Online Payment Fraud Detection using Machine Learning in Python
Introduction: With the exponential growth of online transactions, the risk of fraudulent activities has become a significant concern. This project focuses on leveraging machine learning techniques in Python to detect and prevent online payment fraud. By analyzing transactional data, we aim to develop a robust fraud detection system that enhances security and trust in online payment platforms.
Dataset Description: The dataset comprises essential features relevant to each transaction:
- Step: Unit of time measurement.
- Type: Transaction type.
- Amount: Total transaction amount.
- NameOrig: Sender's account name.
- OldbalanceOrg: Sender's account balance before the transaction.
- NewbalanceOrg: Sender's account balance after the transaction.
- NameDest: Receiver's account name.
- OldbalanceDest: Receiver's account balance before the transaction.
- NewbalanceDest: Receiver's account balance after the transaction.
- isFraud: Binary label indicating whether the transaction is fraudulent (1) or not (0).
Methodology:
- Data Preprocessing: Cleaning and preparing the dataset for analysis, handling missing values, and encoding categorical variables.
- Exploratory Data Analysis (EDA): Visualizing data distributions, correlations, and patterns to gain insights into fraudulent transactions.
- Feature Engineering: Selecting and creating relevant features to improve model performance.
- Model Development: Implementing various machine learning algorithms such as Random Forest, Logistic Regression, and Gradient Boosting to build predictive models.
- Model Evaluation: Assessing model performance using metrics like accuracy, precision, recall, and F1-score through cross-validation.
- Hyperparameter Tuning: Optimizing model parameters to enhance predictive accuracy and generalization.
- Model Deployment: Deploying the best-performing model to detect fraudulent transactions in real-time online payment systems.
Tools and Libraries Used:
- Python: Pandas, NumPy, scikit-learn
- Data Visualization: Matplotlib, Seaborn