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Data Science Projects with Python

You're reading from   Data Science Projects with Python A case study approach to successful data science projects using Python, pandas, and scikit-learn

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781838551025
Length 374 pages
Edition 1st Edition
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Author (1):
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Stephen Klosterman Stephen Klosterman
Author Profile Icon Stephen Klosterman
Stephen Klosterman
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Table of Contents (9) Chapters Close

Data Science Projects with Python
Preface
1. Data Exploration and Cleaning FREE CHAPTER 2. Introduction toScikit-Learn and Model Evaluation 3. Details of Logistic Regression and Feature Exploration 4. The Bias-Variance Trade-off 5. Decision Trees and Random Forests 6. Imputation of Missing Data, Financial Analysis, and Delivery to Client Appendix

Chapter 1. Data Exploration and Cleaning

Note

Learning Objectives

By the end of this chapter, you will be able to:

  • Perform basic operations in Python

  • Describe the business context of the case study data and its suitability for the task

  • Perform data cleaning operations

  • Examine statistical summaries and visualize the case study data

  • Implement one-hot encoding on categorical variables

Note

This chapter will get you started with basic operations in Python and shows you how to perform data-related operations such as data verification, data cleaning, datatype conversion, examining statistical summaries, and more.

You have been reading a chapter from
Data Science Projects with Python
Published in: Apr 2019
Publisher: Packt
ISBN-13: 9781838551025
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