The Credit Card Fraud Detection project is a machine learning-based solution that aims to identify fraudulent credit card transactions from a dataset of credit card transactions. It utilizes Apache Spark and a Random Forest classification model to predict whether a transaction is fraudulent or not. It uses Flyte for orchestrating the machine learning workflow and Flask for serving the model as a REST API.

• Efficiently detects fraudulent credit card transactions.
• Utilizes Apache Spark for distributed data processing.
• Easy-to-use REST API for making real-time predictions.
• Supports batch prediction for multiple transactions.

• Python
• Apache Spark
• Flyte
• PySpark
• Flask
• Pandas
• Scikit-learn

Before you begin, ensure you have met the following requirements:

• Python 3.7 or higher installed
• Java 8 or higher installed (for Apache Spark)

Model Accuracy: 0.9958040104826124                                              
Model Precision: 0.9956810532257646
Model Recall: 0.9958040104826125
Model F1-Score: 0.9945218711604894

1. Clone this repository:

   git clone https://github.com/your-username/credit-card-fraud-detection.git

2. Install the required Python packages:

   pip install -r requirements.txt

3. Run the application:

   python workflow.py