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Practical Machine Learning with R

You're reading from Practical Machine Learning with R Define, build, and evaluate machine learning models for real-world applications

Product type Paperback

Published in Aug 2019

Publisher Packt

ISBN-13 9781838550134

Length 416 pages

Edition 1st Edition

Languages

R

Tools

RStudio

Concepts

Machine Learning

Authors (3):

Brindha Priyadarshini Jeyaraman

Ludvig Renbo Olsen

Monicah Wambugu

View More author details

Table of Contents (8) Chapters

About the Book

1. An Introduction to Machine Learning

2. Data Cleaning and Pre-processing FREE CHAPTER

3. Feature Engineering

4. Introduction to neuralnet and Evaluation Methods

5. Linear and Logistic Regression Models

6. Unsupervised Learning

1. Appendix

Data Cleaning and Pre-processing

Learning Objectives

By the end of this chapter, you will be able to:

Perform the sort, rank, filter, subset, normalize, scale, and join operations in an R data frame.
Identify and handle outliers, missing values, and duplicates gracefully using the MICE and rpart packages.
Perform undersampling and oversampling on a dataset.
Apply the concepts of ROSE and SMOTE to handle unbalanced data.

This chapter covers the important concepts of handling data and making the data ready for analysis.

You have been reading a chapter from

Practical Machine Learning with R

Published in: Aug 2019

Publisher: Packt

ISBN-13: 9781838550134

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Authors (3)

Jeyaraman

Jeyaraman

Brindha Priyadarshini Jeyaraman is a senior data scientist at AIDA Technologies. She has completed her M.Tech in knowledge engineering with a gold medal from the National University of Singapore. She has more than 10 years of work experience and she is an expert in understanding business problems, and designing and implementing solutions using machine learning. She has worked on several real data science projects in the insurance and finance domain.

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Wambugu

Wambugu

Monicah Wambugu is the lead Data Scientist at Loanbee, a financial technology company that offers micro-loans by leveraging on data, machine learning and analytics to perform alternative credit scoring. She is a graduate student at the School of Information at UC Berkeley Masters in Information Management and Systems. Monicah is particularly interested in how data science and machine learning can be used to design products and applications that respond to the behavioral and socio-economic needs of target audiences.

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Olsen

Olsen

Ludvig Renbo Olsen, BSc in Cognitive Science from Aarhus University, is the author of multiple R packages, such as groupdata2 and cvms. With 4 years of R and Python experience, including working as a machine learning researcher at the Danish startup UNSILO, he is passionate about creating tools and tutorials for students and scientists. Guided by Effective Altruism, he intends to positively impact the world through his career.

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