Book Image

Apache Spark 2 for Beginners

By : Rajanarayanan Thottuvaikkatumana
Book Image

Apache Spark 2 for Beginners

By: Rajanarayanan Thottuvaikkatumana

Overview of this book

<p>Spark is one of the most widely-used large-scale data processing engines and runs extremely fast. It is a framework that has tools that are equally useful for application developers as well as data scientists.</p> <p>This book starts with the fundamentals of Spark 2 and covers the core data processing framework and API, installation, and application development setup. Then the Spark programming model is introduced through real-world examples followed by Spark SQL programming with DataFrames. An introduction to SparkR is covered next. Later, we cover the charting and plotting features of Python in conjunction with Spark data processing. After that, we take a look at Spark's stream processing, machine learning, and graph processing libraries. The last chapter combines all the skills you learned from the preceding chapters to develop a real-world Spark application.</p> <p>By the end of this book, you will have all the knowledge you need to develop efficient large-scale applications using Apache Spark.</p>
Table of Contents (15 chapters)
Apache Spark 2 for Beginners
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface

Wine classification


The dataset containing various features of white wine is used in this wine quality classification use case. The following are the features of the dataset:

  • Fixed acidity

  • Volatile acidity

  • Citric acid

  • Residual sugar

  • Chlorides

  • Free sulfur dioxide

  • Total sulfur dioxide

  • Density

  • pH

  • Sulphates

  • Alcohol

Based on these features, the quality (score between 0 and 10) is determined. If the quality is less than 7, then it is classified as bad and a value of 0 is assigned to the label. If the quality is 7 or above, then it is classified as good and a value of 1 is assigned to the label. In other words, the classification value is the label of this dataset. Using this dataset, a model is going to be trained and then using the trained model, testing is done and predictions are made. This is a classification problem. The Logistic Regression algorithm is used to train the model. In this machine learning application use case, it deals with modeling the relationship between a dependent variable...