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Hands-On Automated Machine Learning
book

Hands-On Automated Machine Learning

by Sibanjan Das, Umit Mert Cakmak
April 2018
Beginner to intermediate content levelBeginner to intermediate
282 pages
6h 52m
English
Packt Publishing
Content preview from Hands-On Automated Machine Learning

Partitioning clustering (KMeans)

We need to import a KMeans method from the scikit-learn package and the rest of the code remains similar to the hierarchical clustering's code:

import pandas as pdimport numpy as npfrom sklearn import preprocessingimport matplotlib.pyplot as plt from sklearn.cluster import KMeansfrom sklearn.metrics import silhouette_samples, silhouette_scorehr_data = pd.read_csv('data/hr.csv', header=0)hr_data.head()hr_data = hr_data.dropna()print(hr_data.shape)print(list(hr_data.columns))data_trnsf = pd.get_dummies(hr_data, columns =['salary', 'sales'])data_trnsf.columns

We need to specify the number of clusters (n_clusters) in the k-means function to create a model. It is an essential parameter for creating k-means clusters. ...

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Publisher Resources

ISBN: 9781788629898Supplemental Content