Data Cleaning and Exploration with Machine Learning

Table of Contents

Preface

Section 1 – Data Cleaning and Machine Learning Algorithms

Chapter 1: Examining the Distribution of Features and Targets

Technical requirements

Subsetting data

Generating frequencies for categorical features

Generating summary statistics for continuous and discrete features

Identifying extreme values and outliers in univariate analysis

Using histograms, boxplots, and violin plots to examine the distribution of features

Using histograms

Using boxplots

Using violin plots

Summary

Chapter 2: Examining Bivariate and Multivariate Relationships between Features and Targets

Technical requirements

Identifying outliers and extreme values in bivariate relationships

Using scatter plots to view bivariate relationships between continuous ...