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
Machine Learning with LightGBM and Python

You're reading from   Machine Learning with LightGBM and Python A practitioner's guide to developing production-ready machine learning systems

Arrow left icon
Product type Paperback
Published in Sep 2023
Publisher Packt
ISBN-13 9781800564749
Length 252 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Andrich van Wyk Andrich van Wyk
Author Profile Icon Andrich van Wyk
Andrich van Wyk
Arrow right icon
View More author details
Toc

Table of Contents (17) Chapters Close

Preface 1. Part 1: Gradient Boosting and LightGBM Fundamentals
2. Chapter 1: Introducing Machine Learning FREE CHAPTER 3. Chapter 2: Ensemble Learning – Bagging and Boosting 4. Chapter 3: An Overview of LightGBM in Python 5. Chapter 4: Comparing LightGBM, XGBoost, and Deep Learning 6. Part 2: Practical Machine Learning with LightGBM
7. Chapter 5: LightGBM Parameter Optimization with Optuna 8. Chapter 6: Solving Real-World Data Science Problems with LightGBM 9. Chapter 7: AutoML with LightGBM and FLAML 10. Part 3: Production-ready Machine Learning with LightGBM
11. Chapter 8: Machine Learning Pipelines and MLOps with LightGBM 12. Chapter 9: LightGBM MLOps with AWS SageMaker 13. Chapter 10: LightGBM Models with PostgresML 14. Chapter 11: Distributed and GPU-Based Learning with LightGBM 15. Index 16. Other Books You May Enjoy

Classifying individual credit scores with LightGBM

Our second case study is a problem of credit score classification for individuals. The dataset is available from https://www.kaggle.com/datasets/parisrohan/credit-score-classification?datasetId=2289007.

The dataset is significantly larger than the previous problem and has unique data formatting problems. For brevity, we will not go through the solution in as much detail as with the previous problem (as much of the work is the same), but the end-to-end solution is available at https://github.com/PacktPublishing/Practical-Machine-Learning-with-LightGBM-and-Python/tree/main/chapter-6/credit-score-classification.ipynb.

Problem definition

The dataset consists of 100,000 rows and 27 columns representing individuals’ demographic and financial information, including a credit score rating. The data includes information regarding individual income, number of loans, payment behavior, and investments. The credit score may be rated...

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