The dataset called bank.csv (attached as an XLSX file) contains 21 variables (or columns) and the
description-bank.docx 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 (knowledge of SAS is a must) perform the following tasks:
1. Examine info.docx and state if there are any anomalies. Describe the method(s) you used as well as the
results.
2. Examine if there are any association among the variables. Describe the approaches as well as the results.
3 – Using one of the clustering techniques, analyze all the quantitative variables. Explain the results.
4 – Using one of the SAS classification techniques to 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.
5 – Evaluate the model. How well does the model fit? Can you improve the model? Explain.
Paper is due 11/26/20 11:00am EST
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