Practice Case Study 1: Customer Churn Prediction in Telecom - 17.3 | 17. Case Studies and Real-World Projects | Data Science Advance
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

Define customer churn in your own words.

💡 Hint: Think about what happens when customers stop using a service.

Question 2

Easy

What does SMOTE stand for?

💡 Hint: Consider the need to handle minority classes in data.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What is customer churn?

💡 Hint: Think about customer retention.

Question 2

What technique was used to tackle data imbalance?

  • SMOTE
  • Logistic Regression
  • Random Forests

💡 Hint: Consider which method addresses class representation in data.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Analyze the implications of a predictive model that fails to account for imbalanced data. How could that affect a telecom company's retention strategy?

💡 Hint: Consider the costs associated with misallocated retention efforts.

Question 2

Provide an example of how SHAP outputs could be presented in a business meeting to help non-technical stakeholders understand customer churn predictions.

💡 Hint: Think about visual representation and storytelling to convey data effectively.

Challenge and get performance evaluation