Jaya0925

Jaya0925 / digital-payment-trends-India

Public

A complete data science project analyzing India's digital payment trends (2016–2025) using Python. Covers EDA, cleaning, 9 visualizations, and ML models (Linear Regression & Random Forest) to predict UPI transaction volume. R² = 0.99

16
0
100% credibility
Found Apr 16, 2026 at 16 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Jupyter Notebook
AI Summary

This project provides a complete data analysis of India's digital payment trends from 2016 to 2025, featuring visualizations, statistics, and machine learning predictions for UPI transaction volumes.

How It Works

1
🔍 Discover the Project

You stumble upon this interesting study about how digital payments like phone apps have exploded in India over the years.

2
📥 Download the Files

Grab the main analysis file and the data spreadsheet to your computer so you can explore it yourself.

3
📂 Open the Analysis

Launch the easy-to-use analysis guide in your web browser or simple app.

4
▶️ Run the Whole Story

Hit the button to go through everything step by step, and watch the data load, clean up, and reveal patterns automatically.

5
📊 See the Pictures

Enjoy nine colorful charts showing payment growth, top methods like UPI, and big trends from 2016 to now.

6
🔮 Check Smart Predictions

Discover forecasts for future UPI transactions using clever pattern-matching that beats simple guesses.

🎉 Master the Trends

You've now got a clear picture of India's digital payment boom, from tiny starts to world-leading success.

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Star Growth

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AI-Generated Review

What is digital-payment-trends-India?

This Jupyter Notebook project delivers a full data science workflow on India's digital payment trends from 2016 to 2025, tracking 11 modes like UPI, NEFT, and cards via Python tools for EDA, data cleaning, nine trend visualizations, and ML models predicting UPI transaction volume with 0.99 R² accuracy using Random Forest. It pulls real RBI and NPCI data to reveal explosive UPI growth from zero to 90 lakh transactions monthly, helping users forecast fintech shifts without hunting datasets. Run it locally after pip-installing basics like pandas, scikit-learn, and matplotlib for instant insights.

Why is it gaining traction?

It stands out as a complete data science machine learning bootcamp from basics to advanced in one repo, covering EDA to production-ready predictions on timely digital payment trends India February 2025 data—unlike scattered tutorials or toy datasets. Developers grab it for the high-fidelity Random Forest outperforming linear models, plus ready visualizations on YoY growth and market shares that hook anyone building fintech dashboards. As a complete GitHub project, it skips setup headaches for quick portfolio wins.

Who should use this?

Data science students in bootcamps like Krish Naik's complete data analyst course need it for hands-on pipelines from raw CSV to ML forecasts. Fintech analysts tracking UPI dominance or payment index correlations will reference its trends for reports. Junior devs wanting a complete data analytics course project to demo at interviews.

Verdict

Solid educational starter with clean docs and runnable pipeline, but 16 stars and 1.0% credibility score signal it's an academic assignment—not battle-tested production code. Use for learning digital payment trends India; skip for real apps until more contributions.

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