This repository provides data analysis and the ML-Backend of our data app.

The key technologies:

• MLxtend
• Flask
• Pandas

With Flask the REST-server was built. The MLxtend library provides an implementation for the association rules, the convenient think is the C implementation. The Pandas package was used only for data manipulation.

The Algorithm used for the computation of personalised recommendations is called association rules.

To run locally the Flask REST-server app, please follow the next steps:

1. setup a virtual environment
2. pip install the requirements.txt
3. execute: python wsgi.py

To see how to send requests to the REST-server please see src/send_requests.py.