This book contains a fast-paced introduction to data-related tasks in preparation for training models on
datasets. It presents a step-by-step, Python-based code sample that uses the kNN algorithm to manage a model on a dataset. Chapter One begins with an introduction to datasets and issues that can arise, followed by Chapter Two on outliers and anomaly detection. The next chapter explores ways for handling missing data and invalid data, and Chapter Four demonstrates how to train models with classification algorithms. Chapter 5 introduces visualization toolkits, such as Sweetviz, Skimpy, Matplotlib, and Seaborn, along with some simple Python-based code samples that render charts and graphs. An appendix includes some basics on using awk. Companion files with code, datasets, and figures are available for downloading.
FEATURES:

Covers extensive topics related to cleaning datasets and working with models

Includes Python-based code samples and a separate chapter on Matplotlib and Seaborn

Features companion files with source code, datasets, and figures from the book

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