Using machine learning to predict which insurance products customers of Zimnat are likely to purchase
Zimnat wants an ML model to use customer data to predict which kinds of insurance products to recommend to customers. The company has provided data on nearly 40,000 customers who have purchased two or more insurance products from Zimnat.
Your challenge: for around 10,000 customers in the test set, you are given all but one of the products they own, and are asked to make predictions around which products are most likely to be the missing product. This same model can then be applied to any customer to identify insurance products that might be useful to them given their current profile.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
What things you need to install the software and how to install them.
Give examples
A step by step series of examples that tell you how to get a development env running.
Say what the step will be
Give the example
And repeat
until finished
End with an example of getting some data out of the system or using it for a little demo.
Explain how to run the automated tests for this system.
Explain what these tests test and why
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Explain what these tests test and why
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Add notes about how to use the system.
Add additional notes about how to deploy this on a live system.
- @kylelobo - Idea & Initial work
See also the list of contributors who participated in this project.
- Hat tip to anyone whose code was used
- Inspiration
- References