BANK CUSTOMER DEPOSIT PRODUCT PURCHASE ANALYSIS AND PREDICTION

This study examines the differences in characteristics between customers who purchase deposit products and those who do not, using relevant information from a bank's customer dataset. The key features analyzed include customer ID, age, occupation, marital status, credit card default history, mortgage status, contact method, last contact month and duration, the three-month interbank lending rate, previous marketing campaign results, the number of contacts before the current campaign, the number of days since the last contact, employment variation rate, consumer price index, consumer confidence index, number of employees, and whether the customer purchased a deposit product. Python is employed for descriptive analysis and classification analysis, and decision tree, logistic regression, and random forest models are used to predict whether a customer will purchase a deposit product. The analysis results reveal key factors influencing customer decisions, providing insights for banks to conduct targeted marketing within limited time frames, increase the likelihood of customer purchases, and ultimately improve overall deposit performance.