Loan Default Analysis

Multi-file data processing with Pandas - Joins, DateTime, and String operations

📊 Project Overview

Analysis of loan default risk using customer and loan data from multiple CSV files. Implements data joining, temporal analysis, and string cleaning operations.

🎯 Features Implemented

Multi-File Joins

• Read multiple CSV files (customers.csv, loans.csv)
• Join customer + loan data on customer_id
• Inner, left, and right joins
• Handle missing values

DateTime Operations

• Convert string dates to datetime
• Extract date components (year, month, day)
• Filter data by date ranges
• Calculate date differences
• Find recent loans (last 6 months)

String Operations

• Clean city names: .str.upper()
• Remove whitespace: .str.strip()
• Pattern matching: .str.contains()
• Split full names
• Replace values: 'Bombay' → 'Mumbai'

DTI Calculation

• DTI ratio = (loan_emi / monthly_income) * 100
• Identify high-risk customers (DTI > 40%)
• Group by credit score ranges

🛠️ Technologies

• Python 3.x
• Pandas
• NumPy

📈 Key Concepts

✅ Multi-file joining (merge operations)
✅ DateTime handling (.dt accessor)
✅ String manipulation (.str accessor)
✅ Missing data handling
✅ Feature calculation (DTI ratio)

📂 Dataset

• customers.csv: 2000 records
• loans.csv: 2000 records