Kaggle challenge: Santander

In this notebook we use the following concepts and methods:

• Undersampling and oversampling.
• Outlier detection.
• Anomaly detection with autoencoders.
• Gaussian Naive Bayes.
• Gradient boosted trees (XGB and LGBM), with grod-search and cross-validation.
• Bayesian hyperparameter optimization.
• Model blending for improved AUC scores.

If you have problems viewing the .ipynb notebook, go to: https://nbviewer.jupyter.org/github/peitsche/Santander/blob/master/kaggle_santander_git.ipynb