AWS / Udacity
Selected among 1,500 learners globally for the prestigious AWS/Udacity Machine Learning Nanodegree, valued at $4000.
AWS
ETHNUS
ETHNUS
NPTEL
Coursera
NPTEL
Udacity- In partnership with AWS
Java, Python, JavaScript, HTML5, CSS.
React, Node.js, Express.js, Next.js, TailwindCSS, Vercel, Front-end Development, Back-end Development.
Power BI, Excel, Tableau, Python (Data Analysis), MATLAB, Data Analysis, Predictive Modeling, Demand Forecasting, Inventory Optimization, Machine Learning, Marketing Analytics.
n8n, APIs, AI Tools, ChatGPT, Claude, Gemini, Perplexity, Workflow Automation.
AWS, Firebase, MySQL, MongoDB.
Git, Postman, Figma, Canva, Microsoft Office, Replit.
Summary
A sophisticated AI-powered system that transforms harsh code review comments into empathetic, educational guidance that builds developer confidence while teaching best practices—all contained within a single, comprehensive Jupyter notebook.
Summary
A modern productivity app combining to-do lists, Google Calendar sync, and Pomodoro timers to help users stay organized and focused. Built with React, Convex, Tailwind, and Framer Motion for a responsive, real-time, and animated user experience.
Summary
A comprehensive Pet Adoption and Care Platform allowing pet owners to adopt pets, track and monitor their health. Built using Next.js it features a comprehensive dashboard allowing pet owners to track the health of their pets and adopt them
Summary
Built a comprehensive Power BI dashboard to analyze Zomato restaurant data across 15 countries, 141 cities, and 250 cuisines—covering over 9,500 restaurants, 1M+ votes, and average rating metrics. The dashboard delivers insights into restaurant distribution, cuisine popularity, service availability (online delivery & table booking), and top-rated brands
Summary
Automated workflows using n8n, APIs, and AI tools for data gathering and reporting.
Summary
Built and deployed a responsive health and wellness platform.
Summary
Developed an AI-driven system to predict demand, inventory requirements, and supply chain risks using historical sales, logistics, and external factors. Built interactive dashboards for real-time monitoring and forecasting, enabling data-driven decisions, reduced stock-outs, and optimized resource allocation. Implemented using Python, ML models (regression & time-series), Streamlit and data visualization tools.
Fluent
Native
Basic