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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 summary statistics

Practitioners in the field of descriptive analytics use a set of four summary statistics to quickly understand a dataset. With practice, you should be able to strengthen your intuition about each one of these statistical measurements. In fact, it's a great place to start with most problem statements that you will face. The four summary statistics are described as follows:

  • Locations: The location or center of the data; this can be measured by the mean (average), median, or mode. The median is the point of delineation in 50% of the data, and the mode is the most occurring points, or largest part of the distribution.
  • Spread: How the data is spread around the center; this can be measured with standard deviation, which sums the average distance from the mean of each data point, or variance, which is the square of the deviation.
  • Shape: A description...
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