Description: Developed an end-to-end machine learning project to predict Netflix user ratings and viewing preferences using Amazon SageMaker. The project focused on data preparation, model training, deployment, monitoring, and performance evaluation. Key Contributions: Prepared and transformed large-scale user viewing data into LIBSVM format for model training and testing. Engineered key features such as watch time, duration, and watch ratio to improve prediction accuracy. Built both regression and classification models using XGBoost on Amazon SageMaker. Deployed trained models as real-time endpoints for inference. Monitored model performance and system logs using AWS CloudWatch. Evaluated model results using accuracy, precision, recall, and F1-score metrics. Visualized prediction results and user genre preferences for analysis. Managed AWS S3 buckets for training and testing datasets and deleted endpoints after testing to control costs.