---
title: Credit Card Fraud Detection App
emoji: 🚀
colorFrom: red
colorTo: red
sdk: docker
app_port: 8501
tags:
- streamlit
pinned: false
short_description: Streamlit template space
---
# 💳 Credit Card Fraud Detection

Real-time fraud detection using Machine Learning and an interactive Streamlit dashboard.

## 🚀 Live App
👉 [HuggingFace Space link]

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## 📌 Problem

Credit card fraud detection is a highly imbalanced classification problem where fraudulent transactions represent a very small fraction of the data.

The goal is to:

- Detect fraudulent transactions
- Minimize false negatives
- Provide real-time predictions

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## 📊 Dataset

Source: Kaggle – Credit Card Fraud Detection

### Features

The dataset contains:

- **Time** → seconds since first transaction  
- **Amount** → transaction value  
- **V1 – V28** → PCA-transformed anonymized features  

### 🔐 Why PCA?

The original transaction data contains sensitive financial information.

To preserve privacy:

- All original features were transformed using **Principal Component Analysis (PCA)**
- The resulting components are labeled **V1–V28**

These components:

- Are **not directly interpretable**
- Capture the **underlying transaction patterns**
- Retain the information needed for fraud detection

In other words:

> V1–V28 are orthogonal principal components representing the variance of the original feature space while ensuring data anonymization.

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## 🧠 Model

Baseline model trained using:

- Scaled features
- Train/test split
- ROC-AUC evaluation

### Evaluation Metric

ROC-AUC was used because:

- The dataset is highly imbalanced
- Accuracy is misleading
- AUC measures class separability

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## 🎯 Streamlit App Features

### 🔍 Prediction

- Manual transaction input
- Random transaction generator
- Fraud probability score
- Adjustable decision threshold
- Downloadable prediction report

### 📊 Model Insights

- ROC Curve
- Confusion Matrix
- AUC score
- Feature importance (tree-based models)

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## ⚙️ Tech Stack

- Python
- Scikit-learn
- Streamlit
- NumPy
- Matplotlib

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## 🧠 What I Learned

- Handling imbalanced datasets
- Why ROC-AUC is better than accuracy for fraud detection
- Feature scaling impact
- Threshold tuning for business use-cases
- Building ML dashboards for real-time inference

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## 🚀 Future Improvements

- SMOTE / class weighting
- XGBoost / LightGBM
- SHAP explainability
- Real-time API deployment

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## 👤 Author

Beyza Topbas

Machine Learning Portfolio Project