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Loan Status Prediction

@kaggle.bhavikjikadara_loan_status_prediction

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Predict the loan to be approved or to be rejected for an applicant.

  • In this Loan Status Prediction dataset, we have the data of applicants who previously applied for the loan based on the property which is a Property Loan.
  • The bank will decide whether to give a loan to the applicant based on some factors such as Applicant Income, Loan Amount, previous Credit History, Co-applicant Income, etc...
  • Our goal is to build a Machine Learning Model to predict the loan to be approved or to be rejected for an applicant.

About the loan_data.csv file:

  • Loan_ID: A unique loan ID.
  • Gender: Either male or female.
  • Married: Weather Married(yes) or Not Marttied(No).
  • Dependents: Number of persons depending on the client.
  • Education: Applicant Education(Graduate or Undergraduate).
  • Self_Employed: Self-employed (Yes/No).
  • ApplicantIncome: Applicant income.
  • CoapplicantIncome: Co-applicant income.
  • LoanAmount: Loan amount in thousands.
  • Loan_Amount_Term: Terms of the loan in months.
  • Credit_History: Credit history meets guidelines.
  • Property_Area: Applicants are living either Urban, Semi-Urban or Rural.
  • Loan_Status: Loan approved (Y/N).

Goal:

  • In this project, we are going to classify an individual whether he/she can get the loan amount based on his/her Income, Education, Working Experience, Loan taken previously, and many more factors.
    Let’s get more into it by looking at the data.

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