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

View More author details

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

Binary classification with logistic regression

Logistic regression is often used to model health outcomes when the target is binary, such as whether the person gets a disease or not. We will go through an example of that in this section. We will build a model to predict if an individual will have heart disease based on personal characteristics such as smoking and alcohol drinking habits; health features, including BMI, asthma, diabetes, and skin cancer; and age.

Note

In this chapter, we will work exclusively with data on heart disease that’s available for public download at https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease. This dataset is derived from the United States Center for Disease Control data on more than 400,000 individuals from 2020. Data columns include whether respondents ever had heart disease, body mass index, ever smoked, heavy alcohol drinking, age, diabetes, and kidney disease. We will work with a 30,000 individual sample...

The rest of the chapter is locked

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

Binary classification with logistic regression

Authors (1)

Personalised recommendations for you

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

Binary classification with logistic regression

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you