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Financial Loan Decision Dataset
Finance & Banking Analytics
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About
This dataset is designed for predicting the status of loan applications, classifying whether a loan will be approved or rejected for an applicant. It contains details of individuals who have previously applied for property loans. The core purpose is to facilitate the development of machine learning models that can forecast loan outcomes based on various factors. These factors include the applicant's income, co-applicant's income, the requested loan amount, and their credit history. The ultimate goal is to build a robust model to determine if an individual qualifies for a loan based on these and other personal and financial attributes.
Columns
- Loan_ID: A unique identifier for each loan application.
- Gender: Indicates the applicant's gender (male or female).
- Married: Denotes the applicant's marital status (yes or no).
- Dependents: The number of individuals financially dependent on the applicant.
- Education: The applicant's educational background (Graduate or Undergraduate).
- Self_Employed: Indicates whether the applicant is self-employed (Yes or No).
- ApplicantIncome: The reported income of the primary applicant.
- CoapplicantIncome: The reported income of the co-applicant, if any.
- LoanAmount: The loan amount requested, specified in thousands.
- Loan_Amount_Term: The duration of the loan, expressed in months.
- Credit_History: A binary indicator showing whether the applicant's credit history meets established guidelines.
- Property_Area: The geographical area where the applicant's property is located (Urban, Semi-Urban, or Rural).
- Loan_Status: The final status of the loan application (Approved 'Y' or Rejected 'N').
Distribution
The dataset is provided as a CSV file,
loan_data.csv, and is approximately 26.28 KB in size. It comprises 13 distinct columns. The dataset typically contains around 381 records, though some columns may have a slightly lower number of valid entries due to missing values. For instance, the 'Gender' column has 376 valid entries, 'Dependents' has 373, 'Self_Employed' has 360, 'Loan_Amount_Term' has 370, and 'Credit_History' has 351 valid entries. The remaining columns have 381 valid entries.Usage
This dataset is ideally suited for:
- Developing and evaluating machine learning models for loan status prediction.
- Building classification algorithms to determine loan approval or rejection.
- Financial risk assessment and credit scoring modelling.
- Exploring the impact of various applicant demographics and financial attributes on loan outcomes.
- Educational purposes for those learning about classification, deep learning, and data cleaning in the context of finance.
Coverage
The dataset focuses on the demographic and financial attributes of loan applicants for property loans, including gender, marital status, number of dependents, education level, employment status, and income details. It also captures the property's area (Urban, Semi-Urban, Rural). No specific geographic regions or time ranges are explicitly provided within the dataset's description.
License
Attribution 4.0 International (CC BY 4.0)
Who Can Use It
This dataset is particularly useful for:
- Data scientists and machine learning engineers working on predictive analytics in the financial sector.
- Financial analysts seeking to understand factors influencing loan approval.
- Students and researchers in data science, artificial intelligence, and finance, especially those at a beginner level interested in classification problems.
- Organisations looking to automate or improve their loan approval processes.
Dataset Name Suggestions
- Loan Status Prediction Dataset
- Property Loan Approval Predictor
- Applicant Loan Status Classifier
- Financial Loan Decision Dataset
- Credit Approval Prediction Model
Attributes
Original Data Source: Financial Loan Decision Dataset
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