An intelligent fraud detection system that leverages Machine Learning to identify fraudulent financial transactions. The application analyzes transaction data, extracts meaningful features, and predicts whether a transaction is Legitimate or Fraudulent using a Random Forest Classifier.
Financial fraud has become one of the biggest challenges in digital transactions. Traditional rule-based systems often fail to detect new fraud patterns. This project utilizes a Machine Learning-based classification model trained on historical transaction data to accurately distinguish between legitimate and fraudulent transactions.
The system is designed to provide fast, reliable, and scalable fraud detection that can assist banks, payment gateways, and financial institutions.
- 💳 Detect fraudulent transactions using Machine Learning
- 📊 Data preprocessing and feature engineering
- 🌲 Random Forest Classifier for prediction
- 📈 Model training and evaluation
- 📋 Performance metrics and accuracy calculation
- 💾 Save and load trained model using Joblib
- ⚡ Fast prediction for new transactions
- 🖥️ Simple and user-friendly interface
Dataset
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Data Preprocessing
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Feature Selection
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Train-Test Split
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Random Forest Classifier
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Model Training
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Model Evaluation
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Save Trained Model (.pkl)
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Fraud Prediction
- Python
- Scikit-learn
- Random Forest Classifier
- Pandas
- NumPy
- Joblib
The model is trained using a financial transaction dataset containing various transaction-related attributes.
Typical features include:
- Transaction Amount
- Transaction Time
- Merchant Details
- Customer Information
- Transaction Type
- Device Information
- Location Data
- Other engineered transaction features
Target Variable
- 0 → Legitimate Transaction
- 1 → Fraudulent Transaction
The project uses a Random Forest Classifier, an ensemble learning algorithm that combines multiple decision trees to improve prediction accuracy and reduce overfitting.
- High classification accuracy
- Handles large datasets efficiently
- Robust against overfitting
- Works well with tabular datasets
- Provides reliable predictions
- Handles nonlinear relationships effectively
- Load the dataset
- Clean and preprocess data
- Select relevant features
- Split dataset into training and testing sets
- Train Random Forest model
- Evaluate model performance
- Save trained model
- Predict fraud for new transactions
The trained model is evaluated using:
- Accuracy Score
- Precision
- Recall
- F1 Score
- Confusion Matrix
Fraud-Detection-System/
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├── dataset/
│ └── fraud_dataset.csv
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├── model/
│ └── fraud_model.pkl
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├── main.py
├── train_model.py
├── requirements.txt
├── README.md
└── assets/
Clone the repository
git clone https://github.com/your-username/Fraud-Detection-System.gitNavigate into the project
cd Fraud-Detection-SystemInstall dependencies
pip install -r requirements.txtRun the project
python main.py- Deep Learning models
- XGBoost implementation
- Real-time fraud detection API
- Dashboard for transaction monitoring
- Explainable AI (SHAP/LIME)
- Cloud deployment
- Live streaming transaction analysis
Through this project, I learned:
- Machine Learning model development
- Data preprocessing techniques
- Feature engineering
- Ensemble learning
- Random Forest implementation
- Model evaluation
- Saving and loading trained models
- Building end-to-end ML applications
Rohan R
Engineering Student | Android Developer | Machine Learning Enthusiast
If you found this project useful, consider giving it a ⭐ on GitHub.