Banks often market their financial products through phone calls. Assume that you are a data mining expert who works in the marketing campaign team at a bank. You are tasked with analyzing their marketing data to better understand future customers. The dataset called bank.csv (Links to an external site.) contains 21 variables (or columns) and the description-bank.docx (Links to an external site.) contains a description of the dataset. The end goal is to build an appropriate model (or tool) to successfully predict whether a potential client will subscribe a term deposit or not. Using SAS Studio, perform the following tasks:
Explore the dataset by providing summary statistics and graphical summaries of all the variables.
Explain some of the key aspects of data in part 1.
Examine if the dataset has any anomalies. Describe the method(s) you used as well as the results.
Examine if there are any association among the variables. Describe the approaches as well as the results.
Using one of the clustering techniques, analyze all the quantitative variables. Explain the results.
Using one of the classification techniques from the course, build the model that predicts whether the client will subscribe. Explain why you think the model you’ve chosen is most appropriate for this dataset.
Evaluate the model. How well does the model fit? Can you improve the model? Explain.