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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
Author (1):
Stephen Klosterman Stephen Klosterman
Profile icon Stephen Klosterman
Toc

Table of Contents (9) Chapters close

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

Data Quality Assurance and Exploration

So far, we remedied two data quality issues just by asking basic questions or by looking at the .info() summary. Let's now take a look at the first few columns of data. Before we get to the historical bill payments, we have the credit limits of the LIMIT_BAL accounts, and the SEX, EDUCATION, MARRIAGE, and AGE demographic features. Our business partner has reached out to us, to let us know that gender should not be used to predict credit-worthiness, as this is unethical by their standards. So we keep this in mind for future reference. Now we'll explore the rest of these columns, making any corrections that are necessary.

In order to further explore the data, we will use histograms. Histograms are a good way to visualize data that is on a continuous scale, such as currency amounts and ages. A histogram groups similar values into bins and shows the number of data points in these bins as a bar graph.

To plot histograms, we will start...

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