PayPulse is an end-to-end digital payment analytics platform analyzing 250K+ UPI transactions (Jan 2023 – Dec 2025). Built with Python, SQL, and Power BI, the project delivers actionable business intelligence through a 6-page interactive dashboard for executive stakeholders, merchant performance analysis, customer behavior insights, and failure diagnostics.
Live Dashboard: [https://app.powerbi.com/groups/me/reports/bcd4031b-9a69-47c3-a059-47d9d2e89504?ctid=19cae115-0ae3-4c47-a134-660fffd277d8&pbi_source=linkShare]
PayPulse simulates a real-world digital payment analytics solution for UPI and online transactions. The project follows a complete analytics pipeline:
- Data Understanding
- Data Cleaning
- Exploratory Data Analysis (EDA)
- Statistical Analysis
- Feature Engineering
- SQL Database Design
- Business Analytics using SQL
- Interactive Power BI Dashboard
PAYPULSE/
│
├── data/
│ ├── raw/
│ ├── processed/
│ └── powerbi/
│
├── notebooks/
│ ├── 01_Data_Understanding.ipynb
│ ├── 02_DataCleaning.ipynb
│ ├── 03_EDA.ipynb
│ ├── 04_Feature_Engineering.ipynb
│
├── sql/
│ ├── schema.sql
│ └── analysis/
│ ├── 01_executive_overview.sql
│ ├── 02_customer_analytics.sql
│ ├── 03_merchant_analytics.sql
│ ├── 04_transaction_trends.sql
│ ├── 05_failure_analytics.sql
│ └── 06_regional_analytics.sql
│
├── reports/
│
├── PayPulse Dashboard.pbix
│
├── README.md
│
└── requirements.txt
The project analyzes 250,001 digital payment transactions.
- Transaction ID
- Customer ID
- Merchant ID
- Bank ID
- Device ID
- Transaction Amount
- Transaction Status
- Failure Reason
- Payment Mode
- Transaction Timestamp
Raw Dataset
│
▼
Data Understanding
│
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Data Cleaning
│
▼
Exploratory Data Analysis
│
▼
Statistical Analysis
│
▼
Feature Engineering
│
▼
SQL Database
│
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SQL Business Analysis
│
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Power BI Dashboard
- Dataset Overview
- Data Types
- Missing Values
- Duplicate Records
- Descriptive Statistics
- Data Cleaning
- Missing Value Analysis
- Duplicate Removal
- Data Validation
- Data Type Conversion
- Univariate Analysis
- Bivariate Analysis
- Transaction Trends
- Merchant Analysis
- Payment Analysis
- Statistical Analysis
- Business Insights
Created analytical features including:
- Transaction Time Features
- Weekend Flag
- Time Period
- Transaction Buckets
- Customer Total Spend
- Customer Average Spend
- Merchant Revenue
- Customer Segmentation
- Success & Failure Flags
The project includes:
- Mean
- Median
- Mode
- Variance
- Standard Deviation
- Quartiles
- IQR
- Skewness
- Kurtosis
- Outlier Detection
- Distribution Analysis
- Correlation Analysis
- Confidence Intervals
- Hypothesis Testing
Database created using SQLite.
- Joins
- GROUP BY
- HAVING
- CASE WHEN
- Common Table Expressions (CTEs)
- Window Functions
- Aggregate Functions
- Ranking Functions
- Views
Business queries include:
- Executive Overview
- Customer Analytics
- Merchant Analytics
- Transaction Trends
- Failure Analysis
- Regional Performance
Interactive dashboard includes:
- Executive KPI Dashboard
- Customer Analytics
- Merchant Performance
- Regional Analysis
- Transaction Trends
- Failure Analysis
Features:
- Interactive Filters
- Drill Through
- KPI Cards
- Trend Analysis
- Dynamic Visualizations
| Category | Tools |
|---|---|
| Programming | Python |
| Notebook | Jupyter Notebook |
| Data Analysis | Pandas, NumPy |
| Visualization | Matplotlib, Seaborn |
| Database | SQLite |
| SQL | SQLite SQL |
| BI Tool | Power BI |
| Version Control | Git & GitHub |
- Identified high-value customer segments.
- Measured transaction success and failure rates.
- Ranked merchants based on transaction revenue.
- Analyzed regional transaction performance.
- Identified common transaction failure reasons.
- Explored payment mode usage and customer behavior.
- Generated business recommendations from analytical findings.
- Data Cleaning
- Exploratory Data Analysis
- Statistical Analysis
- Feature Engineering
- SQL Analytics
- Database Design
- Data Visualization
- Dashboard Development
- Business Intelligence
- Data Storytelling
Add screenshots of your Power BI dashboard here.
Abhishek Rawat