Machine Learning Internship Internship

Data Collection: Gather historical transaction data, ensuring privacy and regulatory compliance.

Data Preprocessing: Clean, normalize, and handle class imbalance in the data.

Model Development: Select an appropriate machine learning algorithm, split data, and train the model, optimizing hyperparameters.

Model Evaluation: Assess model performance using accuracy, precision, recall, and cross-validation techniques.

Deployment and Integration: Deploy the model in a real-time environment, integrate it into transaction processing systems, and implement real-time monitoring and alerts for fraud detection.