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Data Science Projects with Python

You're reading from   Data Science Projects with Python A case study approach to gaining valuable insights from real data with machine learning

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Product type Paperback
Published in Jul 2021
Publisher Packt
ISBN-13 9781800564480
Length 432 pages
Edition 2nd Edition
Languages
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Author (1):
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Stephen Klosterman Stephen Klosterman
Author Profile Icon Stephen Klosterman
Stephen Klosterman
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Toc

Table of Contents (9) Chapters Close

Preface
1. Data Exploration and Cleaning 2. Introduction to Scikit-Learn and Model Evaluation FREE CHAPTER 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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