A food delivery website built using the MERN stack (MongoDB, Express, React, Node.js) offers a comprehensive and dynamic platform for ordering food online. User authentication and authorization are seamlessly integrated, allowing users to sign up or log in with their email or social media accounts, and manage their profiles and saved addresses. The restaurant and menu management features enable users to browse a diverse list of restaurants, view detailed information about each one, including its menu, location, and hours of operation, and customize their orders with options such as extra toppings. Search and filter functionalities enhance the user experience by allowing customers to quickly find specific restaurants or dishes based on criteria like cuisine type, price range, rating, and distance. The order management system includes the ability to add items to a cart, proceed through a secure checkout process, and receive an order confirmation with an estimated delivery time. Additionally, users can track their orders in real time to see the status and delivery progress. Payment integration ensures a secure and smooth transaction experience, allowing users to choose from various payment methods. This combination of features provides a user-friendly and efficient platform for managing food delivery from start to finish.

The project aims to create a platform in which one can play Tic-tac-Toe game with his friend , with people ground globe,or may with computer. Next.js and tailwind provides semless ui to and give the best user Experience while

The project is designed to create an online integrated platform for student projects, utilizing a tech stack of HTML, CSS, Tailwind, EJS, JavaScript, Multer, MongoDB, and Express.js. This platform not only allows students to submit and showcase their projects but also fosters a collaborative community. Users can connect with each other, make friends, and join groups to work together on projects. The platform features real-time chat functionality, enabling users to communicate and collaborate seamlessly. Tailwind CSS ensures a responsive and visually appealing design, while JavaScript enables dynamic interactions for searching and filtering projects. Multer facilitates the efficient upload of project files, and MongoDB, along with Express.js, supports a robust backend for managing user profiles, project submissions, and group collaborations. This comprehensive system promotes a vibrant community where students can engage, collaborate, and advance their projects collectively.

The stock trend prediction project utilizes machine learning techniques to forecast future stock prices based on historical data. By implementing a layered Long Short-Term Memory (LSTM) neural network, the model analyzes past stock trends and makes predictions with a high degree of accuracy. The project demonstrates advanced knowledge of time series analysis, deep learning, and predictive modeling, providing insights into market behavior and helping users make informed investment decisions. Additionally, the project incorporates Python libraries like TensorFlow and Keras for model development, training, and evaluation.