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🛡️ Fraud Detection in Financial Transactions
This project uses Machine Learning to detect fraud in financial transactions, based on the PaySim dataset.
📊 Dataset
The data simulates bank transactions and includes columns such as:
amount: the transaction amounttransaction_type: transfer or withdrawalsourceanddestination: involved accountsisFraud: indicates whether the transaction is fraudulent (1) or not (0)
📈 Results
We trained a Random Forest model, achieving the following evaluation metrics:
| Class | Precision | Recall | F1-Score | Support |
|---|---|---|---|---|
| Non-Fraud (0) | 1.00 | 1.00 | 1.00 | 1,270,904 |
| Fraud (1) | 0.98 | 0.79 | 0.87 | 1,620 |
Accuracy: 1.00
Macro Average: 0.99 | 0.89 | 0.94
Weighted Average: 1.00 | 1.00 | 1.00
🚀 Test the Model
Access the Hugging Face Space to test fraud detection interactively:
🔗 Test Here
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