Predictive Analytics using Networked Data in R

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Course Features

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Duration

4 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Mobile, Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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Video Content

4 hours

Course Description

This course will show you how to use networked information to perform advanced predictive analysis. We will be discussing how to use the network's structure and information in a predictive way. We also introduce featurization which allows network features and other features to be added. This improves the performance of any analytic model. This course will show you how to use igraph to label and create a network of customers in a churn environment. The foundations of network learning will also be covered. Next, you will learn about heterophily and dyadicity and how they can help you gain important insights into your network. Next, you will use the igraph package functionality for computing network features. This includes both neighbor-based and node centric network features. The Google PageRank algorithm will be used to compute and validate network features. We will also demonstrate how to create a flat data set using the network, and then analyze it with logistic regression or random forest.

Course Overview

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Virtual Labs

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International Faculty

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Post Course Interactions

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Hands-On Training,Instructor-Moderated Discussions

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Case Studies, Captstone Projects

Skills You Will Gain

Prerequisites/Requirements

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Network Analysis in R

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Supervised Learning in R: Classification

What You Will Learn

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Learn to predict labels of nodes in networks using network learning and by extracting descriptive features from the network

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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

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Then, you will learn about homophily, dyadicity and heterophilicty, and how these can be used to get key exploratory insights in your network

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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

Course Instructors

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Bart 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...
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Maria 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 tec...

Course Reviews

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$12

$6

49% OFF

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