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Mastering Predictive Analytics with Python

You're reading from   Mastering Predictive Analytics with Python Exploit the power of data in your business by building advanced predictive modeling applications with Python

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Product type Paperback
Published in Aug 2016
Publisher
ISBN-13 9781785882715
Length 334 pages
Edition 1st Edition
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Author (1):
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Joseph Babcock Joseph Babcock
Author Profile Icon Joseph Babcock
Joseph Babcock
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Table of Contents (11) Chapters Close

Preface 1. From Data to Decisions – Getting Started with Analytic Applications FREE CHAPTER 2. Exploratory Data Analysis and Visualization in Python 3. Finding Patterns in the Noise – Clustering and Unsupervised Learning 4. Connecting the Dots with Models – Regression Methods 5. Putting Data in its Place – Classification Methods and Analysis 6. Words and Pixels – Working with Unstructured Data 7. Learning from the Bottom Up – Deep Networks and Unsupervised Features 8. Sharing Models with Prediction Services 9. Reporting and Testing – Iterating on Analytic Systems Index

Time series analysis

While the imdb data contained movie release years, fundamentally the objects of interest were the individual films and the ratings, not a linked series of events over time that might be correlated with one another. This latter type of data – a time series – raises a different set of questions. Are datapoints correlated with one another? If so, over what timeframe are they correlated? How noisy is the signal? Pandas DataFrames have many built-in tools for time series analysis, which we will examine in the next section.

Cleaning and converting

In our previous example, we were able to use the data more or less in the form in which it was supplied. However, there is not always a guarantee that this will be the case. In our second example, we'll look at a time series of oil prices in the US by year over the last century (Makridakis, Spyros, Steven C. Wheelwright, and Rob J. Hyndman. Forecasting methods and applications, John Wiley & Sons. Inc, New York...

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