⭐ Key Project for Data Analyst Portfolio
This project analyzes banking transaction data to understand customer financial behavior, transaction patterns, and overall business performance. It focuses on identifying high-value customers, analyzing branch performance, and evaluating loan approval trends.
To derive insights that help improve revenue generation, manage financial risk, and support better decision-making in banking operations.
- Python (Pandas)
- MySQL (SQL Queries)
- Power BI (Dashboard)
The dataset contains banking transaction records including customer details, account types, transaction amounts, branch information, loan status, and transaction channels.
- Data cleaning and preprocessing using Python (Pandas)
- Handled missing values and removed duplicate records
- Converted date column for time-based analysis
- Performed grouping and aggregation for customer and transaction insights
- Wrote SQL queries to analyze transaction value, top customers, and branch performance
- Built interactive Power BI dashboard to visualize key financial metrics
- Total Transaction Value: ₹12.59M
- Average Transaction Value: ₹25.18K
- Savings accounts contributed the highest transaction volume
- A small group of customers contributed a large portion of total revenue
- Delhi branch showed strong performance compared to others
- Loan approvals and rejections indicate controlled risk management
This analysis helps banks identify high-value customers, improve targeting strategies, optimize branch performance, and make better loan approval decisions based on data insights.
Power BI dashboard showcasing transaction trends, customer segmentation, loan status distribution, and branch-wise performance.