kaggle-IEEE-fraud-detection

The notebooks are ordered from 0 to 6, following my progression through the competition

nb0 splits the data in train/valid/test
nb1 is a quick and dirty first modeling approach, to create a baseline
nb2 explores a validation scheme specific to the data we have in this competition
nb3 uses this scheme to perform feature selection on the hundreds of features we have
nb4 investigates the variables our feature importance highlighted, to get a sense of why these would matter
nb5 here we try to create additional features
nb6 perform a Kfold ensembling of lightgbm models

Name		Name	Last commit message	Last commit date
Latest commit History 17 Commits
presentation documents		presentation documents
.gitignore		.gitignore
README.md		README.md
nb0_train_valid_test_split.ipynb		nb0_train_valid_test_split.ipynb
nb1_XGB_baseline.ipynb		nb1_XGB_baseline.ipynb
nb2_Correlation_valid-leaderboard_for_time-series-split_strategy.ipynb		nb2_Correlation_valid-leaderboard_for_time-series-split_strategy.ipynb
nb3_Feature_Selection.ipynb		nb3_Feature_Selection.ipynb
nb4_EDA_on_some_features.ipynb		nb4_EDA_on_some_features.ipynb
nb5_New_Features_Ideas.ipynb		nb5_New_Features_Ideas.ipynb
nb6_Inference.ipynb		nb6_Inference.ipynb
reduce_mem_usage.py		reduce_mem_usage.py

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