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

Learning and classification


When we want to automatically identify which category belongs to a specific value (categorical value), we need to implement an algorithm that can decide the most likely category for the value based on previous data. This is called a classifier. In the words of Tom Mitchell:

"How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"

The key word here is learning (supervised learning, in this case) and knowing how to train an algorithm to identify categorical elements. The common examples are spam classification, speech recognition, search engines, computer vision, and language detection, but there is a large number of applications for a classifier. We can find two kinds of problems in classification. The Binary classification is where we only have two categories (Spam or Not Spam) and the Multiclass classification, in which there are many categories involved (such as the...