Analyzing 42,500 loans via Pandas and Python

Analyzing 42,500 loans via Pandas and Python

Every modern bank can be compared to a huge analytical machine. Banks store and process enormous amounts of information. They know everything about people’s income, their property, their priorities. And they have such information not only about their borrowers, but also about those who have already taken loans from other banks.

Banking analytics is a powerful tool for modern banks to make data-driven decisions and provide great customer service. By collecting, analyzing and interpreting large amounts of financial data, banks can gain valuable insights about their customers’ needs and trends in the banking industry.

They can then use this information to develop more efficient products and services, create better user experiences, reduce fraud and improve risk management. With the help of this technology, banks can make smart and timely decisions that improve their operations while keeping up with the ever-changing world.

Analyzing banking data is always interesting. I always discover something valuable in this kind of data. I found myself in possession of a dataset with an impersonal data of 42500 loans of one American bank. And I immediately decided to investigate it with my favorite tools: Pandas and Python.

I was interested in analyzing the following points:

  • How much money do people usually borrow in the United States?
  • What monthly payment do people focus on?
  • How much do Americans owe on all loans?
  • What affects the interest rate on loans?
  • Does credit rating affect the interest rate?
  • Does a borrower’s annual income affect whether they get a loan at a lower rate?
  • Does seniority affect the interest rate?
  • Does ownership of real estate affect the interest rate?
  • Is it easier for rich people to get a loan?
  • How much risk do banks take? How many of their loans are bad loans?

And I found the answers to all these questions. At the link below you will find them, too.


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