Google Professional Data Engineer
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You are a bank employee with access to a labeled dataset containing information on approved loan applications and their default outcomes. Your objective is to develop a model to predict default rates for future credit applicants. What steps should you take to accomplish this?
Explanation:
To predict default rates effectively, training a predictive model on your labeled dataset is essential. Classification models, such as logistic regression or decision trees, are suitable for this task. These models can categorize loan applications into default or non-default based on features like the applicant's credit history, income, and employment status.