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license: apache-2.0 |
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language: |
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- en |
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- fr |
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metrics: |
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- accuracy |
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- f1 |
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- recall |
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- precision |
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- matthews_correlation |
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pipeline_tag: tabular-classification |
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tags: |
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- finance |
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--- |
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# 💳 Credit Card Fraud Detection with Random Forest |
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## 📚 Project Description |
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This project detects fraudulent credit card transactions using a supervised machine learning approach. The dataset is highly imbalanced, making it a real-world anomaly detection problem. We trained a **Random Forest Classifier** optimized for performance and robustness. |
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--- |
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## 📁 Dataset Overview |
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- **Source**: [Kaggle - Credit Card Fraud Detection](https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud) |
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- **Description**: Transactions made by European cardholders in September 2013. |
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- **Total Samples**: 284,807 transactions |
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- **Fraudulent Cases**: 492 (~0.172%) |
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- **Features**: |
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- `Time`: Time elapsed from the first transaction |
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- `Amount`: Transaction amount |
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- `V1` to `V28`: Principal components (PCA-transformed) |
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- `Class`: Target (0 = Legitimate, 1 = Fraudulent) |
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## 🧠 Model Used |
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### `RandomForestClassifier` Configuration: |
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```python |
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from sklearn.ensemble import RandomForestClassifier |
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rfc = RandomForestClassifier( |
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n_estimators=500, |
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max_depth=20, |
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min_samples_split=2, |
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min_samples_leaf=1, |
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max_features='sqrt', |
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bootstrap=True, |
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random_state=42, |
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n_jobs=-1 |
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) |
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``` |
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--- |
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## 📊 Model Evaluation Metrics |
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| Metric | Value | |
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|----------------------------------|-----------| |
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| **Accuracy** | 0.9996 | |
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| **Precision** | 0.9747 | |
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| **Recall (Sensitivity)** | 0.7857 | |
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| **F1 Score** | 0.8701 | |
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| **Matthews Correlation Coefficient (MCC)** | 0.8749 | |
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📌 **Interpretation**: |
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- **High accuracy** is expected due to class imbalance. |
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- **Precision** is high: most predicted frauds are true frauds. |
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- **Recall** is moderate: some frauds are missed. |
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- **F1 score** balances precision and recall. |
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- **MCC** gives a reliable measure even with class imbalance. |
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--- |
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## ⏱️ Performance Timing |
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| Phase | Time (seconds) | |
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|--------------------|----------------| |
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| Training | 375.41 | |
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| Prediction | 0.94 | |
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--- |
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## 📦 Exported Artifacts |
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- `random_forest_model_fraud_classification.pkl`: Trained Random Forest model |
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- `features.json`: Feature list used during training |
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--- |
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## 🚀 Usage Guide |
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### 1️⃣ Install Dependencies |
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```bash |
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pip install pandas scikit-learn joblib |
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``` |
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--- |
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### 2️⃣ Load Model and Features |
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```python |
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import joblib |
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import json |
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import pandas as pd |
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# Load the trained model |
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model = joblib.load("random_forest_model_fraud_classification.pkl") |
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# Load the feature list |
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with open("features.json", "r") as f: |
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features = json.load(f) |
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``` |
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--- |
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### 3️⃣ Prepare Input Data |
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```python |
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# Load your new transaction data |
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df = pd.read_csv("your_new_transactions.csv") |
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# Filter to keep only relevant features |
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df = df[features] |
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``` |
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--- |
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### 4️⃣ Make Predictions |
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```python |
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# Predict classes |
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predictions = model.predict(df) |
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# Predict fraud probability |
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probabilities = model.predict_proba(df)[:, 1] |
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print(predictions) |
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print(probabilities) |
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``` |
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--- |
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## 📌 Notes |
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- Due to the **high class imbalance**, precision and recall should always be monitored. |
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- Adjust the decision threshold to optimize for recall or precision depending on your business needs. |
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- The model generalizes well but should be retrained periodically with new data. |
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--- |
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## 🙏 Acknowledgements |
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- Dataset provided by ULB & Worldline |
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- Original research: *Dal Pozzolo et al.* |
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- [Credit Card Fraud Detection - Kaggle](https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud) |
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--- |
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## 📃 License |
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Apache License 2.0 — you are free to use, modify, and distribute this project under the terms of the Apache 2.0 License. |
