{"id":29561704,"url":"https://github.com/obirikan/ml_model_fraud_detection","last_synced_at":"2025-07-18T16:39:37.520Z","repository":{"id":302292008,"uuid":"1009682196","full_name":"obirikan/ML_Model_Fraud_Detection","owner":"obirikan","description":"This project demonstrates how to use Logistic Regression to detect fraudulent transactions using SMOTE for an imbalanced data","archived":false,"fork":false,"pushed_at":"2025-07-01T14:35:54.000Z","size":6407,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-07-01T15:40:19.550Z","etag":null,"topics":["imbalanced-data","logistic-regression","smote-oversampler"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# 💳 Logistic Regression Fraud Detection \n\nThis dataset provides a small but representative sample of anonymized financial transactions intended for building and testing **fraud detection models**.\n\nEach record represents a **single transaction**, including:\n- Transaction type (e.g., `CASH_OUT`, `TRANSFER`)\n- Transaction amount\n- Sender and receiver account balances before and after the transaction\n- Fraud indicator flags\n\nIt is suitable for:\n- Binary classification\n- Anomaly detection\n- Machine learning tasks related to **financial security**\n\n---\n\n## 📦 Dataset Structure\n\n| Column Name     | Description                                                  |\n|------------------|--------------------------------------------------------------|\n| `step`           | Time step of the transaction                                 |\n| `type`           | Type of transaction (e.g., `TRANSFER`, `CASH_OUT`)           |\n| `amount`         | Amount involved in the transaction                           |\n| `nameOrig`       | ID of sender account                                         |\n| `oldbalanceOrg`  | Sender’s balance before the transaction                      |\n| `newbalanceOrig` | Sender’s balance after the transaction                       |\n| `nameDest`       | ID of receiver account                                       |\n| `oldbalanceDest` | Receiver’s balance before the transaction                    |\n| `newbalanceDest` | Receiver’s balance after the transaction                     |\n| `isFraud`        | **Target variable**: 1 if fraudulent, 0 otherwise            |\n| `isPayment`      | Indicates if the transaction is a payment                    |\n| `isMovement`     | Indicates if it involved a balance change                    |\n| `accountDiff`    | Difference in account balances (derived feature)             |\n\n---\n\n## ⚠️ Class Imbalance Notice\n\n\u003e **Important:**  \n\u003e This dataset is **highly imbalanced** — the number of fraudulent transactions (`isFraud = 1`) is much lower compared to non-fraudulent ones.  \n\u003e This reflects real-world financial data and may affect model performance if not handled properly.\n\nTo improve results, consider:\n- **Resampling techniques** like SMOTE or undersampling\n- Using **evaluation metrics** like precision, recall, F1-score, or ROC-AUC instead of just accuracy\n\n---\n\n## 💡 Inspiration\n\nThis dataset can help you explore:\n- How fraud differs from legitimate behavior\n- Techniques to detect rare but critical patterns\n- How to evaluate models fairly when fraud is rare\n\n---\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fobirikan%2Fml_model_fraud_detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fobirikan%2Fml_model_fraud_detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fobirikan%2Fml_model_fraud_detection/lists"}