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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 2. Basic Terminology and Our End-to-End Example FREE CHAPTER 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

The scikit-learn transformer API

One of the reasons that scikit-learn is so popular is due to its ease of use. There are only a few, well thought-out API designs in the scikit-learn library and they are applied in a sweeping manner across many different methods and routines. This chapter will make use of the transformer API. It's extremely straightforward and once you understand how to use it, you can try our new transformer methods with ease because they all work the same (that is, they all make use of the transformer API).

The steps for transforming data are given as follows:

  1. Import the module.
  2. Instantiate the transformer object (model in the following diagram).
  3. Fit the model to input the training data (X_train in the following diagram).
  4. Transform the new test data (X_test in the following diagram).

These steps can also be represented as a workflow diagram, as follows...

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