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Data Science Projects with Python - Second Edition

You're reading from Data Science Projects with Python - Second Edition

Product type Book

Published in Jul 2021

Publisher Packt

ISBN-13 9781800564480

Pages 432 pages

Edition 2nd Edition

Languages

Concepts

Author (1):

Stephen Klosterman

Table of Contents (9) Chapters

Preface

1. Data Exploration and Cleaning

2. Introduction to Scikit-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. Gradient Boosting, XGBoost, and SHAP Values

7. Test Set Analysis, Financial Insights, and Delivery to the Client

Appendix

Python and the Anaconda Package Management System

In this book, we will use the Python programming language. Python is a top language for data science and is one of the fastest-growing programming languages. A commonly cited reason for Python's popularity is that it is easy to learn. If you have Python experience, that's great; however, if you have experience with other languages, such as C, Matlab, or R, you shouldn't have much trouble using Python. You should be familiar with the general constructs of computer programming to get the most out of this book. Examples of such constructs are for loops and if statements that guide the control flow of a program. No matter what language you have used, you are likely familiar with these constructs, which you will also find in Python.

A key feature of Python that is different from some other languages is that it is zero-indexed; in other words, the first element of an ordered collection has an index of 0. Python also...

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Authors (1)

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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