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FintechPaymentsEDAPython

UPI Transaction Analysis

UPI Payments EDA — 15,000+ Transactions

In-depth exploratory analysis of 15,000+ UPI transactions revealing payment patterns, failure reasons, popular app usage, transaction type distribution, merchant category trends, and time-based activity. Key findings: 85%+ success rate, insufficient balance is the top failure reason, evenings are peak hours, and P2P is the most common transaction type.

Type
Data Analysis
Category
Analytics & BI
Year
2025
Stack
Python · pandas
UPI Transaction Analysis
At a Glance
15K+
Transactions
85%+
Success rate
P2P
Top type
Evening
Peak hours
What's Built

Key features & capabilities.

01

Transaction status breakdown: success, failure, and pending rates

02

Failure reason analysis — insufficient balance is the leading cause

03

UPI app popularity and app-level success rate comparison

04

Hourly transaction volume heatmap — 6–9 PM peak period identified

05

Top merchant categories: Food and Shopping dominate

06

City-level transaction volume ranking

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