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Data-Centric Machine Learning with Python

You're reading from   Data-Centric Machine Learning with Python The ultimate guide to engineering and deploying high-quality models based on good data

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Product type Paperback
Published in Feb 2024
Publisher Packt
ISBN-13 9781804618127
Length 378 pages
Edition 1st Edition
Languages
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Authors (3):
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Jonas Christensen Jonas Christensen
Author Profile Icon Jonas Christensen
Jonas Christensen
Manmohan Gosada Manmohan Gosada
Author Profile Icon Manmohan Gosada
Manmohan Gosada
Nakul Bajaj Nakul Bajaj
Author Profile Icon Nakul Bajaj
Nakul Bajaj
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Table of Contents (17) Chapters Close

Preface 1. Part 1: What Data-Centric Machine Learning Is and Why We Need It FREE CHAPTER
2. Chapter 1: Exploring Data-Centric Machine Learning 3. Chapter 2: From Model-Centric to Data-Centric – ML’s Evolution 4. Part 2: The Building Blocks of Data-Centric ML
5. Chapter 3: Principles of Data-Centric ML 6. Chapter 4: Data Labeling Is a Collaborative Process 7. Part 3: Technical Approaches to Better Data
8. Chapter 5: Techniques for Data Cleaning 9. Chapter 6: Techniques for Programmatic Labeling in Machine Learning 10. Chapter 7: Using Synthetic Data in Data-Centric Machine Learning 11. Chapter 8: Techniques for Identifying and Removing Bias 12. Chapter 9: Dealing with Edge Cases and Rare Events in Machine Learning 13. Part 4: Getting Started with Data-Centric ML
14. Chapter 10: Kick-Starting Your Journey in Data-Centric Machine Learning 15. Index 16. Other Books You May Enjoy

Introducing the dataset

First, let’s introduce our problem statement. For loan providers, it is important to ensure that people who get a loan can make payment and don’t default. However, it is equally important that people are not denied a loan due to a model trained on poor-quality data. This is where the data-centric approach helps make the world a better place – it provides a framework for data scientists and data engineers to question the quality of data.

For this chapter, we will use the loan prediction dataset from Analytics Vidhya. You can download the dataset from https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/#ProblemStatement. There are two files – one for training and one for testing. The test file doesn’t contain any labels. For this chapter, we will utilize the training file, which has been downloaded and saved as train_loan_prediction.csv.

First, we will look at the dataset and check the first...

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