data_modeling/data/task/amazon-employee-access-challenge.txt

Description  
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.

Objective  
The objective of this competition is to build a model, learned using historical data, that will determine an employee's access needs, such that manual access transactions (grants and revokes) are minimized as the employee's attributes change over time. The model will take an employee's role information and a resource code and will return whether or not access should be granted.

Partners  
This competition is hosted in collaboration with the IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2013).

Evaluation  
Submissions are judged on area under the ROC curve. In Matlab (using the stats toolbox): 
```
[~, ~, ~, auc ] = perfcurve(true_labels, predictions, 1);
```
In R (using the verification package):
```
auc = roc.area(true_labels, predictions)
```
In Python (using the metrics module of scikit-learn):
```
fpr, tpr, thresholds = metrics.roc_curve(true_labels, predictions, pos_label=1)
auc = metrics.auc(fpr, tpr)
```

Submission File  
For every line in the test set, submission files should contain two columns: id and ACTION. In the ground truth, ACTION is 1 if the resource should be allowed, 0 if the resource should not. Your predictions do not need to be binary. You may submit probabilities/predictions having any real value. The submission file should have a header: 
```
id,ACTION
1,1
2,0.2
3,1
4,0
5,2
...
```

Dataset Description  
The data consists of real historical data collected from 2010 & 2011. Employees are manually allowed or denied access to resources over time. You must create an algorithm capable of learning from this historical data to predict approval/denial for an unseen set of employees.

File Descriptions  
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.

Column Descriptions  
Column Name | Description
--- | ---
ACTION | ACTION is 1 if the resource was approved, 0 if the resource was not.
RESOURCE | An ID for each resource.
MGR_ID | The EMPLOYEE ID of the manager of the current EMPLOYEE ID record; an employee may have only one manager at a time.
ROLE_ROLLUP_1 | Company role grouping category id 1 (e.g., US Engineering).
ROLE_ROLLUP_2 | Company role grouping category id 2 (e.g., US Retail).
ROLE_DEPTNAME | Company role department description (e.g., Retail).
ROLE_TITLE | Company role business title description (e.g., Senior Engineering Retail Manager).
ROLE_FAMILY_DESC | Company role family extended description (e.g., Retail Manager, Software Engineering).
ROLE_FAMILY | Company role family description (e.g., Retail Manager).
ROLE_CODE | Company role code; this code is unique to each role (e.g., Manager).