- Mastering Deep Learning is not just about knowing the intuition and tools, it’s also about being able to apply these models to real-world scenarios and derive actual measurable results for the business or project. That’s why I decided to do this project
#1 Churn Modelling Problem
I will be solving a data analytics challenge for a bank.
What I need:
- A dataset with a large sample of the bank’s customers.
- To make this dataset, the bank gathered information such as customer id, credit score, gender, age, tenure, balance, if the customer is active, has a credit card, etc.
Scenario: During a period of 6 months, the bank observed if these customers left or stayed in the bank.
My goal is to make an Artificial Neural Network that can predict, based on geo-demographical and transactional information given above, if any individual customer will leave the bank or stay (customer churn). Also, I’m asked to rank all the customers of the bank, based on their probability of leaving. To do that, I will need to use the right Deep Learning model, one that is based on a probabilistic approach.
If Isucceed in this project, I will create significant added value to the bank. By applying my Deep Learning model the bank may significantly reduce customer churn.