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It leverages machine learning techniques to analyze historical data and predict the likelihood of a transaction being fraudulent.\n\n## Features\n- **Data Collection**: Collects data from NSFAS and University of Limpopo CSV files.\n- **Data Preprocessing**: Cleans, preprocesses, and merges the data, handling missing values and performing feature engineering.\n- **Model Training**: Trains a neural network classifier using TensorFlow/Keras.\n- **Model Evaluation**: Evaluates the trained model's performance using accuracy, precision, recall, and F1 score.\n- **SMS Confirmation**: Generates a random confirmation code and sends it via SMS to the user for confirmation.\n\n## Requirements\n- Python 3.x\n- Pandas\n- Scikit-learn\n- Imbalanced-learn\n- Keras\n- TensorFlow\n- H2O.ai\n- Other dependencies listed in `requirements.txt`\n\n## Usage\nInstallation: Install the required packages listed in `requirements.txt`.\n   ```bash\n   pip install -r requirements.txt\nData Preparation: Ensure that the NSFAS and University of Limpopo CSV files are available and correctly located. Update the file paths in the code if necessary.\nRunning the Application: Execute the main script Fraud Detection.py.\nbash\npython fraud_detection.py\nResult Analysis: Check the output for model evaluation metrics and the confirmation code sent via SMS.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feva-kaushik%2Ffraud-detection-mlops","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Feva-kaushik%2Ffraud-detection-mlops","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feva-kaushik%2Ffraud-detection-mlops/lists"}