📊 PhonePe Transaction Insights Dashboard — The Beat of Digital Payments

A fully interactive Streamlit dashboard analyzing India's digital transaction landscape using PhonePe Pulse data.
Built with Python, SQL, Plotly, and Streamlit — this project delivers business insights through beautiful, data-driven visualizations.

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🚀 Project Overview

With India's rapid adoption of digital payments, understanding transaction patterns, user engagement, and market growth areas is crucial.
This dashboard connects to a MySQL database containing curated PhonePe Pulse data, enabling real-time exploration of:

• 📈 Transaction Trends
• 📱 Device Usage Insights
• 🛡 Insurance Penetration & Growth
• 🌏 Geographic Payment Patterns
• 🧩 User Engagement Strategies

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🎯 Business Case Studies Implemented

This project delivers five detailed business case analyses from the PhonePe dataset:

1. Decoding Transaction Dynamics
   Explore variations across states, quarters, and payment types to design targeted growth strategies.

2. Device Dominance & User Engagement
   Understand which device brands dominate and how that impacts app usage across regions.

3. Insurance Penetration & Growth Potential
   Identify untapped insurance markets and monitor growth trends.

4. Transaction Analysis for Market Expansion
   Spot high-potential states/districts for PhonePe’s future expansion.

5. User Engagement & Growth Strategy
   Compare app opens vs. registered users to measure engagement levels.

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🛠 Tech Stack

• Backend / Data Layer: MySQL + SQLAlchemy
• Frontend / Dashboard: Streamlit
• Data Analysis & Viz: Pandas, Plotly (Express + Graph Objects)
• Geo Mapping: Custom India GeoJSON integration
• ETL: Python scripts + SQL queries for data extraction and transformation

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📂 Project Structure

📦 phonepe-pulse-dashboard
├── app1.py                 # Main Streamlit dashboard code
├── phonepe_analysis.ipynb  # Python analysis & preprocessing
├── pysql.ipynb              # SQL queries & data extraction
├── Business Case Study.pdf # Original case study prompts
├── 1st_PhonePay.pdf         # Project specification & guidelines
├── Indian_States.geojson   # Map boundaries for visualization
└── README.md               # This file

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🔍 Key Features

• Dynamic Filters: Switch between years & quarters to see real-time data updates.
• Interactive Maps: Choropleth visualizations for state-wise metrics.
• Top N Insights: Instantly see top-performing states, districts, or device brands.
• Time Series Trends: Transaction & insurance growth over time.
• Responsive Layout: Optimized for desktop and large screens.

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⚙️ Setup Instructions

1️⃣ Clone the Repository

!git clone https://github.com/PhonePe/pulse.git

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Prepare the Database

• Install MySQL and create a database named phonepe_db.
• Import your processed PhonePe data into relevant tables:
  • aggregated_transaction
  • aggregated_insurance
  • aggregated_user
  • map_transaction
  • map_insurance
  • map_user
  • top_transaction
  • top_insurance
  • top_user

4️⃣ Run the Dashboard

streamlit run app1.py

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💡 Insights Gained

• Seasonal patterns in transaction spikes (festive quarters dominate).
• Brand affinity — certain devices dominate usage in specific states.
• Insurance adoption gap — large opportunities in underpenetrated states.
• High-growth clusters for market expansion.

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👨‍💻 Author

Deepak Manian
📍 India
💌 Reach me: LinkedIn • Email

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⭐ Acknowledgements

• PhonePe Pulse for open transaction data.
• Streamlit for making dashboards fun & fast.
• Plotly for interactive visualizations.

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If you like this project, give it a ⭐ on GitHub — it motivates me to create more awesome projects like this!