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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

Product type Paperback

Published in Apr 2019

Publisher Packt

ISBN-13 9781838551025

Length 374 pages

Edition 1st Edition

Languages

Python

Tools

NumPy

Concepts

Data Science

Author (1):

Stephen Klosterman

View More author details

Table of Contents (9) Chapters

Data Science Projects with Python

Preface

1. Data Exploration and Cleaning

2. Introduction toScikit-Learn and Model Evaluation FREE CHAPTER

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.

The rest of the chapter is locked

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Authors (1)

Stephen Klosterman

Stephen Klosterman

Stephen Klosterman is a Machine Learning Data Scientist with a background in math, environmental science, and ecology. His education includes a Ph.D. in Biology from Harvard University, where he was an assistant teacher of the Data Science course. His professional experience includes work in the environmental, health care, and financial sectors. At work, he likes to research and develop machine learning solutions that create value, and that stakeholders understand. In his spare time, he enjoys running, biking, paddleboarding, and music.

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