Book Image

Practical Big Data Analytics

By : Nataraj Dasgupta
Book Image

Practical Big Data Analytics

By: Nataraj Dasgupta

Overview of this book

Big Data analytics relates to the strategies used by organizations to collect, organize, and analyze large amounts of data to uncover valuable business insights that cannot be analyzed through traditional systems. Crafting an enterprise-scale cost-efficient Big Data and machine learning solution to uncover insights and value from your organization’s data is a challenge. Today, with hundreds of new Big Data systems, machine learning packages, and BI tools, selecting the right combination of technologies is an even greater challenge. This book will help you do that. With the help of this guide, you will be able to bridge the gap between the theoretical world of technology and the practical reality of building corporate Big Data and data science platforms. You will get hands-on exposure to Hadoop and Spark, build machine learning dashboards using R and R Shiny, create web-based apps using NoSQL databases such as MongoDB, and even learn how to write R code for neural networks. By the end of the book, you will have a very clear and concrete understanding of what Big Data analytics means, how it drives revenues for organizations, and how you can develop your own Big Data analytics solution using the different tools and methods articulated in this book.
Table of Contents (16 chapters)
Title Page
Packt Upsell
Contributors
Preface

Popular machine learning algorithms


There are various different classes of machine learning algorithms. As such, since algorithms can belong to multiple 'classes' or categories at the same time at a conceptual level, it is hard to specifically state that an algorithm belongs exclusively to a single class. In this section, we will briefly discuss a few of the most commonly used and well-known algorithms.

These include:

  • Regression models
  • Association rules
  • Decision trees
  • Random forest
  • Boosting algorithms
  • Support vector machines
  • K-means
  • Neural networks

Note that in the examples, we have shown the basic use of the R functions using the entire dataset. In practice, we'd split the data into a training and test set, and once we have built a satisfactory model apply the same on the test dataset to evaluate the model's performance.

Regression models

Regression models range from commonly used linear, logistic, and multiple regression algorithms used in statistics to Ridge and Lasso regression, which penalizes...