By using machine learning techniques, new outcomes for bank telemarketing could be achieved to increase bank deposits over time. For banks, obtaining time deposits is always a crucial business, and a successful marketing campaign is usually crucial to financial sales. Identifying the consumer segment for this purpose is typically a challenge for banking institutions. The data shows a class imbalance while direct telemarketing operations are not well received by customers. The economic crisis has made it difficult for banks to attract consumers. Marketing is therefore viewed as a helpful tool. The banking sector is making an effort to draw customers’ attention to recurring deposits. Knowing the company's objective, what precisely are we intending to predict in this situation? We ought to be aware of every feature, depending on your involvement or because of domain expertise. This project's primary objective is to predict the probability that a customer would sign up for an agreement to deposit (variable y) with a financial institution. Numerous procedures, including data collection, exploratory data analysis, information preprocessing, and model building, have been used to accomplish this. This is the Cat boost classifier, which uses scientific methods and low-budget resources to provide the most accurate results.