Book Image

Apache Spark for Data Science Cookbook

By : Padma Priya Chitturi
Book Image

Apache Spark for Data Science Cookbook

By: Padma Priya Chitturi

Overview of this book

Spark has emerged as the most promising big data analytics engine for data science professionals. The true power and value of Apache Spark lies in its ability to execute data science tasks with speed and accuracy. Spark’s selling point is that it combines ETL, batch analytics, real-time stream analysis, machine learning, graph processing, and visualizations. It lets you tackle the complexities that come with raw unstructured data sets with ease. This guide will get you comfortable and confident performing data science tasks with Spark. You will learn about implementations including distributed deep learning, numerical computing, and scalable machine learning. You will be shown effective solutions to problematic concepts in data science using Spark’s data science libraries such as MLLib, Pandas, NumPy, SciPy, and more. These simple and efficient recipes will show you how to implement algorithms and optimize your work.
Table of Contents (17 chapters)
Apache Spark for Data Science Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Visualize machine learning models with Databricks notebook


Databricks provides flexibility to visualize machine learning models using the built-in display() command that displays DataFrames as a table and creates convenient one-click plots. In the following recipe we'll, we'll see how to visualize data with Databricks notebook.

Getting ready

To step through this recipe, you will need a running Spark cluster in any one of the modes, that is, local, standalone, YARN, or Mesos. Install Hadoop (optionally), Scala, and Java. Create a user account in Databricks and get access for the Notebook.

How to do it…

The fitted versus residuals plot is available for linear regression and logistic regression models. The Databricks fitted versus residuals plot is analogous to R's residuals versus fitted plot for linear models. Linear regression computes a prediction as a weighted sum of the input variables. The fitted versus residuals plot can be used to assess a linear regression model's goodness of fit. The...