MLSR

Maching Learning Scholarship Rater(MLSR), is a rating assistant for scholarship that helps rate the applicants based on maching-learning methods.
Clarification: This project currently is incomplete, and it is only for research, not application. Some rules (for the convenience of data manipulation) of model such as feature engineering may seems discriminatory but we are not intended to do so.

Install

1. install dependency If you use pip, run

pip install -r config/requirement.txt

2. load data in data folder. Due to privacy protection we hide our original dataset so you need to construct your own dataset .
   Demo.csv is an example for dataset format.

Run

1. For model training

python main.py --dt
# This means training decision tree.

Type python main.py --help for command of other models.
Note: you need to create the model output directory before training!
2. For plot, run python plot_main.py. (You may need to modify this code to draw the plot you want.)

API Documentation

click here

Building the Desktop Demo

We write a GUI application as a demo of our model, which is in the demo folder. If you want to build it, first ensure PyQt5 and pyinstaller is installed(version specification can be found in /config/requirement.txt). Then type this command in the demo folder:

pyinstaller -F -w -i favicon.ico MLSR_Demo.py

Run the web demo

The web demo use Flask as backend and a simple html as frontend which can be run on production environment with Gunicorn.

1. Install the entire repo with all the dependencies and gunicorn.
2. Enter command

cd /xxxxxx/MLSR/web
gunicorn -b 127.0.0.1:5000 app:app

3. It should be running on http://127.0.0.1:5000 and make a reverse proxy by Nginx or Apache if you want it open to the Internet.