https://github.com/djdurga/upi-transactions-eda

The dataset provided encompasses a comprehensive record of Unified Payments Interface (UPI) transactions spanning the period from January 2023 to December 2023. Unified Payments Interface (UPI) has emerged as a prominent digital payment system in India, facilitating seamless fund transfers between bank accounts instantly.
https://github.com/djdurga/upi-transactions-eda

matplotlib numpy pandas python

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The dataset provided encompasses a comprehensive record of Unified Payments Interface (UPI) transactions spanning the period from January 2023 to December 2023. Unified Payments Interface (UPI) has emerged as a prominent digital payment system in India, facilitating seamless fund transfers between bank accounts instantly.

• Host: GitHub
• URL: https://github.com/djdurga/upi-transactions-eda
• Owner: Djdurga
• Created: 2025-03-16T11:51:39.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-03-16T11:59:59.000Z (over 1 year ago)
• Last Synced: 2025-03-16T12:39:19.951Z (over 1 year ago)
• Topics: matplotlib, numpy, pandas, python
• Language: Jupyter Notebook
• Homepage:
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# UPI Transaction Analysis

## 📌 Overview

This repository contains an analysis of **Unified Payments Interface (UPI) transactions** from **January 2023 to December 2023**. UPI is a widely used digital payment system in India that enables instant **fund transfers between bank accounts**.

## 📂 Dataset Description

The dataset includes UPI transaction records with the following attributes:

### **Columns**

- 🗓 **Date**: Date of the transaction.

- 🏷 **Category**: Type of transaction (e.g., groceries, utilities, transportation).

- 🔢 **RefNo**: Transaction reference number for tracking.

- 💰 **Withdrawal**: Amount debited from the account.

- 💵 **Deposit**: Amount credited to the account.

- 🏦 **Balance**: Account balance after the transaction.

---

## 📊 **Key Insights**

### 🔴 **Withdrawals**

- **🏠 Rent**:

  - High, consistent withdrawals, indicating a fixed monthly expense.

  - Minimal variance (stable spending pattern).

- **🔀 Miscellaneous (Misc)**:

  - **Highest variance** in withdrawals, meaning large unpredictable expenses.

  - Includes **big purchases, emergency spending, or one-time transactions**.

  - **Pie & bar charts** show that Misc accounts for a large portion of spending, requiring better categorization.

### 🟢 **Deposits**

- **💼 Salary**:

  - The most consistent and predictable deposit source.

  - Low variance, reinforcing **salary as a stable income stream**.

- **🔄 Miscellaneous Deposits**:

  - Includes **irregular income** such as refunds, bonuses, or transfers.

  - Pie charts show a **large portion of deposits fall under Misc**, indicating a need for better income classification.

### 🔥 **Correlation Heatmap Analysis**

- **Withdrawal vs Deposit (-0.03)** ➝ No significant relationship; deposits and withdrawals occur **independently**.

- **Withdrawal vs Balance (0.17)** ➝ Weak positive correlation, suggesting some withdrawals are followed by deposits.

- **Deposit vs Balance (0.37)** ➝ **Moderate positive correlation**, meaning **higher deposits lead to a stable account balance**.

---

## ✅ **Key Takeaways**

- **🏠 Rent is a predictable fixed expense**, while **Miscellaneous transactions require further classification**.

- **💼 Salary deposits ensure financial stability**, but irregular income (Misc) needs monitoring.

- **📈 Deposits have a stronger impact on balance than reducing withdrawals**.

- **💳 Tracking irregular transactions** can help in **better financial planning**.

---

## 📌 **Conclusion & Recommendations**

- **Categorizing Misc transactions** more precisely will improve spending & income tracking.

- **Stabilizing deposits** (such as salary) can help maintain a healthier balance.

- **Reducing unnecessary withdrawals** in the Misc category will lead to better savings.

- A **detailed classification of transactions** will enhance financial planning.

---

## 🚀 **Next Steps**

- Implement **automated transaction categorization** using machine learning.

- Create a **dashboard for real-time expense tracking**.

- Analyze **seasonal spending patterns** to optimize financial planning.

This analysis provides a **data-driven perspective on UPI transactions**, helping users understand spending patterns and optimize financial decisions. 📊💰