Part 4: Applications
In this fourth and final part of the book, we delve into the development of comprehensive applications that utilize real-world data. Our focus will be on encompassing aspects previously omitted in previous chapters, such as exploratory data analysis and data processing. We aim to provide an exhaustive overview of the machine learning pipeline, from raw data to model output analysis. We will also highlight the strengths and limitations of the techniques discussed.
The projects in this section have been designed to be adaptable and customizable, enabling readers to apply them to other datasets and tasks with ease. This makes it an ideal resource for readers who wish to build a portfolio of applications and showcase their work on GitHub.
By the end of this part, you will know how to implement GNNs for traffic forecasting, anomaly detection, and recommender systems. These projects have been selected to demonstrate the versatility and potential of GNNs in solving...
