https://github.com/obirikan/ml_model_fraud_detection

This project demonstrates how to use Logistic Regression to detect fraudulent transactions using SMOTE for an imbalanced data
https://github.com/obirikan/ml_model_fraud_detection

imbalanced-data logistic-regression smote-oversampler

Last synced: 12 months ago
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This project demonstrates how to use Logistic Regression to detect fraudulent transactions using SMOTE for an imbalanced data

• Host: GitHub
• URL: https://github.com/obirikan/ml_model_fraud_detection
• Owner: obirikan
• Created: 2025-06-27T14:31:39.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-07-01T14:35:54.000Z (12 months ago)
• Last Synced: 2025-07-01T15:40:19.550Z (12 months ago)
• Topics: imbalanced-data, logistic-regression, smote-oversampler
• Language: Jupyter Notebook
• Homepage:
• Size: 6.11 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: readme.md

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README

          

# 💳 Logistic Regression Fraud Detection 

This dataset provides a small but representative sample of anonymized financial transactions intended for building and testing **fraud detection models**.

Each record represents a **single transaction**, including:

- Transaction type (e.g., `CASH_OUT`, `TRANSFER`)

- Transaction amount

- Sender and receiver account balances before and after the transaction

- Fraud indicator flags

It is suitable for:

- Binary classification

- Anomaly detection

- Machine learning tasks related to **financial security**

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## 📦 Dataset Structure

| Column Name     | Description                                                  |

|------------------|--------------------------------------------------------------|

| `step`           | Time step of the transaction                                 |

| `type`           | Type of transaction (e.g., `TRANSFER`, `CASH_OUT`)           |

| `amount`         | Amount involved in the transaction                           |

| `nameOrig`       | ID of sender account                                         |

| `oldbalanceOrg`  | Sender’s balance before the transaction                      |

| `newbalanceOrig` | Sender’s balance after the transaction                       |

| `nameDest`       | ID of receiver account                                       |

| `oldbalanceDest` | Receiver’s balance before the transaction                    |

| `newbalanceDest` | Receiver’s balance after the transaction                     |

| `isFraud`        | **Target variable**: 1 if fraudulent, 0 otherwise            |

| `isPayment`      | Indicates if the transaction is a payment                    |

| `isMovement`     | Indicates if it involved a balance change                    |

| `accountDiff`    | Difference in account balances (derived feature)             |

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## ⚠️ Class Imbalance Notice

> **Important:**  

> This dataset is **highly imbalanced** — the number of fraudulent transactions (`isFraud = 1`) is much lower compared to non-fraudulent ones.  

> This reflects real-world financial data and may affect model performance if not handled properly.

To improve results, consider:

- **Resampling techniques** like SMOTE or undersampling

- Using **evaluation metrics** like precision, recall, F1-score, or ROC-AUC instead of just accuracy

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## 💡 Inspiration

This dataset can help you explore:

- How fraud differs from legitimate behavior

- Techniques to detect rare but critical patterns

- How to evaluate models fairly when fraud is rare

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