You're reading from Data Cleaning and Exploration with Machine Learning Get to grips with machine learning techniques to achieve sparkling-clean data quickly

Product type Paperback

Published in Aug 2022

Publisher Packt

ISBN-13 9781803241678

Length 542 pages

Edition 1st Edition

Concepts

Machine Learning

Author (1):

Michael Walker

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Table of Contents (23) Chapters

Preface

1. Section 1 – Data Cleaning and Machine Learning Algorithms

2. Chapter 1: Examining the Distribution of Features and Targets FREE CHAPTER

3. Chapter 2: Examining Bivariate and Multivariate Relationships between Features and Targets

4. Chapter 3: Identifying and Fixing Missing Values

5. Section 2 – Preprocessing, Feature Selection, and Sampling

6. Chapter 4: Encoding, Transforming, and Scaling Features

7. Chapter 5: Feature Selection

8. Chapter 6: Preparing for Model Evaluation

9. Section 3 – Modeling Continuous Targets with Supervised Learning

10. Chapter 7: Linear Regression Models

11. Chapter 8: Support Vector Regression

12. Chapter 9: K-Nearest Neighbors, Decision Tree, Random Forest, and Gradient Boosted Regression

13. Section 4 – Modeling Dichotomous and Multiclass Targets with Supervised Learning

14. Chapter 10: Logistic Regression

15. Chapter 11: Decision Trees and Random Forest Classification

16. Chapter 12: K-Nearest Neighbors for Classification

17. Chapter 13: Support Vector Machine Classification

18. Chapter 14: Naïve Bayes Classification

19. Section 5 – Clustering and Dimensionality Reduction with Unsupervised Learning

20. Chapter 15: Principal Component Analysis

21. Chapter 16: K-Means and DBSCAN Clustering

22. Other Books You May Enjoy

Technical requirements

We will work with the feature_engine, mlxtend, and boruta packages in this chapter, in addition to the scikit-learn library. You can use pip to install these packages. I have chosen a dataset with a small number of observations for our work in this chapter, so the code should work fine even on suboptimal workstations.

Note

We will work exclusively in this chapter with data from The National Longitudinal Survey of Youth, conducted by the United States Bureau of Labor Statistics. This survey started with a cohort of individuals in 1997 who were born between 1980 and 1985, with annual follow-ups each year through 2017. We will work with educational attainment, household demographic, weeks worked, and wage income data. The wage income column represents wages earned in 2016. The NLS dataset can be downloaded for public use at https://www.nlsinfo.org/investigator/pages/search.

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You're reading from Data Cleaning and Exploration with Machine Learning Get to grips with machine learning techniques to achieve sparkling-clean data quickly

Table of Contents (23) Chapters

Technical requirements

Authors (1)

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You're reading from Data Cleaning and Exploration with Machine Learning Get to grips with machine learning techniques to achieve sparkling-clean data quickly

Table of Contents (23) Chapters

Technical requirements

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