piyush0019kumar-del

piyush0019kumar-del / credit-card-fraud-detection

Public

Credit Card Fraud Detection using Python, EDA, Statistics and Machine Learning

17
0
100% credibility
Found Apr 22, 2026 at 17 stars -- GitGems finds repos before they trend. Get early access to the next one.
Sign Up Free
AI Analysis
Python
AI Summary

A demonstration script that analyzes credit card transaction data to detect fraudulent activity using exploration, statistics, and predictive models.

How It Works

1
🔍 Discover the Project

You find this helpful guide online that shows how everyday computers can spot sneaky fake credit card uses.

2
📥 Get the Files

You grab the simple instructions and example files to try it out on your own computer.

3
💻 Start the Demo

You open the files and press go, letting it look at sample shopping data.

4
📈 Watch the Magic

It draws colorful charts and numbers, revealing patterns in normal buys versus tricky ones.

5
🕵️ Spot the Fakes

The smart system flags the bad transactions and explains why with easy scores.

6
📋 Review Results

You see reports on how well it catches fraud without missing real ones.

🎉 Learn and Share

Now you understand how banks fight fraud, and you feel smart sharing it with friends.

Sign up to see the full architecture

5 more

Sign Up Free

Star Growth

See how this repo grew from 17 to 17 stars Sign Up Free
Repurpose This Repo

Repurpose is a Pro feature

Generate ready-to-use prompts for X threads, LinkedIn posts, blog posts, YouTube scripts, and more -- with full repo context baked in.

Unlock Repurpose
AI-Generated Review

What is credit-card-fraud-detection?

This GitHub project delivers a complete credit card fraud detection pipeline using Python, processing the popular credit card fraud detection dataset to spot fraudulent transactions amid heavy class imbalance. It handles data cleaning, exploratory analysis with visualizations and stats tests like Shapiro-Wilk and chi-square, then trains scikit-learn models including logistic regression, decision trees, and random forests, outputting precision, recall, F1-scores, and confusion matrices. Developers get a ready-to-run script that demonstrates an end-to-end machine learning workflow for credit card fraud detection using Python on GitHub.

Why is it gaining traction?

It stands out as a straightforward credit card fraud detection GitHub project that crams EDA, statistical analysis, and ML evaluation into one executable script, skipping boilerplate setup for quick fraud detection ML GitHub experiments. The focus on imbalance-aware metrics and feature engineering like time-to-hour conversion gives immediate insights into real-world challenges, making it a solid reference over scattered Jupyter notebooks. For credit card fraud detection using machine learning GitHub searches, its coverage of VIF for multicollinearity and probability distributions adds educational depth without complexity.

Who should use this?

Data science students building credit card fraud detection projects for portfolios or class assignments will find it perfect for replicating a full pipeline. Junior ML engineers prototyping fraud detection systems on anonymized transaction data can fork it to test custom tweaks. Analysts exploring credit card fraud detection research paper ideas through practical stats and viz get a fast-start template.

Verdict

With 17 stars and a 1.0% credibility score, this is an immature but constructive starter for learning credit card fraud detection GitHub code—run it for education, not production. Pair it with tests and deployment docs to make it viable.

(198 words)

Sign up to read the full AI review Sign Up Free

Similar repos coming soon.