Salon booking microservices based project
Developed a full-stack salon booking system using Spring Boot (backend), React (frontend),
and MySQL, implementing JWT-based authentication, Keycloak for security, RabbitMQ for
asynchronous communication, and WebSocket for real-time notifications, with Razorpay
integration for secure payments and Docker for containerization and seamless deployment.
E-Donor-Blood Donation & Matching Platform
E-Donor is a blood donation and matching platform designed to connect donors and
recipients in real time, ensuring efficient blood management. Developed using .NET MVC,
jQuery, and Bootstrap, the platform features a location-based search system to match
donors with recipients quickly and accurately. It also integrates with hospitals to
track blood availability in real time, improving response times during critical situations.
To enhance the user experience, Elasticsearch was implemented to optimize search functionality,
reducing query processing time by 40%. The system streamlines the entire blood donation process,
from matching to tracking, offering a more responsive and efficient solution for both donors and
healthcare providers.
Distributed Backend System(Similar to LinkedIn)
Developed a distributed backend system using Spring Boot microservices, Apache Kafka for
real-time streaming, Redis for caching, and Docker/Kubernetes for containerization and
orchestration, ensuring high availability, scalability, and optimized performance.
Cricket Win Probability Machine Learning Model
The Cricket Win Probability Machine Learning Model predicts the outcome of cricket matches by
analyzing historical data, player statistics, and team performance metrics. Using a variety
of machine learning algorithms, including Logistic Regression and Random Forest Classifier,
the model estimates the probability of a team winning a match. To improve prediction accuracy,
the project involved extensive data preprocessing, feature engineering, and model evaluation
techniques such as cross-validation. By fine-tuning these elements, the model was optimized for
high accuracy and robust predictions, providing valuable insights into match outcomes based on
historical patterns and current performance metrics.
Medicine Reminder App
Developed a Full Stack Medicine Reminder App using React Native and TypeScript, integrating
Async Storage for data persistence, push notifications for reminders, and a responsive UI
for seamless cross-platform user experience.
Multi-vendor E-commerce project
Developed a multi-vendor e-commerce platform using Spring Boot for backend services, MySQL
for database management, and JWT for secure user authentication, while implementing a
responsive frontend with React, TypeScript, Redux, and MUI; integrated Razorpay and Stripe
for payment processing, and added features like a chatbot, product management, order history,
and seller dashboards, with full admin controls for user, coupon, and deal management.
Crypto trading platform
Developed a crypto trading platform using Spring Boot and React, featuring an AI chatbot for
crypto queries, buy/sell functionality, portfolio management, wallet transfers, transaction
history, two-factor authentication, and integrated with Gemini and CoinGecko APIs for real-time
data and Razorpay/Stripe for payments.
Cryptocurrency Data Analytics Project
The Cryptocurrency Data Analytics Project involves analyzing cryptocurrency market
data to uncover trends, patterns, and insights. Using tools like Python, Pandas, and
visualization libraries such as Matplotlib and Seaborn, the project processes large
datasets from various crypto exchanges to track price movements, market volatility,
and trading volumes. The goal is to provide actionable insights for traders and
investors, helping them make informed decisions based on historical data, market behavior,
and predictive analytics.
