Introduction

A commercial bank that offers many banking products and services, such

as time-deposit accounts, various credit cards, personal loans, mortgages, etc. Recently, the bank is

interested in analyzing its customers spending patterns based on its credit card transaction

database. Based on this database, the bank not only knows when, where and how much a customer

spends, but it also knows certain demographics of the customer, which may contribute to

understanding the customers spending pattern and preferences. The bank wants to analyze this

dataset and use the results of this analysis in estimating the likelihood of customers spending money

where and how, which can then be used to predict customers responses to campaigns and

promotions.

As an analyst employed at the bank, your main job is to analyze the database, which consists of

customer information and the matching transaction records, and come up with descriptive statistics

as well as predictive results. Because you have taken an introductory course on Business Analytics,

you have knowledge of tools and techniques that you can use for this purpose. You have already

acquired a dataset from the banks datamart and you are now ready to use your analytics skills.

The Task

Generally speaking, there are two main tasks you would like to perform on the available dataset.

First, you would like to understand the nature of this dataset by creating a variety of descriptive

statistics and visualizations. You would like to find answers to questions such as:

- What is the distribution of customers according to the given demographics, such as age,

education, income?

- What is the distribution of spending by merchant category, customer demographics, etc.?

Secondly, you would like to run predictive models on this dataset to make estimations such as:

- To which customer characteristics are certain spending behaviors (such as weekend

shopping, evening shopping, luxurious shopping) linked to?

- What are some common groups of customers who exhibit similar demographics and/or

shopping behavior?

- If a marketing promotion in a certain product/service category is to be offered, which

customers are more likely to respond to it?

Your goal is to answer these and similar questions to the maximum possible extent using descriptive

analytics tools as well as predictive analytics concepts and tools such as regression, clustering and/or

classification models.

The Deliverables

You are given a detailed dataset that includes the following layers, which will help you seek answers

to the questions above:

- credit card transactions

- demographic profiles of customers who make these transactions

Using the information above, perform necessary analysis to answer the following questions:

Provide descriptive statistics on customer demographics and transaction activity. For

instance, for each demographic feature you can generate histograms, boxplots or any other

appropriate visual representations that provide insight into the distribution of demographic

data. Similarly, provide distributional statistics and visualizations on transaction data. For

instance, you may analyze the number of transactions and/or transaction amounts, broken

down by days of week, hours of day, category, etc. and further broken down by customer

demographics such as age, gender, marital status, etc. Your response here should be

comprehensive enough to give the reader enough insight about the nature of the dataset.

You should also create at least 1 visualization using R.

You can find the data from link below:

https://drive.google.com/file/d/1esguEqX6icO0z2QO1gosgGVcuQ_oQ3nU/view?usp=sharing