Dark Mode
Housing Finance Applicant Data
Comodities & Real Estate
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This dataset is designed to help automate the home loan eligibility process for a finance company. It contains customer details typically gathered during an online loan application, such as gender, marital status, education, number of dependents, income, requested loan amount, and credit history. The primary purpose of this dataset is to enable the identification of customer segments eligible for loan amounts, allowing the company to specifically target these customers. It can be used for building machine learning models to predict loan approval in real-time.
Columns
- Loan_ID: A unique identifier for each loan application.
- Gender: Indicates the applicant's gender (Male/Female).
- Married: Specifies whether the applicant is married (Yes/No).
- Dependents: Represents the number of dependents the applicant has.
- Education: Describes the applicant's educational background (Graduate/Not Graduate).
- Self_Employed: Indicates if the applicant is self-employed (Yes/No).
- ApplicantIncome: The income of the primary applicant.
- CoapplicantIncome: The income of the co-applicant, if any.
- LoanAmount: The loan amount requested by the applicant, expressed in thousands.
- Loan_Amount_Term: The term of the loan in months.
- Credit_History: Signifies whether the applicant's credit history meets the required guidelines (e.g., 0 for no, 1 for yes).
- Property_Area: Categorises the property's location (Urban/Semiurban/Rural).
Distribution
The dataset is provided as a CSV file, named
loan_sanction_test.csv, and is approximately 21.96 kB in size. It comprises 12 distinct columns and contains 367 records. There are missing values across several columns including Gender, Dependents, Self_Employed, LoanAmount, Loan_Amount_Term, and Credit_History.Usage
This dataset is ideal for:
- Developing and training machine learning classification algorithms to predict loan eligibility.
- Performing exploratory data analysis to understand the factors influencing loan approvals.
- Creating data visualisations to represent patterns and insights within loan application data.
- Automating real-time loan eligibility checks for financial institutions.
- Identifying and segmenting customers based on their eligibility for home loans.
Coverage
The data pertains to home loan applicants across urban, semi-urban, and rural areas. It includes demographic information such as gender, marital status, education level, and number of dependents, alongside financial details like income, loan amount, and credit history. This is a partial dataset, which may imply it does not cover all possible scenarios or a complete population. No specific time range for the data collection is provided.
License
CC0: Public Domain
Who Can Use It
This dataset is highly valuable for:
- Data Analysts and Scientists: For building predictive models, conducting statistical analysis, and generating reports on loan eligibility.
- Financial Institutions/Banks: To streamline and automate their loan approval processes, reduce manual effort, and improve decision-making.
- Researchers: To study factors affecting loan applications and credit risk.
- Students and Educators: As a practical example for learning about data analytics, machine learning, and classification problems in the finance sector.
Dataset Name Suggestions
- Home Loan Eligibility Prediction
- Automated Loan Approval Dataset
- Housing Finance Applicant Data
- Loan Eligibility Assessment
Attributes
Original Data Source: Housing Finance Applicant Data
QUALITY