A retail company wants to predict future customer churn. What type of predictive model would be most appropriate?
- Clustering
- Decision Trees
- Logistic Regression
- Time Series Analysis
Time Series Analysis would be most appropriate for predicting future customer churn in a retail context. This model considers the temporal aspects of data, allowing the company to identify patterns and trends over time that may indicate potential churn. Logistic Regression, Decision Trees, and Clustering are valuable for different scenarios but may not be the best fit for predicting time-dependent events like churn.
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