• Designed and Simulated a traditional and a green network in both GNS3 and Packet Tracer
• Configured the power saving mechanisms on green network enabling 50% power saving
• Visualized the merits of a green network over traditional network in terms of costs, power and efficiency

• Developed a machine learning Model that predicts customer satisfaction using customer reviews and rating
• Implemented model improvement through different datasets and benchmarking with other models
• Managed to get realistic predictions accuracy of 90% influencing customer centered decisions

• Designed and Engineered Smart Bin using IoT technology
• Created a server side using Node.js and MySQL database and web application using Nodejs and React +
Vite
• Used smart bin to assess bin fill levels, optimize collection routes, and analyze bin tilt incidents
• Offered Predictive waste generation forecasts for efficient management and resource optimization
• Developed efficient routing algorithm for waste collection using Nearest Neighbor with weights such as water
content

• Developed machine learning models one that detects and classify pests with a precision of 93% and the other for
recommendations
• Developed a web-based application for inference to help farmers detect pests, and get specific guidance on
control
• Used machine learning and expert judgement to give recommendations to farmers on types of crops they
can plant, irrigation cycles, application of nutrients, lime etc

• Built a web-based shift scheduling system to replace manual, spreadsheet-based workflows
• Integrated real-time notifications, shift swap requests, overtime requests and role-based access control
• Implemented secure backend (bcrypt) with audit trail logging and GDPR-compliant data handling
• Achieved improved scheduling efficiency and reduced administrative errors in shift management