Practice Case Study 1: Customer Churn Prediction In Telecom (17.3) - Case Studies and Real-World Projects

Practice: Case Study 1: Customer Churn Prediction in Telecom

Lesson 17.3Case Studies and Real-World Projects

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10 questions

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6 sample questions

Question 1.
Define customer churn in your own words.
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Think about what happens when customers stop using a service.
Question 2.
What does SMOTE stand for?
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Consider the need to handle minority classes in data.
3Question 3.
What is customer churn?
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Think about customer retention.
4Question 4.
What technique was used to tackle data imbalance?
- SMOTE
- Logistic Regression
- Random Forests
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Consider which method addresses class representation in data.
Question 5.
Analyze the implications of a predictive model that fails to account for imbalanced data. How could that affect a telecom company's retention strategy?
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Consider the costs associated with misallocated retention efforts.
Question 6.
Provide an example of how SHAP outputs could be presented in a business meeting to help non-technical stakeholders understand customer churn predictions.
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Think about visual representation and storytelling to convey data effectively.

6 more questions in this section. Enroll free to practise them all.