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Data Cleaning and Exploration with Machine Learning

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

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Product type Paperback
Published in Aug 2022
Publisher Packt
ISBN-13 9781803241678
Length 542 pages
Edition 1st Edition
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Author (1):
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Michael Walker Michael Walker
Author Profile Icon Michael Walker
Michael Walker
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Table of Contents (23) Chapters Close

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

K-nearest neighbors regression

As mentioned previously, K-nearest neighbors can be a good alternative to linear regression when the assumptions of ordinary least squares do not hold, and the number of observations and dimensions is small. It is also very easy to specify, so even if we do not use it for our final model, it can be valuable for diagnostic purposes.

In this section, we will use KNN to build a model of the ratio of female to male incomes at the level of country. We will base this on labor force participation rates, educational attainment, teenage birth frequency, and female participation in politics at the highest level. This is a good dataset to experiment with because the small sample size and feature space mean that it is not likely to tax your system’s resources. The small number of features also makes it easier to interpret. The drawback is that it might be hard to find significant results. That being said, let’s see what we find.

Note

We will...

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