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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

Different Types of Data Science Problems

Much of your time as a data scientist is likely to be spent wrangling data: figuring out how to get it, getting it, examining it, making sure it's correct and complete, and joining it with other types of data. pandas is a widely used tool for data analysis in Python, and it can facilitate the data exploration process for you, as we will see in this chapter. However, one of the key goals of this book is to start you on your journey to becoming a machine learning data scientist, for which you will need to master the art and science of predictive modeling. This means using a mathematical model, or idealized mathematical formulation, to learn relationships within the data, in the hope of making accurate and useful predictions when new data comes in.

For predictive modeling use cases, data is typically organized in a tabular structure, with features and a response variable. For example, if you want to predict the price of a house based on...

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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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