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Some well-known bank has been observing a lot of customers closing their accounts or switching to competitor banks over the past couple of quarters.
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This has caused a huge dent in their quarterly revenues and might drastically affect annual revenues for the ongoing financial year, causing stocks to plunge and market cap to reduce significantly.
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The idea is to be able to predict which customers are going to churn so that necessary actions/interventions can be taken by the bank to retain such customers.
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To develop a solution for this churn prediction problem I have been using customer data pertaining to his past transactions with the bank and some demographic information.
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I have used this customer data to establish relations/associations between data features & customers' propensity to churn by building a classification model to predict whether the customer will leave the bank or not.
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Furthermore, with an objective of explaning model predictions, I have built multiple visualisations which give insight into which factors are responsible for the churn of the customers.