Book Image

Hands-On Data Analysis with Scala

By : Rajesh Gupta
Book Image

Hands-On Data Analysis with Scala

By: Rajesh Gupta

Overview of this book

Efficient business decisions with an accurate sense of business data helps in delivering better performance across products and services. This book helps you to leverage the popular Scala libraries and tools for performing core data analysis tasks with ease. The book begins with a quick overview of the building blocks of a standard data analysis process. You will learn to perform basic tasks like Extraction, Staging, Validation, Cleaning, and Shaping of datasets. You will later deep dive into the data exploration and visualization areas of the data analysis life cycle. You will make use of popular Scala libraries like Saddle, Breeze, Vegas, and PredictionIO for processing your datasets. You will learn statistical methods for deriving meaningful insights from data. You will also learn to create applications for Apache Spark 2.x on complex data analysis, in real-time. You will discover traditional machine learning techniques for doing data analysis. Furthermore, you will also be introduced to neural networks and deep learning from a data analysis standpoint. By the end of this book, you will be capable of handling large sets of structured and unstructured data, perform exploratory analysis, and building efficient Scala applications for discovering and delivering insights
Table of Contents (14 chapters)
Free Chapter
Section 1: Scala and Data Analysis Life Cycle
Section 2: Advanced Data Analysis and Machine Learning
Section 3: Real-Time Data Analysis and Scalability

Streaming linear regression using Spark

Using Spark Streaming, it is possible to update the parameters of the linear model online. In many ways, Spark Streaming's linear regression solution works very similarly to the k-means streaming solution.

We will be using the StreamingLinearRegressionWithSGD class that is provided as part of Spark MLlib. To initialize a StreamingLinearRegressionWithSGD object, the following needs to be done:

  1. Instantiate the StreamingLinearRegressionWithSGD object using the new StreamingLinearRegressionWithSGD() method
  2. Set the number of initial weights
  3. We should get a model that can be trained in a streaming fashion and can be used to make predictions

Let's explore this solution in a Spark shell by going through the following steps:

  1. Start a Spark shell in your Terminal as follows:
$ spark-shell
  1. Stop the current Spark session using the following...