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Industry Use Case
Predict Customer Attrition Using Naïve Bayes Classification
This case study exposes the learner to the concept of Naive bayes and how it can be used for making predictions in real time.
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Difficulty: AdvancedDesigned for those with a technical background or industry experience
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Difficulty: Approximately 3 hours
Course Overview
- A group of supervised learning algorithms known as Naive Bayes methods utilize Bayes’ theorem with the “Naive” assumption that each pair of features is conditionally independent given the value of the class variable. It is useful for making predictions and forecasting data based on historical results.
- The rise of fintech businesses, which are threatening banks’ dominant role in society, is the result of new technology, legislation, and changes in demand.
- When a client departs or terminates their relationship with a business during a specific time frame, this is known as customer attrition or customer churn. Customer attrition can be a serious threat to already established businesses during periods of increased competition.
- Learn how different ML algorithms can be used by banks to make strategies during marketing campaigns and provide better customer experiences.
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