Tools: Excel. Link to full project

Challenge: A fictional bank wants to integrate a data mining algorithm into their operations to identify which customers are most likely to stop using their services. A maximum of four risk factors were to be identified and ranked, and the model presented for evaluation as part of CRISP-DM methodology. Cleaned dataset and addressed PI contained, conducted exploratory data analysis to identify meaningful differences between customers staying and leaving the bank. Ranked risk factors and designed a decision tree model for classification of customers, based on age, membership status, gender, and country of residence.