Book Image

Practical Data Analysis - Second Edition

By : Hector Cuesta, Dr. Sampath Kumar
Book Image

Practical Data Analysis - Second Edition

By: Hector Cuesta, Dr. Sampath Kumar

Overview of this book

Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service. This book explains the basic data algorithms without the theoretical jargon, and you’ll get hands-on turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark.
Table of Contents (21 chapters)
Practical Data Analysis - Second Edition
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface

Summary


In this chapter, we got into dimensionality reduction and linear classification using SVM. In our example, we created a simple but powerful SVM classifier using different kinds of kernels, and you learned how to perform a dimensionality reduction using PCA implemented in Python with mlpy. Finally, we presented how to use nonlinear kernels, such as Gaussian or Polynomial. The work in this chapter was just an introduction to the SVM algorithm, with only two dimensions. The results can be improved with a multidimensional approach with an optimal hyperplane.

In the next chapter, you will learn how to model an epidemiological event (infectious disease) and how an infectious disease is spread through a population. We will create a simulator of an outbreak with a cellular automaton implemented in D3.js.