Bank-Marketing-Campaign-Predictive-Analysis. Problem Statement All bank marketing campaigns are dependent on customers’ huge electronic data. The size of these data sources is impossible for a human analyst to come up with interesting information that will help in the decision-making process. Data mining models are completely helping in the performance of these campaigns. The purpose is increasing the campaign effectiveness by identifying the main characteristics that affect a success (the deposit subscribed by the client) based on a handful of algorithms that we will test (e.g. Logistic Regression, Random Forests, Decision Trees and others). With the experimental results we will demonstrate the performance of the models by statistical metrics like accuracy, sensitivity, precision, recall, etc. We the higher scoring of these metrics, we will be able to judge the success of these models in predicting the best campaign contact with the clients for subscribing deposit. The aim of the marketing campaign was to get customers to subscribe to a bank term deposit product. Whether they did this or not is variable ‘y’ in the data set. The bank in question is considering how to optimize this campaign in future. What would your recommendations to the marketing manager be?