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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