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Python Data Mining Quick Start Guide

You're reading from   Python Data Mining Quick Start Guide A beginner's guide to extracting valuable insights from your data

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781789800265
Length 188 pages
Edition 1st Edition
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Author (1):
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Nathan Greeneltch Nathan Greeneltch
Author Profile Icon Nathan Greeneltch
Nathan Greeneltch
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Table of Contents (9) Chapters Close

Preface 1. Data Mining and Getting Started with Python Tools FREE CHAPTER 2. Basic Terminology and Our End-to-End Example 3. Collecting, Exploring, and Visualizing Data 4. Cleaning and Readying Data for Analysis 5. Grouping and Clustering Data 6. Prediction with Regression and Classification 7. Advanced Topics - Building a Data Processing Pipeline and Deploying It 8. Other Books You May Enjoy

Basic data terminology

This section is meant to be a quick overview of the terms that you should know before you get started. This list is very streamlined and is not exhaustive. Please refer to the suggested reading in Chapter 1, Data Mining and Getting Started with Python Tools, for wider coverage of domain-specific terminology.

Sample spaces

The sample space is the space that is covered by all the possible outcomes of a measurement. For example, if a feature column in a dataset is populated with the number of days last month that a responder watched television, then the sample space will include all the integers in the {0,1,2...31} set. If a manufacturing tool measures the temperature difference before and after processing...

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