loans

This dataset is geared twords teaching about algorithmic bias, as well as some basic financial literacy.

Dataset Structure

Attribute	Type	Description
ID	Categorical/Numeric	Unique identifier for each individual entry.
Gender	Categorical	Gender of the applicant (Female, Male, Other).
Race	Categorical	Self-identified racial group (White, Hispanic, Other, etc.).
Age	Numerical	Age of the individual in years (Range: 18–82).
Age_Group	Categorical	Age categorized into specific brackets (e.g., 25–60, Over 60).
Income	Numerical	Annual income in USD (e.g., 25,000 – 175,000).
Credit_Score	Numerical	Creditworthiness score (Range: 500 – 849).
Loan_Amount	Numerical	Amount requested for the loan (e.g., 50,100 – 450,000).
Employment_Type	Categorical	Employment status (Full-time, Part-time, Other).
Education_Level	Categorical	Highest qualification (High School, Bachelor’s, Graduate, etc.).
Citizenship_Status	Categorical	Legal status (Citizen, Permanent Resident, Other).
Language_Proficiency	Categorical	Language fluency level (Fluent, Limited).
Disability_Status	Categorical	Disability indicator (True, False).
Criminal_Record	Categorical	Whether the applicant has a criminal record (True, False).
Zip_Code_Group	Categorical	Geodemographic area (e.g., High-income Suburban, Working Class Urban).
Loan_Approved	Categorical	Target variable for prediction (APPROVED or DENIED).

Lab Questions

1. Which employment type has the highest salary on average?

Dataset Source

This is a synthetic dataset for the purpose of teaching & education. It was made by Abbas Rianat and 1 collaborator. It can be found on kaggle. This dataset from kaggle was then significantly modified by Charles Bennington.

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