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

Removing redundant or unhelpful features

During the process of data cleaning and manipulation, we often end up with data that is no longer meaningful. Perhaps we subsetted data based on a single feature value, and we have retained that feature even though it now has the same value for all observations. Or, for the subset of the data that we are using, two features have the same value. Ideally, we catch those redundancies during our data cleaning. However, if we do not catch them during that process, we can use the open source feature-engine package to help us.

Additionally, there might be features that are so highly correlated that it is very unlikely that we could build a model that could use all of them effectively. feature-engine has a method, DropCorrelatedFeatures, that makes it easy to remove a feature when it is highly correlated with another feature.

In this section, we will work with land temperature data, along with the NLS data. Note that we will only load temperature...

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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

Removing redundant or unhelpful 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

Removing redundant or unhelpful features

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