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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

Scikit-learn Estimator API

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

The steps are given as follows:

  1. Import the module
  2. Instantiate the estimator object (regression or classification model in the following diagram)
  3. Fit the model-to-map input training data (X_train in the following diagram) to the ground truth y_train labels
  4. Predict y_pred on the new test data (X_test in the following diagram)

It can also be...

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