/node2vec.ipynb)
        3.  [LDA](https://github.com/JiaxiangBU/data-science-bowl-2019EX/blob/master/lda.ipynb)
        
        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).
        
        <div id="refs" class="references">
        
        <div id="ref-Grecnik2019">
        
        Grecnik. 2019. “Bowl Lstm Prediction | Kaggle.” Kaggle. 2019.
        <https://www.kaggle.com/nikitagrec/bowl-lstm-prediction>.
        
        </div>
        
        <div id="ref-Massoud_Hosseinali2019">
        
        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>.
        
        </div>
        
        </div>
        
        
        ## Install
        
        `pip install data_science_bowl_2019`
        
        ## How to use
        
        See demo.
        
Keywords: wide_and_deep LDA
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
