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Answer: Remove biases from the data and collect details on declined loan applications for a fuller picture., Develop a predictive model, like linear regression, to assess credit default risk., Increase the dataset size by collecting more data to enhance model accuracy.
To accurately predict default rates, it's essential to first eliminate biases and include declined applications for a comprehensive dataset. Training a predictive model, such as linear regression, on this clean data helps in identifying patterns related to defaults. Expanding the dataset further improves model reliability. While integrating social media data could offer additional insights, it's crucial to navigate privacy and regulatory considerations carefully.
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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.
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