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
Python for Finance Cookbook – Second Edition

You're reading from   Python for Finance Cookbook – Second Edition Over 80 powerful recipes for effective financial data analysis

Arrow left icon
Product type Paperback
Published in Dec 2022
Publisher Packt
ISBN-13 9781803243191
Length 740 pages
Edition 2nd Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Eryk Lewinson Eryk Lewinson
Author Profile Icon Eryk Lewinson
Eryk Lewinson
Arrow right icon
View More author details
Toc

Table of Contents (18) Chapters Close

Preface 1. Acquiring Financial Data 2. Data Preprocessing FREE CHAPTER 3. Visualizing Financial Time Series 4. Exploring Financial Time Series Data 5. Technical Analysis and Building Interactive Dashboards 6. Time Series Analysis and Forecasting 7. Machine Learning-Based Approaches to Time Series Forecasting 8. Multi-Factor Models 9. Modeling Volatility with GARCH Class Models 10. Monte Carlo Simulations in Finance 11. Asset Allocation 12. Backtesting Trading Strategies 13. Applied Machine Learning: Identifying Credit Default 14. Advanced Concepts for Machine Learning Projects 15. Deep Learning in Finance 16. Other Books You May Enjoy
17. Index

Applied Machine Learning: Identifying Credit Default

In recent years, we have witnessed machine learning gaining more and more popularity in solving traditional business problems. Every so often, a new algorithm is published, beating the current state of the art. It is only natural for businesses (in all industries) to try to leverage the incredible powers of machine learning in their core functionalities.

Before specifying the task we will be focusing on in this chapter, we provide a brief introduction to the field of machine learning. The machine learning domain can be broken down into two main areas: supervised learning and unsupervised learning. In the former, we have a target variable (label), which we try to predict as accurately as possible. In the latter, there is no target, and we try to use different techniques to draw some insights from the data.

We can further break down supervised problems into regression problems (where a target variable is a continuous number...

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 ₹800/month. Cancel anytime