| Description |
1 online resource |
| Contents |
Cover; Copyright; Credits; About the Author; About the Reviewer; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Spark for Machine Learning; Spark overview and Spark advantages; Spark overview; Spark advantages; Spark computing for machine learning; Machine learning algorithms; MLlib; Other ML libraries; Spark RDD and dataframes; Spark RDD; Spark dataframes; Dataframes API for R; ML frameworks, RM4Es and Spark computing; ML frameworks; RM4Es; The Spark computing framework; ML workflows and Spark pipelines; ML as a step-by-step workflow; ML workflow examples; Spark notebooks |
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Notebook approach for MLStep 1: Getting the software ready; Step 2: Installing the Knitr package; Step 3: Creating a simple report; Spark notebooks; Summary; Chapter 2: Data Preparation for Spark ML; Accessing and loading datasets; Accessing publicly available datasets; Loading datasets into Spark; Exploring and visualizing datasets; Data cleaning; Dealing with data incompleteness; Data cleaning in Spark; Data cleaning made easy; Identity matching; Identity issues; Identity matching on Spark; Entity resolution; Short string comparison; Long string comparison; Record deduplication |
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Identity matching made betterCrowdsourced deduplication; Configuring the crowd; Using the crowd; Dataset reorganizing; Dataset reorganizing tasks; Dataset reorganizing with Spark SQL; Dataset reorganizing with R on Spark; Dataset joining; Dataset joining and its tool -- the Spark SQL; Dataset joining in Spark; Dataset joining with the R data table package; Feature extraction; Feature development challenges; Feature development with Spark MLlib; Feature development with R; Repeatability and automation; Dataset preprocessing workflows; Spark pipelines for dataset preprocessing |
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Dataset preprocessing automationSummary; Chapter 3: A Holistic View on Spark; Spark for a holistic view; The use case; Fast and easy computing; Methods for a holistic view; Regression modeling; The SEM approach; Decision trees; Feature preparation; PCA; Grouping by category to use subject knowledge; Feature selection; Model estimation; MLlib implementation; The R notebooks' implementation; Model evaluation; Quick evaluations; RMSE; ROC curves; Results explanation; Impact assessments; Deployment; Dashboard; Rules; Summary; Chapter 4: Fraud Detection on Spark; Spark for fraud detection |
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The use caseDistributed computing; Methods for fraud detection; Random forest; Decision trees; Feature preparation; Feature extraction from LogFile; Data merging; Model estimation; MLlib implementation; R notebooks implementation; Model evaluation; A quick evaluation; Confusion matrix and false positive ratios; Results explanation; Big influencers and their impacts; Deploying fraud detection; Rules; Scoring; Summary; Chapter 5: Risk Scoring on Spark; Spark for risk scoring; The use case; Apache Spark notebooks; Methods of risk scoring; Logistic regression; Preparing coding in R |
| Summary |
Develop a range of cutting-edge machine learning projects with Apache Spark using this actionable guideAbout This Book Customize Apache Spark and R to fit your analytical needs in customer research, fraud detection, risk analytics, and recommendation engine development Develop a set of practical Machine Learning applications that can be implemented in real-life projects A comprehensive, project-based guide to improve and refine your predictive models for practical implementationWho This Book Is For If you are a data scientist, a data analyst, or an R and SPSS user with a good understanding of machine learning concepts, algorithms, and techniques, then this is the book for you. Some basic understanding of Spark and its core elements and application is required. What You Will Learn Set up Apache Spark for machine learning and discover its impressive processing power Combine Spark and R to unlock detailed business insights essential for decision making Build machine learning systems with Spark that can detect fraud and analyze financial risks Build predictive models focusing on customer scoring and service ranking Build a recommendation systems using SPSS on Apache Spark Tackle parallel computing and find out how it can support your machine learning projects Turn open data and communication data into actionable insights by making use of various forms of machine learningIn Detail There's a reason why Apache Spark has become one of the most popular tools in Machine Learning - its ability to handle huge datasets at an impressive speed means you can be much more responsive to the data at your disposal. This book shows you Spark at its very best, demonstrating how to connect it with R and unlock maximum value not only from the tool but also from your data. Packed with a range of project "blueprints" that demonstrate some of the most interesting challenges that Spark can help you tackle, you'll find out how to use Spark notebooks and access, clean, and join different datasets before putting your knowledge into practice with some real-world projects, in which you will see how Spark Machine Learning can help you with everything from fraud detection to analyzing customer attrition. You'll also find out how to build a recommendation engine using Spark's parallel computing powers. Style and approach This book offers a step-by-step approach to setting up Apache Spark, and use other analytical tools with it to process Big Data and build machine learning projects. The initial chapters focus more on the theory aspect of machine learning with Spark, while each of the later chapters focuses on building standalone projects using Spark |
| Notes |
Print version record |
| SUBJECT |
Spark (Electronic resource : Apache Software Foundation) http://id.loc.gov/authorities/names/no2015027445
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Spark (Electronic resource : Apache Software Foundation) fast |
| Subject |
Machine learning.
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Big data.
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Information retrieval.
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information retrieval.
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Big data
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Information retrieval
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Machine learning
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| Form |
Electronic book
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| ISBN |
1785887785 |
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9781785887789 |
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9781785880391 |
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178588039X |
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