Option #1 Bank Marketing 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...

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Option #1Bank Marketing

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 calledbank.csv(Links to an external site.)contains 21 variables (or columns) and thedescription-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:

  1. Explore the dataset by providing summary statistics and graphical summaries of all the variables.
  2. Explain some of the key aspects of data in part 1.
  3. Examine if the dataset has any anomalies. Describe the method(s) you used as well as the results.
  4. Examine if there are any association among the variables. Describe the approaches as well as the results.
  5. Using one of the clustering techniques, analyze all the quantitative variables. Explain the results.
  6. 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.
  7. Evaluate the model. How well does the model fit? Can you improve the model? Explain.

Your paper should be 10 to 12 pages in length (not including the title page and references) and conform to theCSU Global Writing Center(Links to an external site.). Use the APA Paper Template which can be found fromAPA Paper Templates(Links to an external site.). Include at least five scholarly references in addition to the course textbook. The CSU Global Library is a good place to find these references.

Answered 3 days AfterFeb 03, 2021


Aarti answered on Feb 06 2021
50 Votes
Abstract: Banks often come up with new financial products via campaigns through phone calls. The paper is written in such a way that all the descriptive and graphical analysis is done based on individuals’ preferences given in the data. By analysing the data further, we modelled a way if a new product is launched in the market, how will customers make choices. Using the SAS studio, we have built a model to predict the success/failure rate of the launch.
There were total 21 variables, with 11 being the categorical variables and rest being numerical.
1) Summary and graphical statistics:
The frequency of all the categorical variables are as follows:
We can see from above; the maximum number of contacts are from Administrative section.
The martial status of the contacts holds more weightage for Ma
ied individuals.
Here, maximum of the individuals holds University degree.
In terms of loan, only single individual holds a credit default, more than 50% holds housing loan and only 16% holds personal loan.
We can see that in the month of May maximum contacts were made. However, it does not matter which specific day of the month an individual was contacted as all the days from Monday to Friday hold almost same weightage.
There exists only 3% success rate in the previous campaign.
Around 65% of the individuals were contacted through cellular and remaining via telephone.
Based on the above graph, there were 3 different outcomes from the previous campaign, i.e., failure, non-existent and success. However, using the bars graph, we show number of contacts made in this campaign in contrast with the previous campaign result. Also, we can see maximum of them were made to Non-Existent; aiming to involve the individuals in this campaign. Pdays refers to the number of days passed by when previous contact was made, which is maximum for the Non-Existent (i.e. sum of more 999 (no previous contact was made), which here means there all the individuals...

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