Code I used in the Amazon Employee Access Challenge competition on Kaggle
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Code I used in the Amazon Employee Access Challenge competition on Kaggle. Find my profile here.

My other compilations of code for kaggle competitions have been much more organized. This is a bit of dumping ground.

Amazon was interested in automating the process of granting access to various computer resources to its employees. They released a bunch of data about an employee's role at the company along with what resources they had been granted access to and those they'd been denied access. The goal was to build a model that accurately predicted that final permission.

The employee's roles, managers, divisions, and other information was already vectorized into unique integers. So data processing and cleaning was not a big part of this competition. I initially dumped this data into the gradient boosted decision trees and stochastic gradient descent models that had performed well in the last competition. Their performance was OK.

I then found that competitors were sharing a lot of information in the forums. The most successful strategies involved using simple logistic regression on a highly engineered sample of features. Specifically, each unique integer is converted into its own column where the value is one where it appears in the original data and zero otherwise in a process called one-hot encoding. Extra columns can then be generated from groups of the original features and binary columns constructed from unique combinations of integers. This generates a very large set of binary features. Greedy forward feature selection can then be used to optimize the output of the final logistic regression model.

I successfully replicated those all those feature engineering techniques to achieve a pretty good score. I then extended the dimensionality of the feature grouping and added greedy backward feature selection. Averaging all of these models together produced my best score.

I also tried out some techniques that did not work. I tried to optimize the stacking of the many models that I came up with. These results always produced a better CV score but a lower leaderboard score. I also tried to use the selected features in different models like naïve bayesian. This didn't really affect the score very much.

I probably learned the most from this competition than any of the others. I have to thank all the people on the forums that really made that possible. I think the importance of feature engineering has finally sunk in.