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

Using grouped boxplots to view bivariate relationships between continuous and categorical features

Grouped boxplots are an underappreciated visualization. They are helpful when we're examining the relationship between continuous and categorical features since they show how the distribution of a continuous feature can vary by the values of the categorical feature.

We can explore this by returning to the National Longitudinal Survey (NLS) data we worked with in the previous chapter. The NLS has one observation per survey respondent but collects annual data on education and employment (data for each year is captured in different columns).

Data Note

As stated in Chapter 1, Examining the Distribution of Features and Targets, the NLS of Youth is conducted by the United States Bureau of Labor Statistics. Separate files for SPSS, Stata, and SAS can be downloaded from the respective repository. The NLS data can be downloaded from 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

Using grouped boxplots to view bivariate relationships between continuous and categorical features

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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

Using grouped boxplots to view bivariate relationships between continuous and categorical features

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