Credit card fraud detection using machine learning is one of the most impactful applications in financial security analytics.
This project examines transaction patterns such as amount time and anonymized numerical features to determine whether a credit card transaction is legitimate or fraudulent.
Using Python and machine learning the model identifies unusual behavior patterns and helps banks detect fraud early ensuring safer transactions for customers.
Project Overview
- Machine learning based fraud classification system
- Uses standard preprocessing with scaling and model training
- Applies Logistic Regression or other classifiers to detect fraudulent transactions
- Built using Python and widely used data science libraries
Libraries Used
- Pandas for data loading cleaning and manipulation
- NumPy for numerical computations
- Scikit Learn for preprocessing scaling model building and evaluation
- StandardScaler for normalizing continuous features
- Train Test Split for validation of model performance
- Classification metrics such as accuracy recall precision and F1 score
Dataset Details
The dataset contains anonymized credit card transaction features.
Since real transaction data contains sensitive information the dataset is preprocessed using PCA like transformations resulting in numerical features V1 through V28.
Important columns include
- Time
- Amount
- Numerical features V1 to V28
- Class column where
- Zero represents legitimate transaction
- One represents fraudulent transaction
The dataset is highly imbalanced because fraudulent transactions occur far less frequently than normal transactions.
Preprocessing Steps
- Loaded and inspected the dataset
- Scaled continuous features such as Amount and Time using StandardScaler
- Created training and testing splits to evaluate model performance
- Ensured data balance handling techniques if necessary
Model Building
- Logistic Regression used as the main classification model
- The model learns hidden patterns and correlations between numerical transaction features and fraudulent behavior
- Trained on scaled input data and evaluated on unseen test samples
- Logistic Regression performs well for binary classification especially on large numerical datasets
Performance and Accuracy
- Model evaluated using accuracy precision recall and F1 score
- Since data is imbalanced recall is an important metric for identifying fraud correctly
- Confusion matrix used to assess true fraud detection versus missed fraud cases
- Model provides reliable fraud detection suitable for real world applications
Prediction Flow
1 User provides transaction feature values including time amount and V1 to V28
2 Values are scaled using the same StandardScaler used during training
3 Logistic Regression model predicts output
- Zero means legitimate transaction
- One means fraudulent transaction
Deployment Possibilities
- Can be deployed using Flask or Streamlit for real time fraud detection
- Useful for banking platforms and risk management systems
- Can be integrated into fraud alert systems for immediate action
Key Takeaways
- Complete end to end fraud detection system built using machine learning
- Demonstrates effective preprocessing and classification on imbalanced datasets
- Shows strong usability for financial security and fraud risk analysis
Future Enhancements
- Apply oversampling techniques such as SMOTE to handle imbalance
- Experiment with advanced models like Random Forest XGBoost or Neural Networks
- Optimize decision thresholds to reduce false negatives in fraud detection
- Deploy a full dashboard with real time monitoring tools