Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Machine Learning with R Quick Start Guide

You're reading from   Machine Learning with R Quick Start Guide A beginner's guide to implementing machine learning techniques from scratch using R 3.5

Arrow left icon
Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781838644338
Length 250 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Iván Pastor Sanz Iván Pastor Sanz
Author Profile Icon Iván Pastor Sanz
Iván Pastor Sanz
Arrow right icon
View More author details
Toc

Summary

In this chapter, we have learned some initial important steps to prepare and understand our data. How many variables are available in our dataset? What kind of information do we have? Are there some missing values in the data? How can I treat missing values and outliers? I hope you can now answer these questions.

Moreover, in this chapter, we also learned how to split our data to train and validate our forthcoming predictive model. In the next chapter, we will advance one step ahead, performing a univariate analysis on this data, which means analyzing whether variables are useful for predicting bank failures.

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at £16.99/month. Cancel anytime