This is an individual assignment and will be a chance for you to perform an applied data science project on a real data set

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Introduction

This is an individual assignment and will be a chance for you to perform an applied data science project on a real data set.

We will be working with the credit_card_df data frame in this project. This data set contains information on over 4,000 customers of a U.S. bank. The description of this data and the variables contained in it are provided below.

The objective of this project is to explore the factors that lead to customers canceling their credit card accounts and develop machine learning algorithms that will predict the likelihood of a customer canceling their account in the future.

To complete this assignment, students must download the R notebook template and open the file in their RStudio application. Please click the button below to download the template.

Credit Card Account Data

The credit_card_df data frame contains information on the customers of a large U.S. bank which provides a number of financial services including multiple credit card offerings.

The bank is looking to see if it can determine the factors that lead to customers canceling their credit card account and whether it can predict if a customer will cancel their account in the future.

To maintain profits, banks must maximize the number of customers with credit lines. It is also in their best interests for customers to carry large credit card balances from month-to-month to maximize revenue from interest charges.

The bank has experienced record levels of customers closing their credit accounts in the past couple of years and this is leading to declining revenue.

The bank’s goal is to become better at identifying customers at risk of canceling their account to minimize financial losses.

Specifically, the broad questions that the company is trying to answer include:

• What are the factors that are associated with customers closing their credit card accounts?
• Is it possible to predict whether a customer will close their account? If so, how accurate are the predictions?
• How many costly errors is the model expected to produce?
• Are there any actions or policies the company can implement to reduce the risk of losing their customers?

 

The data set contains a mixture of customer demographics and their financial behavior.

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