Managing Datasets and Models

Paperback
March 2023
9781683929529
More details
  • Publisher
    Mercury Learning and Information
  • Published
    1st March 2023
  • ISBN 9781683929529
  • Language English
  • Pages 368 pp.
  • Size 7" x 9"
$54.95
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February 2023
9781683929505
More details
  • Publisher
    Mercury Learning and Information
  • Published
    27th February 2023
  • ISBN 9781683929505
  • Language English
  • Pages 368 pp.
  • Size 7" x 9"
$54.95
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February 2023
9781683929512
More details
  • Publisher
    Mercury Learning and Information
  • Published
    27th February 2023
  • ISBN 9781683929512
  • Language English
  • Pages 368 pp.
  • Size 7" x 9"
$139.95

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

1: Working with Data
2: Outlier and Anomaly Detection
3: Cleaning Data Sets
4: Working with Models
5: Matplotlib and Seaborn
Appendix: Working with awk
Index

Oswald Campesato

Oswald Campesato specializes in Deep Learning, Python, Data Science, and generative AI. He is the author/co-author of over forty books including Google Gemini for Python, Data Cleaning, and GPT-4 for Developers (all Mercury Learning).

Python-based code; kNN algorithm; model; dataset; anomaly detection; visualization; Sweetviz; Skimpy; Matplotlib; Seaborn; data analysis