Google Professional Machine Learning Engineer
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A financial services company is embarking on a project to develop a machine learning model aimed at predicting customer churn with high accuracy, scalability, and compliance with stringent financial regulations. The company possesses extensive customer data, including detailed transaction histories and records of customer service interactions. Given these constraints and objectives, which of the following best describes the primary and secondary objectives of this project? Choose the best two options.
A
To rigorously evaluate the model's performance using comprehensive metrics such as accuracy, recall, and precision, ensuring it aligns with the business's high standards and regulatory requirements.
B
To indiscriminately collect and utilize all available customer data, including highly sensitive financial information, without implementing necessary safeguards or considering data privacy laws.
C
To accurately predict which customers are at risk of churning, thereby enabling the company to deploy targeted and effective retention strategies that comply with financial regulations.
D
To meticulously preprocess the data by normalizing and encoding features, ensuring the model's input is optimized for training while adhering to data privacy standards.
E
Both A and C are correct because evaluating model performance is as critical as making accurate predictions for the project's success, especially in a regulated financial environment.