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Open solution to the Santander Value Prediction Challenge :tropical_fish:

Home Page: https://www.kaggle.com/c/santander-value-prediction-challenge

License: MIT License

Python 100.00%

machine-learning deep-learning data-science reproducibility reproducible-experiments open-source education training python lightgbm

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open-solution-value-prediction's Issues

A ton of missing values

validation

add lgbm monitoring

implement 2 stage model predict if zero/not_zero and than if not zero second model

Sparse Features

Test distribution is different

explore manifold learning techniques

http://scikit-learn.org/stable/modules/classes.html#module-sklearn.manifold

Implement missing value imputation transformer

Investigate removing columns after aggregation features have been calculated

heterogenous models

Implement drop columns where non zero frequency very low

monitor Kaggle's discussion

explorse sparse factorization machines

create first simple end-to-end pipeline

Explore outliers in train/test

Small Dataset

features

Error in command line execution

neptune: Executing in Offline Mode.
neptune: Error: Invalid parameter 'train'. Parameter names must begin with double dash.

I just add config in neptune.yaml and change this line in utils.py

Add KNN-features

explore rounding predictions

Adversarial validation

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