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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 network

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4 Horas14 Videos56 Ejercicios
4350 AprendicesTrophyDeclaración de cumplimiento

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Descripción del curso

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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Análisis de redes con R

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  1. 1

    Introduction, networks and labelled networks

    Gratuito

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

    Reproducir Capítulo Ahora
    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
Empresas

Group¿Entrenar a 2 o más personas?

Obtenga acceso de su equipo a la biblioteca completa de DataCamp, con informes centralizados, tareas, proyectos y más

En las siguientes pistas

Análisis de redes con R

Ir a la pista

Sets De Datos

Student Customers datasetStudent Edge List datasetStudent Network dataset

Colaboradores

Collaborator's avatar
David Campos
Collaborator's avatar
Shon Inouye
Collaborator's avatar
Chester Ismay
Maria Oskarsdottir HeadshotMaria Oskarsdottir

Post-doctoral Researcher

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