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Predictive Analytics using Networked Data in R

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4 hours
4,300 XP
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Course Description

In this course, you will learn to perform state-of-the art predictive analytics using networked data in R. The aim of network analytics is to predict to which class a network node belongs, such as churner or not, fraudster or not, defaulter or not, etc. To accomplish this, we discuss how to leverage information from the network and its underlying structure in a predictive way. More specifically, we introduce the idea of featurization such that network features can be added to non-network features as such boosting the performance of any resulting analytical model. In this course, you will use the igraph package to generate and label a network of customers in a churn setting and learn about the foundations of network learning. Then, you will learn about homophily, dyadicity and heterophilicty, and how these can be used to get key exploratory insights in your network. Next, you will use the functionality of the igraph package to compute various network features to calculate both node-centric as well as neighbor based network features. Furthermore, you will use the Google PageRank algorithm to compute network features and empirically validate their predictive power. Finally, we teach you how to generate a flat dataset from the network and analyze it using logistic regression and random forests.
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  1. 1

    Introduction, networks and labelled networks


    In this chapter you will be introduced to labelled networks, network learning and the challanges that can arise.

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    Motivation: social networks and predictive analytics
    50 xp
    Most likely to churn
    50 xp
    Create a network from an edgelist
    100 xp
    Labeled networks and network learning
    50 xp
    Labeling nodes
    100 xp
    Coloring nodes
    100 xp
    Visualizing Churners
    100 xp
    Relational Neighbor Classifier
    100 xp
    Challenges of network-based inference
    50 xp
    Challenges in Network learning
    50 xp
    Probabilistic Relational Neighbor Classifier
    100 xp
    Collective Inferencing
    100 xp

In the following tracks

Network Analysis


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David Campos
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Shon Inouye
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Chester Ismay
Maria Oskarsdottir HeadshotMaria Oskarsdottir

Post-doctoral Researcher

María Óskarsdóttir is a post-doctoral researcher and an active R user. She holds a PhD in Business Economics from KU Leuven (Belgium). Her research puts focus on applying social network analytics techniques for predictive modeling in marketing, credit scoring and insurance.
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Bart Baesens HeadshotBart Baesens

Professor in Analytics and Data Science at KU Leuven

Bart Baesens is professor in Analytics and Data Science at the Faculty of Economics and Business of KU Leuven, and a lecturer at the University of Southampton (UK). He has done extensive research on big data & analytics, credit risk analytics and fraud analytics. He regularly tutors, advises and provides consulting support to international firms with respect to their big data, analytics and fraud & credit risk management strategy.
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