Loan Approval Prediction

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Overview

Loan approval prediction refers to the use of machine learning techniques to predict the likelihood of a loan application being approved or denied by banks and financial institutions. By using advanced algorithms and predictive models, banks can streamline their loan approval processes and make informed decisions for the benefit of both lenders and borrowers.

The dataset provided here contains the following columns:

• Loan_ID: Unique identifier for each loan application
• Gender: Indicates the gender of the applicant
• Married: Indicates whether the applicant is married or not
• Dependents: Indicates the number of dependents of the applicant
• Education: Indicates the educational background of the applicant
• Self_Employed: Indicates whether the applicant is self-employed or not
• ApplicantIncome: Indicates the income of the applicant
• CoapplicantIncome: Indicates the income of the co-applicant
• LoanAmount: Indicates the loan amount requested by the applicant
• Loan_Amount_Term: Indicates the term of the loan in months
• Credit_History: Indicates the credit history of the applicant
• Property_Area: Indicates the location of the property
• Loan_Status: Indicates whether the loan was approved or not

Objectives

Create a model that can accurately predict whether a loan request will be approved or denied, based on the details provided by the applicant. The model should have high accuracy in determining loan approval outcomes.