I'm a Software developer with a passion for Full-Stack development. I love working on exciting projects, learning new technologies, and collaborating with others to create amazing user experiences.

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• Project Overview: Sales Savvy is a web-based application designed to streamline and track sales processes efficiently. It helps businesses manage leads, customers, and sales activities in one platform.

• Frontend Development: Developed responsive user interfaces using React.js, HTML5, CSS3, and JavaScript. Implemented dynamic views and interactive dashboards for seamless user experience.

• Backend Implementation: Built RESTful APIs using Spring Boot for managing business logic and data processing. Ensured secure and efficient data handling between client and server.

• Database Integration: Utilized MySQL to store and retrieve customer data, sales records, and product information. Performed complex queries and relationships using SQL and JPA.

• Authentication & Authorization: Integrated user authentication and role-based access control for Admin and Sales roles. Ensured secure login using Spring Security and JWT tokens.

• Sales Tracking Features: Implemented modules to track leads, deals, follow-ups, and conversions. Added features like filters, search, and status updates for easy monitoring.

• Deployment: Packaged the backend using Maven and deployed on Apache Tomcat server. Hosted frontend using live server or local deployment for testing.

• Technologies used: React.js, Spring Boot, MySQL, Rest API, VS code, Eclipse IDE, Postman API testing, GitHub, Git, Tomcat Server.

• GitHub: GitHub Repository

• Created a responsive portfolio to showcase projects and skills.
• Link: Link

• Developed a machine learning model to predict cardiac arrest risk using patient health data.
• Used supervised learning algorithms like Random Forest, Decision Tree, and Logistic Regression.
• Dataset preprocessing and feature selection were done using Pandas and NumPy in Python.
• Performed data visualization using Matplotlib and Seaborn to understand patterns and trends.
• Trained and tested models on Google Colab with proper evaluation metrics like accuracy and recall.
• Achieved reliable prediction accuracy to assist in early detection and prevention of cardiac arrest.
• Built a user-friendly pipeline to take patient inputs and display prediction results clearly.
• Technologies used: Python, Pandas, NumPy, Matplotlib, Seaborn, Random Forest, Decision Tree, Google Colab.

• Cricket, HandBall
• Online games

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