Predictive Process Monitoring - A Starter Package for Jupyter

The notebooks in this repository are part of the assignment in the course Advanced Process Mining and intended as a starter for building your own prediction models for predictive process monitoring. They can be used as:

• Cloud notebooks via MyBinder
• Local stand-alone notebooks
• Local Dockerized notebooks

refer to the Installations & Usage section below for usage instructions.

You may also refer to the PM4Py documentation on Machine Learning for further options or an alternative to this implementation: https://processintelligence.solutions/static/api/2.7.11/api.html#machine-learning-pm4py-ml

The collection of notebooks is a living document and subject to change.

Table of Contents

• Data Loading
• Data Preparation
• Prediction Outcome

Installation & Usage

Cloud notebooks via MyBinder

Click on the launch binder links for either the R or the Python notebook. You could also use the Google Colab service; however, you may need to prepare the Google Colab environment to have the respective packages installed (see standalone instructions).

Local notebooks

Docker Compose

Build and run a Docker image with Compose:

docker compose up -d

Standalone

Please make sure to have installed the following requirements:

Python

pip install -r requirements.txt

Make sure to install GraphViz for the visualization as per PM4Py documentation https://processintelligence.solutions/static/api/2.7.11/install.html

You should be able to run the Jupyter notebooks directly in a Jupyter environment using:

jupyter lab