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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

Loading the Case Study Data with Jupyter and pandas

Now it's time to take a first look at the data we will use in our case study. We won't do anything in this section other than ensure that we can load the data into a Jupyter notebook correctly. Examining the data, and understanding the problem you will solve with it, will come later.

The data file is an Excel spreadsheet called default_of_credit_card_clients__courseware_version_1_21_19.xls. We recommend you first open the spreadsheet in Excel or the spreadsheet program of your choice. Note the number of rows and columns. Look at some example values. This will help you know whether or not you have loaded it correctly in the Jupyter notebook.

Note

The dataset can be obtained from the following link: https://packt.link/wensZ. This is a modified version of the original dataset, which has been sourced from the UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School...

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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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