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

Chapter 7.  Predicting Gold Prices

In this chapter, you will be introduced to the basic concepts of Time Series Data and Regression. First, we distinguish some of the basic concepts such as Trend, Seasonality, and Noise, along with the principles of Lineal Regression using the Python library scikit-learn. Then, we will introduce the Historic Gold Prices time series and see how to perform a nonlinear forecast using Kernel Ridge Regression. Later, we will present a regression using the smoothed time series as the input.

This chapter will cover the following topics:

  • Working with time series data

  • Lineal regression

  • The data: historical gold prices

  • Nonlinear regression

  • Kernel Ridge Regression

  • Smoothing the gold prices time series

  • Predicting in the smoothed time series

  • Contrasting the predicted value