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Synthetic Loan Eligibility Analysis Dataset
Finance & Banking Analytics
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£79.99
About
This synthetic loan dataset has been created as an educational resource for data science, machine learning, and financial analytics applications. The data focuses on key variables related to loan eligibility and approval, such as income levels, loan amounts, education, and property details. It is designed to provide learners and practitioners with an opportunity to explore and analyze loan-related patterns, perform predictive modelling, and improve decision-making in the context of financial services.
Dataset Features:
- Gender: Gender of the applicant (e.g., "Male," "Female").
- Married: Marital status of the applicant (e.g., "Yes," "No").
- Dependents: Number of dependents the applicant has (e.g., "0," "1," "2," "3+").
- Education: Education level of the applicant (e.g., "Graduate," "Not Graduate").
- Self_Employed: Whether the applicant is self-employed (e.g., "Yes," "No").
- ApplicantIncome: Income of the applicant (in monetary units).
- CoapplicantIncome: Income of the co-applicant (in monetary units).
- LoanAmount: Loan amount requested (in monetary units).
- Loan_Amount_Term: Term of the loan (in months).
- Property_Area: Area of the property (e.g., "Urban," "Semiurban," "Rural").
- Loan_Status: Loan approval status (e.g., "Y" for approved, "N" for not approved).
Distribution:
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Usage:
This dataset is useful for a variety of applications, including:
- Financial Analysis: To explore trends in loan eligibility and approval based on demographic and financial factors.
- Educational Training: To practice data cleaning, transformation, and visualization techniques specific to financial datasets.
- Predictive Modeling: To develop models that predict loan approval outcomes based on applicant information and loan parameters.
- Decision Support: To identify key features that influence loan approval and enhance decision-making for financial institutions.
Coverage:
This dataset is synthetic and anonymized, ensuring that it is safe to use for experimentation and learning without exposing any real personal or financial information.
License:
CCO (Public Domain)
Who can use it:
Data science enthusiasts: For learning and practising financial data analysis and predictive modelling.
Researchers and educators: For academic studies or teaching purposes in finance and data science.
Finance professionals: For exploring trends in loan eligibility and approval, and improving risk assessment processes.
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