Data Mining for Profitability | Part 2 – Data Mining for Making Lending Decisions
SUMMARY
There is a global demand for business professionals who can use business data and analytics to make informed business decisions. Business professionals need to have knowledge in statistical analysis and business analytics for improving profitability and efficiency. A good business analyst will be able to interpret data, conduct data mining, find key values from the data, find relationships between variables, determine the best predictors, and interpret key findings.
Scenario:
Lending Club (opens in new window) https://www.lendingclub.com/ is a peer-to-peer lending company based in California that offers loans with a variety of interest rates. The loans are funded by people all over the country. Lending Club connects people who need a loan to people who have extra money to lend, resulting in a borrowing process that is streamlined and stress-free. The company uses key variables to determine who is approved for loans; and while its criteria have led to a lot of success for the company, it has also seen losses in terms of delinquency and loan defaults.
The company hopes to reduce loan defaults and delinquency by creating a more effective customer profile for giving loans. You are a data/business analyst for Lending Club and are tasked with identifying a more effective set of variables for providing loans to consumers. You will need to use the Lending Club Data Dictionary [attached copy] and collect all the Consumers’ Loan Data [attached copy], clean and analyze the data using the Microsoft-R statistical tool, and build a model showcasing the key (predictive) variables to creating a more sustainable and profitable business. In the end, you will make recommendations to key business decision makers on how to meet the company’s business objectives.
Part 1: Write an overview of the Lending Club’s current business strategy. Describe and analyze the company’s market position. Discuss the factors informing the business objective of expanding the current customer base. (The first part of this paper is attached for reference)
Part 2: Data Mining for Making Lending Decisions
Based on the key variables identified in Part 1: Overview of the Lending Club Company, conduct data analysis, identify the predictors to achieving the business objective, and write a report of the key findings.
Perform data mining by conducting the tasks below using Microsoft – R:
Collect the company’s data Consumers’ Loan Data [DOWNLOAD] and perform data cleaning. (attached)
If necessary, use the Lending Club Data Dictionary [DOWNLOAD] as a guide to understand the variables from the loan data. (attahed)
Analyze the data through regression to determine the key variables relevant to the business objective.
Create and test a data model based on the key variables identified.
Use the model to perform correlation and multiple regression analyses of the company’s data.
Write a report that addresses the factors/issues/concerns listed below:
Interpretation of the data
What data visualizations from the analysis (histogram, pie chart, etc.) are you including to support your interpretation?
What are the greatest predictors for achieving the business objective?
Discussion of data issues and considerations
How was the integrity of the data maintained?
How was research bias identified and/or avoided during the data analysis?
Were there any other ethical concerns during the collection, analysis, and interpretation that needed to be addressed? How was it addressed?
Assignment Requirements
Summary of key customer profile variables using data modeling and regression analysis
Justification of variables based on data analysis
Evaluation of data analysis issues
Note: This assignment will be checked for plagiarism. Always review the University’s Academic Integrity Policy statement regarding plagiarism.
REQUIREMENTS
Citation Requirements: minimum of 2
Word Count: 750-1000
APA Formatting
Plagiarism Submission
Last Completed Projects
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