Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases now! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
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
R Machine Learning By Example

You're reading from   R Machine Learning By Example Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully

Arrow left icon
Product type Paperback
Published in Mar 2016
Publisher
ISBN-13 9781784390846
Length 340 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Raghav Bali Raghav Bali
Author Profile Icon Raghav Bali
Raghav Bali
Arrow right icon
View More author details
Toc

Table of Contents (10) Chapters Close

Preface 1. Getting Started with R and Machine Learning FREE CHAPTER 2. Let's Help Machines Learn 3. Predicting Customer Shopping Trends with Market Basket Analysis 4. Building a Product Recommendation System 5. Credit Risk Detection and Prediction – Descriptive Analytics 6. Credit Risk Detection and Prediction – Predictive Analytics 7. Social Media Analysis – Analyzing Twitter Data 8. Sentiment Analysis of Twitter Data Index

Our next challenge


We have dealt with some interesting applications of machine learning in the e-commerce domain in the last couple of chapters. For the next two chapters, our big challenge will be in the financial domain. We will be using data analysis and machine learning techniques to analyze financial data from a German bank. This data will contain a lot of information regarding customers of that bank. We will be analyzing that data in two stages which include descriptive and predictive analytics.

  • Descriptive: Here we will look closely at the data and its various attributes. We will perform descriptive analysis and visualizations to see the kind of features we are dealing with and how they might be related to credit risk. The data we will be dealing with here consists of labeled data already and we will be able to see how many customers were credit risks and how many weren't. We will also look closely at each feature in the data and understand its significance which will be useful in...

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 $19.99/month. Cancel anytime