Exploratory Data Analysis (EDA) was performed to uncover patterns, detect outliers, and identify variables with strong predictive potential for personal loan acceptance.

🧮 Dataset Overview

• Total Records: 5,000
• Target Variable: Personal_Loan (0 = No, 1 = Yes)
• Loan Acceptance Rate: ~9.6%
• Key Features: Age, Income, CCAvg, Education, Mortgage, Account types, Credit card usage, Online banking

📈 Distribution Insights

💰 Income vs Loan Acceptance

• Clear positive correlation between income and likelihood of loan acceptance.
• Most accepted loans come from customers earning $80K+ annually.

💳 CCAvg (Monthly Credit Card Spend)

• Customers spending $3K+ per month are significantly more likely to accept loans.
• Indicates a lifestyle or need for credit that aligns with loan interest.

🎓 Education Level

• Education coded as:
  • 1 = Undergraduate
  • 2 = Graduate
  • 3 = Advanced/Professional
• Customers with Graduate or Advanced education show higher loan acceptance rates.

🏦 Account Types

• CD Account ownership is a strong indicator of loan acceptance.