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A retail company is planning to develop a machine learning model to predict customer churn as part of its strategy to improve customer retention. The project is in its initial phase, and the team is evaluating the critical steps necessary to ensure the model's success. Given the constraints of data privacy regulations, the necessity for scalable data processing, and the imperative for accurate predictions, which of the following steps is the most critical initial phase to guarantee the model's success? (Choose one correct option)
A
Model evaluation to assess the accuracy of predictions, ensuring the model meets the business objectives before deployment.
B
Feature engineering to create meaningful predictors from raw data, focusing on transforming data into formats that better represent the underlying problem to the predictive models.
C
Data collection to gather comprehensive customer interaction data, ensuring the dataset is representative, clean, and complies with data privacy regulations.
D
Hyperparameter tuning to optimize the model's performance, focusing on adjusting the model parameters to improve prediction accuracy.