The Transaction Fraud Detection Model is a machine learning-based solution designed to identify and mitigate fraudulent transactions in financial datasets. By leveraging advanced algorithms and statistical techniques, this model analyzes transaction patterns and flags suspicious activities, helping organizations reduce financial losses and enhance security.
Features
Data Preprocessing: Comprehensive data cleaning and feature engineering to prepare transaction data for analysis.
Model Training: Implements various machine learning algorithms, including Logistic Regression, Decision Trees, and Random Forests, to classify transactions as legitimate or fraudulent.
Evaluation Metrics: Utilizes metrics such as accuracy, precision, recall, and F1-score to evaluate model performance.
Visualization: Provides visualizations of data distributions and model performance metrics for better understanding.
Deployment Ready: Contains scripts for model deployment, allowing integration into existing transaction processing systems.
Prerequisites
Python 3.x
Required libraries: pandas, numpy, scikit-learn, matplotlib, seaborn
Clone the repository:
git clone https://github.com/sarah-abeer/transaction-fraud-detection-model.git
cd transaction-fraud-detection-model
Usage
Load your dataset (CSV format is recommended).link: https://drive.usercontent.google.com/download?id=1VNpyNkGxHdskfdTNRSjjyNa5qC9u0JyV&export=download
Run the preprocessing script to clean and prepare the data.
Choose a model from the provided options and execute the training script.
Evaluate the model using the evaluation metrics provided.
Optionally, deploy the model using the deployment script.
Contributing
Contributions are welcome! Please feel free to submit a pull request or open an issue for any suggestions or enhancements.
Author-Sarah Abeer