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Machine Learning with scikit-learn Quick Start Guide

You're reading from   Machine Learning with scikit-learn Quick Start Guide Classification, regression, and clustering techniques in Python

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Product type Paperback
Published in Oct 2018
Publisher Packt
ISBN-13 9781789343700
Length 172 pages
Edition 1st Edition
Languages
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Author (1):
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Kevin Jolly Kevin Jolly
Author Profile Icon Kevin Jolly
Kevin Jolly
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Table of Contents (10) Chapters Close

Preface 1. Introducing Machine Learning with scikit-learn FREE CHAPTER 2. Predicting Categories with K-Nearest Neighbors 3. Predicting Categories with Logistic Regression 4. Predicting Categories with Naive Bayes and SVMs 5. Predicting Numeric Outcomes with Linear Regression 6. Classification and Regression with Trees 7. Clustering Data with Unsupervised Machine Learning 8. Performance Evaluation Methods 9. Other Books You May Enjoy

Implementing the k-means algorithm in scikit-learn

Now that you understand how the k-means algorithm works internally, we can proceed to implement it in scikit-learn. We are going to work with the same fraud detection dataset that we used in all of the previous chapters. The key difference is that we are going to drop the target feature, which contains the labels, and identify the two clusters that are used to detect fraud.

Creating the base k-means model

In order to load the dataset into our workspace and drop the target feature with the labels, we use the following code:

import pandas as pd
#Reading in the dataset
df = pd.read_csv('fraud_prediction.csv')
#Dropping the target feature & the index
df = df.drop([&apos...
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