Mohammed Rashad
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PythonMLscikit-learnGradient BoostingBanking

academic project

Bank Marketing: Who to Call

problem · A bank sells term deposits by phone, and only about 12% of clients say yes. Calling everyone is expensive. Which clients are worth the call?

approach & results

  • We compared logistic regression, a decision tree, a random forest and gradient boosting in Python on 45,211 bank clients.
  • Gradient boosting ranked likely buyers best, with a test AUC of 0.9248, and its training and test scores were only 0.0028 apart, so it wasn't overfitting.
  • Recommended scoring every client and calling only the top 10%, about 4,521 people, which cuts around 90% of the calls.