Dataset: Loan Status Prediction

About this Dataset

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.

Tables

Loan Data

@kaggle.bhavikjikadara_loan_status_prediction.loan_data

15.73 KB
381 rows
13 columns


CREATE TABLE loan_data (
  "loan_id" VARCHAR,
  "gender" VARCHAR,
  "married" VARCHAR,
  "dependents" VARCHAR,
  "education" VARCHAR,
  "self_employed" VARCHAR,
  "applicantincome" BIGINT,
  "coapplicantincome" DOUBLE,
  "loanamount" DOUBLE,
  "loan_amount_term" DOUBLE,
  "credit_history" DOUBLE,
  "property_area" VARCHAR,
  "loan_status" VARCHAR
);