Appointy is a full-stack doctor appointment web application built using the MERN stack, offering secure login portals for patients, doctors, and administrators. It enables users to book appointments online, manage profiles, view medical history, and make secure payments through Razorpay, while doctors can manage schedules and update availability. The admin dashboard provides tools to verify doctors, monitor transactions, and oversee the complete workflow. With its responsive UI, intuitive navigation, and focus on security and scalability, Appointy delivers a seamless and efficient healthcare booking experience.

View Project

Finlytics empowers users to take control of their finances by providing real-time insights, personalized recommendations, and predictive analytics. Its intuitive interface makes complex financial data easy to understand and act upon. With robust security measures and seamless integration across devices, Finlytics ensures your financial journey is both smart and safe. Designed for both individuals and businesses, it transforms raw data into actionable strategies, helping users maximize savings, optimize investments, and achieve their financial goals faster.

View Project

This project is an algorithm visualization tool designed to help learners and developers understand various data structures and algorithms through interactive animations. Built with HTML, CSS, and JavaScript, it features visual demonstrations for Tries, Binary Search Trees, Graph Algorithms (like Dijkstra’s, A*, BFS, DFS), and multiple Sorting Algorithms (Merge Sort, Quick Sort, Insertion Sort, Bubble Sort, Selection Sort). Users can insert/delete elements in real time to observe how algorithms work step-by-step, making it a valuable resource for both education and practice.

View Project

This project is based on Machine Learning, utilizing ML models to predict the likelihood of a person having certain diseases based on their medical parameters. It supports multiple disease predictions, including Diabetes, Heart Disease, Parkinson’s, and Breast Cancer. By inputting specific health-related values such as glucose level, blood pressure, BMI, and other relevant indicators, the system processes the data through trained models to provide a quick and reliable prediction. This allows users to assess potential health risks beforehand and take necessary preventive measures.

View Project