Predictive Analytics using Networked Data in R
Learn to predict labels of nodes in networks using network learning and by extracting descriptive features from the networkStart Course for Free
4 Hours14 Videos56 Exercises3,872 Learners4300 XPNetwork Analysis with R Track
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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.
Introduction, networks and labelled networksFree
In this chapter you will be introduced to labelled networks, network learning and the challanges that can arise.Motivation: social networks and predictive analytics50 xpMost likely to churn50 xpCreate a network from an edgelist100 xpLabeled networks and network learning50 xpLabeling nodes100 xpColoring nodes100 xpVisualizing Churners100 xpRelational Neighbor Classifier100 xpChallenges of network-based inference50 xpChallenges in Network learning50 xpProbabilistic Relational Neighbor Classifier100 xpCollective Inferencing100 xp
In this chapter you will learn about homophily and how to compute the two measures that can be used to characterice it, dyadicity and heterophilicty.Homophily50 xpHomophilic networks50 xpExtracting types of edges100 xpCounting types of edges100 xpCounting nodes and computing connectance100 xpDyadicity50 xpSame label edges50 xpDyadicity of churners100 xpDyadicity of non-churners50 xpHeterophilicity50 xpCross label edges50 xpCompute heterophilicity100 xpSummary of homophily50 xpDyadicity, Heterophilicity, & Homophily50 xpIs the network homophilic?50 xp
In this chapter you will use the igraph package to compute various network features and add them to the network.Basic Network features50 xpSimple network features100 xpCentrality features100 xpTransitivity100 xpLink-Based Features50 xpAdjacency matrices100 xpLink-based features100 xpSecond order link-based features100 xpNeighborhood link-based features100 xpPageRank50 xpMost influential node50 xpChanges in PageRank100 xpConvergence of PageRank100 xpPersonalized PageRank100 xpExtract PageRank features100 xp
Putting it all together
In this chapter you will use the network from Chapter 3 to create a flat dataset. Using standard data mining techniques, you will build predictive models and measure their performance with AUC and top decile lift.Extract a dataset50 xpGetting a flat dataset100 xpMissing Values50 xpReplace missing values100 xpCorrelated variables100 xpBuilding a predictive model50 xpSplit into train and test100 xpLogistic regression model100 xpRandom forest model100 xpEvaluating model performance50 xpPredicting churn100 xpMeasure AUC50 xpMeasure top decile lift50 xpSummary and final thoughts50 xp
In the following tracksNetwork Analysis with R
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.
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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