Loan Application Case Study | Data Science Bootcamp | Tableau | Ivy Professional School

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Today, we are going to learn about one of the most interesting and exclusive concepts that apply everywhere in business applications: Case Studies.  A case study is an important and useful way to learn about the practical applications of business using science models. These science models are usually those which help us apply logic to business scenarios. Our very talented mentor, Eeshani Agrawal, who is the director and trainer at Ivy Professional School explains to us about the case study. Eeshani Agrawal who specializes in analytics, data science, data visualization, reporting, and automation, has her expertise in descriptive analytics. She works with data using MS-Excel, SQL, Power BI, and Tableau. 

With this Bootcamp, we want to ensure and help those students who want to try and get into data science and should be aware of what data science is. Along with that, it is also important to know how the data is being utilized. In this blog, we are going to focus on the finance industry and this video will be about focusing on hands-on training. 


Why use Tableau?

Let us understand why Tableau is being used. A tableau is a software that is used for data visualization. It is important in our industry to understand the data visually and to get insights from that data set. Looking at the data, in general, is not going to give you the business answers that you’re looking for, however, if proper techniques are used then people can create some dashboards which will give faster understanding of the data. This is one of the reasons why these tools have become very famous. The most famous tools right now in the industry are the Power BI and Tableau. In this blog, we are explaining the video that shows the analysis of a case study where there is a data set of loans. 


What is a loan?

In finance, a loan is the lending of money by one or more individuals, organizations, or other entities to other individuals, organizations etc. The recipient incurs a debt and is usually liable to pay interest on that debt until it is repaid as well as to repay the principal amount borrowed. Whether it is to buy a house, or a bike, or for education, people usually take a loan from the bank. What the bank does is, it understands your profile whether you’re married or not married, your educational status, age, income, if there’s any co-applicant, income of the co-applicant, and whether you have a credit history or not. Based on that, the bank decides whether they would want to give you a loan or not. 


Proceeding with the case study:

In this case study, we are going to look at a data set of 614 applicants. Here, we see that for the data of the 614 applicants, we have gender information. One will notice when you’re working on this data set, there are some values that are missing. In such scenarios, one treats the missing values. Let’s keep that discussion for another time as of now. So, here are some of the required fields : 

  • Gender information 
  • Marital status 
  • Dependence
  • Education 
  • Self-employed or Salaried
  • Income of the main applicant and the co-applicant (if any) 
  • Loan amount
  • Loan term
  • Credit history (if any)
  • Property area (rural or urban) 

So, from all this data we are going to understand the probability of getting a loan or not. We would be interested to find out who is applying for a loan, in terms of gender, in terms of education, whether being married or not married. As a general assumption, we think that the lower-income group has more probability of getting a loan. Since there are a lot of factors, hence visualization is going to help us understand and explore the data. We are going to see if there is a variable that is insignificant so we can remove that. We are also going to find what are the significant variables or the fields on which we can work. 

When we see this data, there are certain fields that can be easily rejected. For example, the loan ID, won’t be needed in our analysis, or this data is not going to help me to make any kind of decision. However, the other columns do matter.  In Tableau, we are going to create a dashboard and analyze how these parameters affect whether a person is getting a loan or not getting a loan and also have an understanding of what type of people are applying for them. 

Since it’s not an excel file it’s a CSV format, these are categorized as text files. Learn more about the difference here. Practice from the dataset here, and watch the video to learn more about the whole loan application case study.

Interesting, right? Tableau has become the talk of the town when it comes to visualizing data and making it useful for business applications. If you are someone who wants to get expert training on Data Science, Data Analytics, and Tableau then Ivy Professional School has come up with the best opportunities. One of them is Learn Data Science and Pay Later. Get in touch with our amazing team to learn more about how a certification in data science or any other data-related field is going to enhance your career for a long time. 

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