Payment Analytics

A portfolio project for analyzing payment transaction data using SQL and Python.

Overview

This project demonstrates a typical payment analytics workflow using synthetic transaction data.

The analysis includes:

• Total payment volume
• Commission revenue
• Active users
• Average transaction amount
• Transaction success rate
• Category-level analysis
• Daily transaction dynamics
• Top users by transaction volume

Dataset

The project uses a synthetic dataset:

transactions.csv

Main fields:

• transaction_id
• user_id
• transaction_date
• category
• amount
• commission
• status

All data in this repository is synthetic and created specifically for demonstration purposes.

SQL Analysis

The file analysis.sql contains SQL queries for:

• Total transaction volume
• Total commission revenue
• Transaction count by status
• Active users
• Average transaction amount
• Category analysis
• Daily transaction dynamics
• Top users by payment volume
• Transaction success rate

Python Analysis

The file payment_analysis.py performs the same type of analytical processing using Python and pandas.

The script:

• Loads transaction data
• Filters successful transactions
• Calculates key KPIs
• Builds category-level analysis
• Analyzes daily transaction dynamics
• Identifies top users by payment volume
• Saves analytical results to Excel

Output

The Python script generates:

payment_analysis_results.xlsx

The workbook contains four sheets:

KPIs

Main business metrics:

• Total Payment Volume
• Total Commission Revenue
• Active Users
• Average Transaction Amount
• Success Rate

Categories

Analysis by transaction category:

• Transaction count
• Total amount
• Total commission
• Average transaction amount

Daily Dynamics

Daily transaction performance:

• Transaction count
• Daily payment volume
• Daily commission

Top Users

User-level analysis based on transaction volume.

Tech Stack

• SQL
• Python
• pandas
• openpyxl
• Microsoft Excel

Project Structure

• transactions.csv — synthetic transaction dataset
• analysis.sql — SQL analytical queries
• payment_analysis.py — Python analytical script
• payment_analysis_results.xlsx — generated analytical report
• requirements.txt — Python dependencies
• README.md — project documentation

How to Run

1. Install dependencies

pip install -r requirements.txt

2. Make sure the transaction dataset is in the project folder

transactions.csv

3. Run the Python analysis

python payment_analysis.py

4. Check the generated report

payment_analysis_results.xlsx

Skills Demonstrated

• SQL analytics
• Python data analysis
• Payment analytics
• Transaction analysis
• KPI calculation
• Data aggregation
• Product analytics
• Financial analytics
• Excel reporting

Data Privacy

All transaction data used in this repository is synthetic.

The repository does not contain confidential, customer, production, or employer data.

Future Improvements

• Add monthly and weekly metrics
• Add MAU / DAU analysis
• Add retention metrics
• Add transaction segmentation
• Add automated visualizations
• Add anomaly detection
• Add Power BI dashboard