JAI ADITHYA RAM NAYANI

A versatile and driven computer science professional with a solid foundation in machine learning, data analytics, and full-stack web development. Adept at leveraging cutting-edge technologies like TensorFlow, React.js, and AWS to craft innovative solutions. Passionate about continuous learning and embracing challenges that foster personal and professional growth. Seeking opportunities to contribute expertise in delivering high-quality, scalable software systems that drive business success.

ADVANCED ML CLASSIFIERS FOR PRECISION CARDIOVASCULAR PROGNOSIS

• Utilized a combination of Machine Learning techniques including Regression Trees, SVM, KNN, and Naïve Bayes, implemented with Python and Scikit-Learn on the UCI Cleveland dataset, targeting improved prediction of heart disease through detailed attribute analysis.
• Employed Random Forest as an overarching Meta Classifier to unify the capabilities of individual algorithms. Applied TensorFlow for an in-depth evaluation of model efficacy, focusing on precision, accuracy, and sensitivity in diagnosing heart conditions.
• Designed a Python-driven automated system for data processing and disease prediction, utilizing Pandas and NumPy. Highlighted the use of RMSE for loss evaluation in TensorFlow, aiming for heightened accuracy in cardiovascular disease profiling and forecasting.

CUSTOMER RELATIONSHIP MANAGEMENT (CRM) SYSTEM

• Developed an interactive CRM web application using React.js, ensuring a fluid user experience for managing customer relationships, combined with Jenkins for CI/CD and JIRA for efficient project management, enabling real-time tracking and effective data visualization.
• Engineered a high-performance backend using Express.js, co-opted with a GraphQL API for efficient and flexible data queries, complemented by AWS services like EC2 and Lambda for reliable hosting and serverless operations.
• Implemented MongoDB for adaptable data storage, utilized Docker for seamless deployment across environments, ensuring quality and consistency with PyTest, all managed through Git and GitHub.

REAL - TIME OBJECT DETECTION WITH OPENCV

• Developed a sophisticated object detection system using OpenCV and MobileNetSSD, enabling precise recognition through webcam feeds.
• Engineered continuous frame analysis for real-time identification, visually encapsulating diverse objects with on-screen bounding boxes.
• Optimized for rapid, high-accuracy detection, the system excels in dynamic environments, recognizing a wide range of object categories efficiently.