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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
Oswald Campesato (San Francisco, CA) is an adjunct instructor at UC-Santa Cruz and specializes in Deep Learning, NLP, Android, and Python. He is the author/co-author of over forty-five books including Data Science Fundamentals Pocket Primer, Python 3 for Machine Learning, and the Python Pocket Primer (Mercury Learning and Information).
2: Outlier and Anomaly Detection
3: Cleaning Data Sets
4: Working with Models
5: Matplotlib and Seaborn
Appendix: Working with awk
Index