Context

When an employee at any company starts work, they first need to obtain the computer access necessary to fulfill their role. This access may allow an employee to read/manipulate resources through various applications or web portals. It is assumed that employees fulfilling the functions of a given role will access the same or similar resources. It is often the case that employees figure out the access they need as they encounter roadblocks during their daily work (e.g. not able to log into a reporting portal). A knowledgeable supervisor then takes time to manually grant the needed access in order to overcome access obstacles. As employees move throughout a company, this access discovery/recovery cycle wastes a nontrivial amount of time and money.

There is a considerable amount of data regarding an employee’s role within an organization and the resources to which they have access. Given the data related to current employees and their provisioned access, models can be built that automatically determine access privileges as employees enter and leave roles within a company. These auto-access models seek to minimize the human involvement required to grant or revoke employee access.

Part of the competition "Amazon.com - Employee Access Challenge" (https://www.kaggle.com/c/amazon-employee-access-challenge), the data consists of real historical data collected from 2010 & 2011. Employees are manually allowed or denied access to resources over time. Your task is to create an algorithm capable of learning from this historical data to predict approval/denial for an unseen set of employees.

Content

The data comes from Amazon Inc. collected from 2010-2011 (published on Kaggle platform). The training set consists of 32769 samples and the testing one of 58922 samples. The training set has one label attribute named “ACTION”, whose value “1” indicates an application is approved whereas “0” indicates rejection. As predictors of this state, there are eight features, indicating characteristics of the required resource anf the role and work group of the employee at Amazon requesting access.

train.csv - The training set. Each row has the ACTION (ground truth), RESOURCE, and information about the employee's role at the time of approval

test.csv - The test set for which predictions should be made. Each row asks whether an employee having the listed characteristics should have access to the listed resource.

Models are judged on area under the ROC curve (https://en.wikipedia.org/wiki/Receiver_operating_characteristic)

Acknowledgements

The data has been donated by Amazon and the original competition has been hosted in collaboration with the IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2013)

Dataset

This dataset was created by SARMISTHA DASH

Contents