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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

2. Introduction to Scikit-Learn and Model Evaluation

Activity 2.01: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve

Solution:

  1. Use scikit-learn's train_test_split to make a new set of training and test data. This time, instead of EDUCATION, use LIMIT_BAL, the account's credit limit, as the feature.

    Execute the following code to do this:

    X_train_2, X_test_2, y_train_2, y_test_2 = train_test_split\
                                              (df['LIMIT_BAL']\
                                               ...
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