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You are a bank employee with a labeled dataset of approved loan applications, including whether they resulted in defaults. Your goal is to develop a model to predict default rates for future applicants. What steps should you take?
A
Increase the dataset size by collecting more data to enhance model accuracy.
B
Develop a predictive model, like linear regression, to assess credit default risk.
C
Remove biases from the data and collect details on declined loan applications for a fuller picture.
D
Integrate applicants' social media profiles with their loan applications for better feature engineering.
