Credit Card Regression

This dataset contains information related to individuals' financial and demographic characteristics, which can be useful for various analyses, such as credit scoring, customer segmentation, or financial behavior studies. The dataset includes the following columns:

• ID: A unique identifier for each individual.
• Income: The individual's annual income (in thousands of dollars).
• Limit: The credit limit assigned to the individual's credit card account.
• Rating: A credit rating score for the individual.
• Cards: The number of credit cards the individual possesses.
• Age: The age of the individual.
• Education: The number of years of education completed by the individual.
• Gender: The gender of the individual (Male/Female).
• Student: Whether the individual is a student (Yes/No).
• Married: The marital status of the individual (Yes/No).
• Ethnicity: The ethnic background of the individual.
• Balance: The current balance on the individual's credit card account.

Key Insights

• Income and Credit Limit: Examining the relationship between income levels and the assigned credit limits.
• Credit Rating: Understanding how various factors such as income, number of cards, and education influence credit ratings.
• Demographic Analysis: Analyzing differences in financial behavior and credit characteristics across different genders, marital statuses, and ethnic backgrounds.
• Age and Financial Behavior: Investigating how financial behavior, such as balance and credit card usage, changes with age.

Potential Applications

• Credit Risk Modeling: Develop models to predict credit risk based on demographic and financial data.
• Customer Segmentation: Identify different customer segments to tailor financial products and marketing strategies.
• Financial Education Programs: Design targeted financial education programs for specific demographics to improve financial literacy and behavior.

This dataset provides a comprehensive view of various factors that can influence an individual's financial behavior and creditworthiness, making it a valuable resource for financial analysis and modeling.