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.
The objective is to build a model, learned using historical data, that will determine an employee's access needs. So model will take an employee's role information and a resource code and will return whether or not access should be granted.
This is a binary classification task.
Resource access may allow an employee to read/manipulate resources through various applications or web portals. So its crucial that employee does not get access to Resource they should not have. Recall would be good metric to evaluate false negative. Therefore F1-score is a good metric to evaluate the performance of this dataset as it weights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously.
Given the data related to current employees and their provisioned access
- We have large class imbalance.
- Features
- ACTION - Class label to grant access to resource. If 1 access is granted to the resource else no access.
- 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)
Required to run code from this repo.
Python 3.9+
dvc-2.34.0
$ git clone https://github.com/SmithaUpadhyaya/employee_access_resource_classifier.git
$ cd employee_access_resource_classifier
Creating a virtual environment and install all the requirements
python -m venv .env
source .env/bin/activate
pip install --upgrade pip
pip install -r requirements.txt
Create and configure a location for remote storage (e.g. AWS S3) OR setup a local DVC remote. Change the pointer to your DVC remote in .dvc/config.
Download and extract zip "amazon-employee-access-challenge.zip" and place the data files in "data/training" folder
1. Open terminal and execute.
uvicorn app:app --reload
2. Open any web browser and type in URL:
http://127.0.0.1:8000/employee_resource_access
1. Build the Docker image
docker build -t employee_access_check .
2. Run the Docker image
docker run -d --name cp_employee_access_check -p 80:80 employee_access_check
3. Open any web browser and type in URL:
http://127.0.0.1:8000/employee_resource_access
-
Reproduce DVC pipeline
dvc repro # runs the pipeline defined in 'dvc.yaml' and param defined in 'param.yaml' dvc push # pushes the resulting artifacts to a DVC remote configured in `.dvc/config` -
Run experiments
Change paramaters in 'param.yaml' file then run
dvc exp run -
Run grid search on paramaters
Change/Define the paramaters for the model in 'src\data\random_search.py'
python src\data\random_search.py
1) Python 3.9.8
2) Random Forest - Ensemble Technique
3) DVC - Pipeline and Track experiments
4) FastAPI
5) Docker
