Skip to Content
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

By which method can KNN be implemented?

Again, we follow a similar process for KNN as we did to create the previous models. We import the KNeighborsClassifier method from scikit-learn's library to use the KNN algorithm for modeling. Next, we import the HR attrition dataset using the pandas library and split the dataset into train and test sets:

import numpy as npimport pandas as pdfrom sklearn.metrics import accuracy_scorefrom sklearn.neighbors import KNeighborsClassifierhr_data = pd.read_csv('data/hr.csv', header=0)hr_data.head()hr_data = hr_data.dropna()print(" Data Set Shape ", hr_data.shape)print(list(hr_data.columns))print(" Sample Data ", hr_data.head())data_trnsf = pd.get_dummies(hr_data, columns =['salary', 'sales'])data_trnsf.columns ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Automated Machine Learning

Automated Machine Learning

Adnan Masood
R: Unleash Machine Learning Techniques

R: Unleash Machine Learning Techniques

Raghav Bali, Dipanjan Sarkar, Brett Lantz, Cory Lesmeister

Publisher Resources

ISBN: 9781788629898Supplemental Content