Starting with a dataset that has details about loan default data inAmazon Simple Storage Service(Amazon S3), we use SageMaker Canvas to gain insights about the data. We then perform feature engineering to apply transformations such as encoding categoric...
We then perform feature engineering to apply transformations such as encoding categorical features, dropping features that are not needed, and more. Next, we store the cleansed data back in Amazon S3. We use the cleaned dataset to create a classifi...
In sum, PASA must be practical. But public executives need to take seriously public value, and specifically social efficiency, when engaging in PASA. Unless they do so, their strategic analyses will not have normative legitimacy because enhancing public value is not the same as in some versions...