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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

Chapter 2. Exploratory Data Analysis and Visualization in Python

Analytic pipelines are not built from raw data in a single step. Rather, development is an iterative process that involves understanding the data in greater detail and systematically refining both model and inputs to solve a problem. A key part of this cycle is interactive data analysis and visualization, which can provide initial ideas for features in our predictive modeling or clues as to why an application is not behaving as expected.

Spreadsheet programs are one kind of interactive tool for this sort of exploration: they allow the user to import tabular information, pivot and summarize data, and generate charts. However, what if the data in question is too large for such a spreadsheet application? What if the data is not tabular, or is not displayed effectively as a line or bar chart? In the former case, we could simply obtain a more powerful computer, but the latter is more problematic. Simply put, many traditional...

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