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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
Concepts
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Author (1):
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Michael Walker Michael Walker
Author Profile Icon Michael Walker
Michael Walker
Michael Walker has worked as a data analyst for over 30 years at a variety of educational institutions. He is currently the CIO at College Unbound in Providence, Rhode Island, in the United States. He has also taught data science, research methods, statistics, and computer programming to undergraduates since 2006.
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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

Technical requirements

In addition to the scikit-learn modules we have been using so far, we will use SMOTENC from Imbalanced-learn. We will use SMOTENC to address class imbalance. The Imbalanced-learn library can be installed with pip install -U imbalanced-learn. All the code in this chapter was tested with scikit-learn versions 0.24.2 and 1.0.2.

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Authors (1)

Profile icon Michael Walker
Michael Walker
Michael Walker has worked as a data analyst for over 30 years at a variety of educational institutions. He is currently the CIO at College Unbound in Providence, Rhode Island, in the United States. He has also taught data science, research methods, statistics, and computer programming to undergraduates since 2006.
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See other products by Michael Walker

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