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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 6. Imputation of Missing Data, Financial Analysis, and Delivery to Client

Note

Learning Objectives

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

  • Compare the results of all models built for a case study

  • Replace missing data using a range of imputation strategies

  • Build a multiclass classification model

  • Conduct financial analysis to find the optimal threshold for binary classification

  • Derive financial insight from the model to help the client guide budgeting and operational strategy

  • Deliver the model and make recommendations for usage

Note

This chapter presents a comparison of the results from the various models built for a study, describes the financial insights derived from the final model, and outlines the final steps of the project needed to satisfy the client's requirements.

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