Worked on multiple Salesforce projects under the RANA-RCC team to create and set up modules that increased the company’s overall efficiency by 35%.Made a data management system using HTML, CSS, and JavaScript to manage customer data and generate reports.Created a database using SQL to store and retrieve employee information.Developed a C++ program to solve a complex mathematical problem.

Designed a relational database for a fictional online bookstore to make sure data is stored and fetched efficiently. Made SQL queries work smoothly for adding, getting, and changing data. Also, Kick started building an inventory system in C++, making product tracking and data handling way better.

Created a full-stack personal blog with a bespoke CMS that included user authentication, live analytics, and SEO optimization, resulting in a 40% increase in user engagement. The backend, built with Python (Django) & SQL, guarantees safe data administration, while the frontend is responsive and uses HTML, CSS (Bootstrap), and JavaScript. Google Analytics integration records visitor information like session length and bounce rate, while Markdown support facilitates content authoring. Features such as social media sharing, comment areas, and search tools improve engagement and user experience. For scalability, the application is deployed via Docker and Heroku, with version control via Git.

Developed a comprehensive real-time market trend analysis and forecasting tool, using Python, Selenium, and Pandas to scrape competitor and market data from various sources. Processed and structured this data to uncover emerging trends. Scikit-Learn to build custom time-series forecasting models, leveraging historical data and seasonal patterns to predict market shifts. The tool featured an intuitive Flutter app for interactive, visual data representation, enabling users to easily monitor and understand trends. Integrated Firebase for real-time data updates and deployed the entire solution on AWS, ensuring scalability, high availability, and smooth performance across varying data loads