Bank term-deposit campaign targeting

The brief

A bank's call centre phones thousands of customers to sell term deposits, and most say no. Build a model that ranks customers by how likely they are to subscribe, and show how many calls (and how much cost) it saves. Good looks like: a lift chart showing better targeting, and a campaign plan with expected subscriptions and cost.

What you will build

1. Explore subscription by customer and campaign features
2. Remove features that would not be known before the call
3. Train and compare classifiers
4. Build lift and gain charts
5. Write a campaign plan with the savings

Free, open data to build it with

• Bank Marketing ↗ UCI Machine Learning Repository

Check each dataset's licence before you publish your work. Your mentor gives your team its own version of the brief (data, scope or region), so every team's project is its own.