Loan Status Prediction

• 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.