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Practical Data Analysis

You're reading from   Practical Data Analysis For small businesses, analyzing the information contained in their data using open source technology could be game-changing. All you need is some basic programming and mathematical skills to do just that.

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Product type Paperback
Published in Oct 2013
Publisher Packt
ISBN-13 9781783280995
Length 360 pages
Edition 1st Edition
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Author (1):
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Hector Cuesta Hector Cuesta
Author Profile Icon Hector Cuesta
Hector Cuesta
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Table of Contents (24) Chapters Close

Practical Data Analysis
Credits
Foreword
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started FREE CHAPTER 2. Working with Data 3. Data Visualization 4. Text Classification 5. Similarity-based Image Retrieval 6. Simulation of Stock Prices 7. Predicting Gold Prices 8. Working with Support Vector Machines 9. Modeling Infectious Disease with Cellular Automata 10. Working with Social Graphs 11. Sentiment Analysis of Twitter Data 12. Data Processing and Aggregation with MongoDB 13. Working with MapReduce 14. Online Data Analysis with IPython and Wakari Setting Up the Infrastructure Index

The data – historical gold prices


Regression analysis is a statistical tool for understanding the relationship between variables. In this chapter, we will implement a nonlinear regression to predict the gold price based on the historic gold prices. For this example, we will use the historical gold prices from January 2003 to May 2013 in a monthly range, obtained from www.gold.org. Finally, we will forecast the gold price for June 2013 and will contrast it with the real price from an independent source. The complete datasets (since December 1978) can be found at http://gold.org/download/value/stats/statistics/xls/gold_prices.xls.

The first seven records of the CSV file (gold.csv) look as follows:

date,price
1/31/2003,367.5
2/28/2003,347.5
3/31/2003,334.9
4/30/2003,336.8
5/30/2003,361.4
6/30/2003,346.0
7/31/2003,354.8

In this example, we will implement a Kernel ridge regression with the original time series and the smoothed time series, to compare the differences in the output.

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