Gen AI for nursing - Data processor to assist in a student recovery plan

This repository is to handle the data for Professor Aboelela's Gen AI project. Data is sanitized and processed into a single file for use with an AI service to help guide student learning for at-risk students.

The process can be performed on your local computer following the instructions below:

QUICK REFERENCE -- for after the initial setup

1. Replace old data from the data folder with the new data.
2. Open up a terminal and enter the following commands:

cd ~/Desktop/aboelela
python process.py

3. The resulting processed.csv will appear in the results folder.
   • This will overwrite the old processed.csv file if it is still present in the directory.

Prerequisites and Set-up

At minimum you will need Python present in your computer. You can check this from your terminal using the command.

python --version

If no python version exists you will have to download python here: https://www.python.org/downloads/

Download the repository from https://github.com/ccnmtl/aboelela

There are two main ways to handle the download:

Simple - .zip download

From the GitHub repository listed in the header above:

1. Click on <> Code → Download ZIP
2. Unzip the folder and move it to your desktop.
3. Open your computer's terminal and input the following instructions into the terminal:

cd ~/Desktop/aboelela

Comprehensive - git clone

git --version

1. Ensure that you have git downloaded on you computer using the above command:
   • If git is not present, it can be downloaded here: https://git-scm.com/downloads
2. From the GitHub repository listed in the header above: click on <> Code → SSH → Copy the git address
3. Navigate to your Desktop from the terminal and clone the repository to your desktop:

cd ~/Desktop
git clone [the copied address from GitHub]

4. The new directory should appear on your desktop. Enter the new directory:

cd aboelela

Populate the data folder

The finished file is the result of the Results and Item .csv files. Download the files and place them in the data folder.

REMEMBER: Clear old data from the folder before adding new data.

Process the data

Once the directory is set up and the data is in the proper folder you can run the following command from the terminal:

python process.py

Retrieve the processed data from the results folder. The file will always be labeled as processed.csv