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scikit-learn Cookbook - Second Edition

You're reading from  scikit-learn Cookbook - Second Edition

Product type Book
Published in Nov 2017
Publisher Packt
ISBN-13 9781787286382
Pages 374 pages
Edition 2nd Edition
Languages
Author (1):
Trent Hauck Trent Hauck
Profile icon Trent Hauck

Table of Contents (13) Chapters

Preface 1. High-Performance Machine Learning – NumPy 2. Pre-Model Workflow and Pre-Processing 3. Dimensionality Reduction 4. Linear Models with scikit-learn 5. Linear Models – Logistic Regression 6. Building Models with Distance Metrics 7. Cross-Validation and Post-Model Workflow 8. Support Vector Machines 9. Tree Algorithms and Ensembles 10. Text and Multiclass Classification with scikit-learn 11. Neural Networks 12. Create a Simple Estimator

Using k-means to cluster data

In a dataset, we observe sets of points gathered together. With k-means, we will categorize all the points into groups, or clusters.

Getting ready

First, let's walk through some simple clustering; then we'll talk about how k-means works:

import numpy as np
import pandas as pd

from sklearn.datasets import make_blobs
blobs, classes = make_blobs(500, centers=3)

Also, since we'll be doing some plotting, import matplotlib as shown:

import matplotlib.pyplot as plt
%matplotlib inline #Within an ipython notebook

How to do it…

We...

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