Purpose

Apply machine learning to understand how to improve the project portfolio

Code and library base

If you wish to go straight to the code and document libraries, start here

Use cases for machine learning in managing projects.

Below shows the phase of project delivery and Operations these use cases first appear.

Fitting ML into an organisation context

This optional survey asks some basic questions about what you and your team is trying to do with machine learning.

ML Online survey

Summary of start-up steps for application

Once a use-case has been chosen, the following decisions can be made:

Identify possible use cases
Map to business area/lifecycle
Narrow down to a generic ML method
Select a straightforward ML model
Choose a suitable model environment
Choose a code library or algorithm to apply that model
select data-set

More is said about each stage here

Applying machine learning at project or programme or portfolio levels

Machine learning provides different types of insight at different project levels. Some machine learning approaches make the most of the extra context provided by graph databases, specifically in terms of which relationships are meaningful.

PROJECT & PROGRAMME level: node, edge and property prediction for risks, dependencies, project sectoral properties and success.
PORTFOLIO level: mostly standard network algorithms (centrality, breadth first search etc). Includes the conversion between graph and tree structures to provide appropriate views for different stakeholders. The ML element is currently restricted to identifying common and anomalous graph motifs.
TASK AND SCHEDULE level: This is the least well developed, but a broad range of approaches to making the most of the Optimisation work of Professor Warren B. Powell, making the most of the inherent graph structure of resource-task-outcome paths. Eventually exploring Graph-Graph neural networks & Seq-Seq/ Transformer approaches as well as Monte Carlo Tree search
PMO and CENTRE OF EXCELLENCE. NLP applied to boost taxonomic and semantic approaches to curating body of project practice for the organisation.

More guidance on applying ML to portfolios

The guidance continues here