Here is the url to access this challenge
https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/#About
Dream Housing Finance company deals in all kinds of home loans. They have presence across all urban, semi urban and rural areas. Customer first applies for home loan and after that company validates the customer eligibility for loan.
Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have provided a dataset to identify the customers segments that are eligible for loan amount so that they can specifically target these customers.
Data Dictionary
Train file: CSV containing the customers for whom loan eligibility is known as 'Loan_Status' Variable Description Loan_ID Unique Loan ID Gender Male/ Female Married Applicant married (Y/N) Dependents Number of dependents Education Applicant Education (Graduate/ Under Graduate) Self_Employed Self employed (Y/N) ApplicantIncome Applicant income CoapplicantIncome Coapplicant income LoanAmount Loan amount in thousands Loan_Amount_Term Term of loan in months Credit_History credit history meets guidelines Property_Area Urban/ Semi Urban/ Rural Loan_Status (Target) Loan approved (Y/N)
Test file: CSV containing the customer information for whom loan eligibility is to be predicted Variable Description Loan_ID Unique Loan ID Gender Male/ Female Married Applicant married (Y/N) Dependents Number of dependents Education Applicant Education (Graduate/ Under Graduate) Self_Employed Self employed (Y/N) ApplicantIncome Applicant income CoapplicantIncome Coapplicant income LoanAmount Loan amount in thousands Loan_Amount_Term Term of loan in months Credit_History credit history meets guidelines Property_Area Urban/ Semi Urban/ Rural
Submission file format Variable Description Loan_ID Unique Loan ID Loan_Status (Target) Loan approved (Y/N)