Srinikesh18

Srinikesh18 / Credit-Card-Fraud-Detection-ML

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End-to-end Credit Card Fraud Detection using Machine Learning with SMOTE, Random Forest, and ROC-AUC evaluation.

49
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100% credibility
Found Feb 11, 2026 at 27 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 repository offers a complete machine learning example for detecting fraudulent credit card transactions on a public dataset, using techniques to handle rare fraud cases and evaluating models with clear charts.

How It Works

1
🔍 Discover the Fraud Buster

You find this handy guide online while looking for ways to spot fake credit card charges using smart pattern recognition.

2
📥 Grab the Starter Files

You download the simple example files to get everything set up on your computer.

3
📊 Add Real Transaction Examples

You pick up a sample set of everyday purchases, including a few sneaky frauds, and place it with your files.

4
🧠 Train Your Smart Detector

You follow the easy steps to teach the tool how to recognize normal buys from tricky frauds, balancing everything just right.

5
📈 Check the Magic Results

You see colorful charts showing how well it catches fraud without too many false alarms.

6
✅ Get Your Ready Detector

Your fraud-spotting helper is now trained and saved, ready to check new transactions anytime.

🎉 Fraud Detection Mastered

You feel like a pro with your own working tool that nails fraud detection perfectly.

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Star Growth

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AI-Generated Review

What is Credit-Card-Fraud-Detection-ML?

This Jupyter Notebook project delivers an end-to-end credit card fraud detection ML solution using Python, Scikit-learn, and imbalanced-learn. It processes the Kaggle creditcardfraud dataset from MLG-ULB—284k transactions with just 0.17% fraud—to train models like Random Forest, balancing classes via SMOTE for better detection. Run it after pip-installing requirements and dropping in the CSV; you get a saved model file, confusion matrices, and ROC-AUC scores around 0.98.

Why is it gaining traction?

It shines in handling real-world imbalance without fluff, offering a clean pipeline that trains multiple credit card fraud detection ML algorithms and spits out visual evals plus a pickle-ready model. Developers grab it for the reproducible setup and portfolio punch—far cleaner than scattered notebooks. The focus on ROC-AUC and high recall hooks those benchmarking fraud models.

Who should use this?

ML beginners tackling imbalanced classification on the classic credit card fraud detection using ML project. Data science students needing a quick end-to-end credit card fraud detection ML GitHub demo for resumes. Analysts prototyping fraud detectors before scaling to XGBoost or APIs.

Verdict

Grab this 12-star credit card fraud detection ML project as a solid starter for learning—docs are clear, setup is straightforward—but its 1.0% credibility score flags it as unproven for production. Fork, tune, and deploy to make it yours.

(178 words)

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