A travel-focused project offering a seamless online marketplace and hospitality service that allows people to lease or rent short-term lodging. The frontend, designed with Next.js, Tailwind CSS, and Redux, ensures an intuitive and responsive interface for travelers. Data security is prioritized through a Spring Boot backend integrating Spring Security, OAuth 2.0, and JWT, seamlessly connecting to PostgreSQL via JPA for efficient data management. To enhance scalability and reliability, AWS cloud services, including EC2, S3, RDS, and Elastic Beanstalk, have been employed. This side project provides a personalized and secure space for exploring and navigating travel information effortlessly.

A Web service is provided to users, enabling them to effortlessly discover the most suitable broadband and data plans. The website also offers up-to-date information on data plans, allowing users to promptly address minor concerns. It utilizes server-side rendering and image optimization techniques to enhance performance and user satisfaction. For flexibility and scalability, Strapi, a headless CMS and backend server, along with PostgreSQL as the database, were employed. The implementation of SEO best practices enhances the website's discoverability and increases organic search traffic.

A web application that provides personalized financial health analysis to users in order to help them establish a better financial future. The app allows users to visualize their financial situation by scanning receipts and uploading them with a single click. It processes user data and provides actionable financial improvement advice by leveraging technologies such as Google's Vision API for Optical Character Recognition (OCR) and OpenAI's NLP model.

An immersive web application that seamlessly integrates with the Metropolitan Museum of Art Collection API, offering users a refined experience in searching and viewing curated art collections. This modern platform, built with React (NextJS) and styled with the sleek aesthetics of Bootstrap, ensures a user-friendly and responsive interface.

A project focuses on decoding the nuanced trends within the Toronto housing market through meticulous analysis of data sourced from Zillow.com. Employing advanced web scraping techniques, a dynamic dataset is curated to facilitate a profound exploration of price distributions, correlations with property features, and the identification of premium areas. The analytical backbone of the initiative relies on Python, with Pandas and Matplotlib enabling robust statistical insights and visualizations. Jupyter Notebooks enhance transparency and collaboration, while Seaborn refines data representation. This cohesive integration of cutting-edge technologies and strategic data analysis positions the project as a potent tool for stakeholders seeking comprehensive insights into the dynamic landscape of Toronto's housing market.

This project revolves around harnessing the power of Pandas in Python to conduct a comprehensive analysis of data extracted from the DVD Rental database. The aim is to execute a series of data manipulations, transformations, and visualizations, ultimately addressing specific inquiries tied to the dataset. Through the utilization of Pandas, the project is empowered to efficiently navigate and derive insights from the intricacies of the DVD Rental data.