Scala data analysis cookbook : navigate the world of data analysis, visualization, and machine learning with over 100 hands-on Scala recipes /

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Navigate the world of data analysis, visualization, and machine learning with over 100 hands-on Scala recipes About This Book Implement Scala in your data analysis using features from Spark, Breeze, and Zeppelin Scale up your data anlytics infrastructure with practical recipes for Scala machine lear...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Manivannan, Arun (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham : Packt Publishing, 2015.
Colección:Quick answers to common problems.
Temas:
Data mining.
Information visualization.
Data Mining
Exploration de données (Informatique)
Visualisation de l'information.
COMPUTERS / Databases / Data Mining
Data mining
Information visualization
Acceso en línea:Texto completo
Texto completo
Tabla de Contenidos:
  • Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with Breeze; Introduction; Getting Breeze
  • the linear algebra library; Working with vectors; Working with matrices; Vectors and matrices with randomly distributed values; Reading and writing CSV files; Chapter 2: Getting Started with Apache Spark DataFrames; Introduction; Getting Apache Spark; Creating a DataFrame from CSV; Manipulating DataFrames; Creating a DataFrame from Scala case classes; Chapter 3: Loading and Preparing Data
  • DataFrame
  • IntroductionLoading more than 22 features into classes; Loading JSON into DataFrames; Storing data as Parquet files; Using the Avro data model in Parquet; Loading from RDBMS; Preparing data in Dataframes; Chapter 4: Data Visualization; Introduction; Visualizing using Zeppelin; Creating scatter plots with Bokeh-Scala; Creating a time series MultiPlot with Bokeh-Scala; Chapter 5: Learning from Data; Introduction; Supervised and unsupervised learning; Gradient descent; Predicting continuous values using linear regression; Binary classification using LogisticRegression and SVM
  • Binary classification using LogisticRegression with Pipeline APIClustering using K-means; Feature reduction using principal component analysis; Chapter 6: Scaling Up; Introduction; Building the Uber JAR; Submitting jobs to the Spark cluster (local); Running the Spark Standalone cluster on EC2; Running the Spark Job on Mesos (local); Running the Spark Job on YARN (local); Chapter 7: Going Further; Introduction; Using Spark Streaming to subscribe to a Twitter stream; Using Spark as an ETL tool; Using StreamingLogisticRegression to classify a Twitter stream using Kafka as a training stream

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