Predict Age Using GNNs

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prerequisites
basic LynxKite • basic SQL • fundamentals of predictive modelling • basic data science and machine learning
skills learned
using linear regression based on profile data • using linear regression with graph invariants as input features • using graph neural networks
Andras Nemeth
1 week · 6-8 hours per week · INTERMEDIATE
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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.

project author

Andras Nemeth
András Németh is the CTO of Lynx Analytics, with significant experience (as the top technology executive on client projects) in using graphs in real-world consultancy projects. He also led the development of LynxKite, a Graph Data Science Platform in the past six years. He has an MsC in Software Engineering and an MsC in Mathematics. Before Lynx Analytics, he worked for Google where he was the tech lead on a project aiming to annotate web pages with entities from Google's Knowledge Graph.

prerequisites

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

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