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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

7. Test Set Analysis, Financial Insights, and Delivery to the Client

Activity 7.01: Deriving Financial Insights

Solution:

  1. Using the testing set, calculate the cost of all defaults if there were no counseling program.

    Use this code for the calculation:

    cost_of_defaults = np.sum(y_test_all * X_test_all[:,5])
    cost_of_defaults 

    The output should be this:

    60587763.0
  2. Calculate by what percent the cost of defaults can be decreased by the counseling program.

    The potential decrease in the cost of default is the greatest possible net savings of the counseling program, divided by the cost of all defaults in the absence of a program:

    net_savings[max_savings_ix]/cost_of_defaults

    The output should be this:

    0.2214260658542551

    Results indicate that we can decrease the cost of defaults by 22% using a counseling program, guided by predictive modeling.

  3. Calculate the net savings per account (considering all accounts it might be possible to counsel, in other words relative to the whole...
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