💸 UPI Transaction Analysis – Power BI Dashboard
UPI Transaction Analysis is a comprehensive Power BI project that delivers in-depth insights into the Unified Payments Interface (UPI) ecosystem. It leverages data cleaning, transformation, and visualization to uncover transaction patterns, user behavior, and geographic distribution.
🔎 The project focuses on: ✔️ Transaction volumes & values over time ✔️ Distribution by payment type (P2M, P2P, etc.) ✔️ Geographic distribution of transactions ✔️ Top transacting entities (banks, merchants) ✔️ Peak transaction periods
🎯 Goal: Provide stakeholders with actionable insights into UPI dynamics and trends.
👥 Team Members
| Name | Role | Responsibilities |
|---|---|---|
| Vaibhav Pandey | Data Analyst | Data Cleaning, Power BI Dashboard Development |
🧹 Data Cleaning & Preparation
Data Source: Raw UPI transaction data (CSV, Excel)
Key Steps:
🗑️ Handling missing values in transaction amount, date, status
🔄 Standardizing date & time formats
❌ Removing duplicates
📊 Transforming data types for optimized Power BI analysis
➕ Creating derived columns (transaction month, day of week) for deeper insights
🏗️ Data Modeling & Visualization
Power BI Data Model:
🔗 Relationships between tables (transactions, merchants, users)
📐 DAX Measures for KPIs: Total Transaction Value, Avg Transaction Amount, Growth Rates
Interactive Dashboards:
📌 Overview Page: High-level KPIs – total transactions, values, trends
⏳ Time Series Analysis: Trends by day, week, month
🗺️ Geographic Distribution: Heatmaps of transaction density across states/regions
💳 Payment Type Breakdown: P2M, P2P, and other categories
🏦 Top Entities: Banks, merchants, and users ranked by volume & value
💡 Key Findings & Visualizations
📊 Remaining Balance by Customer Age: Bar/line charts showing liquidity trends across demographics
⏱️ Transaction Amount Over Time:
Weekly patterns & fluctuations
Daily peaks & troughs
Monthly seasonal variations
🧾 Matrix Representation:
Aggregated values (total transactions, avg amount, unique users)
Cross-tabulated by payment type, location, merchant category
🧰 Tools & Technologies
📊 Power BI – Data Modeling, DAX, Interactive Dashboards
🧹 Power Query – Data Cleaning & Transformation
📑 Excel/CSV – Source dataset
💻 Git & GitHub – Version control & documentation
📫 Contact
💡 For queries or collaboration, feel free to connect:
🔥 “Empowering Digital Payment Insights through Data & Analytics.”