In this liveProject, you’ll build a model that can predict the age of customers for a telecom company. You’ll take basic profile data and call records for your customers, and build and improve a linear regression model using graph features. Finally, you’ll use a powerful graph neural network that can combine data from both profile features and graph structure to reliably reveal customer age range.
This project is designed for learning purposes and is not a complete, production-ready application or solution.
When you start your liveProject, you get full access to the following books for 90 days.
This liveProject is for data scientists interested in the basic techniques of graph data science. This project is suitable for all levels of expertise, from beginners to experienced practitioners. To begin this liveProject, you will need to be familiar with:
- Basic LynxKite
- Basic SQL
- Fundamentals of predictive modelling
- Tables and their representation as CSV file
- Basic data science and machine learning
you will learn
In this liveProject, you’ll learn the basics of graph data structures and how to define relationships between data. You’ll then advance to more sophisticated methods.
- Using linear regression based on profile data
- Predicting customer age based on neighbors
- Linear regression with graph invariants as input features
- Graph neural networks
- You choose the schedule and decide how much time to invest as you build your project.
- Project roadmap
- Each project is divided into several achievable steps.
- Get Help
- While within the liveProject platform, get help from other participants and our expert mentors.
- Compare with others
- For each step, compare your deliverable to the solutions by the author and other participants.
- book resources
- Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.