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MACHINE LEARNING | LOAN PREDICTION WEB APPLICATION

ARE YOU GETTING THE LOAN?

In this post we are going to discuss the application of machine learning in the financial organization.

Are you getting the loan is a machine learning web app to predict whether the loan will be approved or not based on the details provided by customers? This web app is build using streamlit. Streamlit is a popular open-source framework used for model deployment by machine learning and data science teams. And the best part is it’s free of cost and purely in python.

Understanding the problem statement

"Automate the loan eligibility process based on customer details provided while filling online application form".

The machine learning model for this particular web application is build using the loan dataset available in Kaggle. We will not go deeper into the model building and training part as these steps are already discussed in our previous articles.

Loan Prediction App

For this particular web app, we have six variables. These are Gender, Martial Status, ApplicantIncome, LoanAmount, Credit_History, Property_Area and stored them in variable x. Loan_Status is our output, stored in variable y. first thing we need to do is convert the categorical variables into numerical variables. Some of our input variables are categorical example Gender, are you Married, Property_Area, Credit_History. the code for this conversion is shown below.

Machine learning loan prediction python program


At the front-end side of this app it will ask applicant for these six input variables. Gender and Martial Status can be selected using radio buttons. Applicant has to enter his monthly income and amount of loan applying for. Then he needs to select his property area among Rural, Urban, Semiurban and also need to select Credit History (Unclear Debts or No Unclear Depts). After filling all the details applicant needs to click the predict button.

Two most important factors for this app are Amount of loan and Income of Applicant.

Hypothesis:

  • Higher the amount of loan, the lesser will be the chances of loan approval and vice versa.
  • A higher income will lead to higher probability of loan approval.

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If the customer satisfies the required hypothesis the loan will get approved or else it will be rejected. This is shown below the predict button. 

Predicted loan status using machine learning







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