AR.

Description

1. Formulated comprehensive machine learning project to predict student performance, leveraging academic and socio-economic data; resulted in actionable insights that informed academic support strategies for 500+ students. 2. Engineered a ML pipeline that streamlined data ingestion processes, transforming raw inputs into high-quality datasets for predictive modeling; achieved a 40% reduction in processing time while ensuring consistent prediction accuracy. 3. Designed an efficient Continuous Delivery framework with AWS CodePipeline to streamline deployments from GitHub to AWS Elastic Beanstalk. 4. Accomplished prediction accuracy of 87%, automated the deployment process reducing manual intervention by 80%, and provided a user-friendly web interface for real-time predictions.