I am struggling to find the 'statistics' in this scenario. A classification problem like this must have at least the confusion matrix showing false positives/ false negatives. Most importantly SAC must not decide which classifier/regression to use, it must be the data scientist's job to pick the right classifier based on business needs.
Merely piping data into a model and getting out attributes is certainly not statistics. In order to solve such problems ideally the test data must be piped to various classifiers, results interpreted and the best classifier picked with a strongly worded judgement as to why such a classifier was picked.

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