Reference no: EM133496016

In Tableau. The included dataset below is a sample of the actual loans serviced by Lending Club during a particular period. You'll notice that one of the worksheets in the workbook provides a data dictionary to explain what each of the variables refers to. Please note that you do NOT need to use every variable. Rather, explore the variables and pick a handful that play a significant role in defining the clusters. Your goal is to group customers based on what you know about them BEFORE they ever get a Lending Club loan. In other words, do not use any of the variables representing information about their current loan. Create the clusters, then you will use that cluster variable to create new visualizations that explain differences in loan_status and the types of loans they typically get (e.g., loan amount, term, interest rate).

Using the Lending Club data below, "tell another story" in Tableau by walking through several alternative cluster analyses among the available variables.

Save your favorite cluster as a variable back into the list of Dimensions in Tableau.

Next, created at least 3 viz's that use the new cluster variable to see how it differentiates among their Lending Tree loan types (amount, term, interest rate) and/or their loan status (current, late, etc)

Next, create a story that explains what we should expect from each cluster. Explain those visualizations you created and why each cluster has the effect on loan type and status represented.