AI Engineer | MERN + LangChain/RAG | Microsoft Certified (AI-900, AI-103) & AWS AI Certified

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Developed a personalized nutrition platform that offers personalized dietary advice using ML and data analysis.

AI-Dietician System

<p>This project has been transformed from a manual task entry system into an automated pipeline. It efficiently tracks individual employee activity across GitHub and Google Sheets, stores it centrally in a PostgreSQL database, and broadcasts a formatted daily report via WhatsApp.</p>

Automated Individual Daily Work Reporting Sys

Developed Chatter Box, an AI-powered chatbot focused on educating users about mangrove forests and environmental conservation. Built the application using Flask, HTML, CSS, and JavaScript, and integrated Google Gemini AI to generate intelligent and interactive responses. Designed a modern, responsive chat interface with features like chat history, markdown support, dark mode, quick question suggestions, and export functionality to improve user experience and engagement.

Chatter Box

Developed a Delivery Admin Dashboard to manage delivery operations, employees, orders, live tracking, and financial analytics through a modern and responsive interface. Built the application using React, Vite, Tailwind CSS, Firebase, and Google Maps API for real-time delivery monitoring and route visualization. Implemented features such as employee and order management, route planning, notifications, wallet tracking, and secure authentication with Firebase. Designed an interactive admin experience with smooth animations, responsive layouts, and efficient dashboard navigation.

DeliverOps

Built a Liver Disease Prediction System using Machine Learning, Flask, Pandas, Numpy, SciKit-Learn and SQL Database integration. It uses the Random Forest Classifier algorithm to analyze the input data and predict the output accurately. The application analyzes user health data to predict the possibility of liver disease with improved accuracy.

LIVER-DISEASE-PREDICATION

Developed a Lung Disease Detection System using Machine Learning and Flask to help identify lung-related diseases from patient data and medical inputs. Used Python libraries like Pandas and Scikit-learn for data processing, model training, and prediction. Implemented the Random Forest Classifier algorithm to improve detection accuracy and reliability.

Lung Disease Detection System

Developed Music Therapy AI, an AI-powered music recommendation system that detects user emotions through facial expressions, voice tone, and text sentiment analysis. Built the full-stack application using React, Flask, MongoDB, Tailwind CSS, and machine learning libraries like Scikit-learn and Librosa. Integrated AI technologies such as LangChain, Ollama, and Gemini API to provide personalized music recommendations and conversational mental wellness support. Implemented secure user authentication, mood tracking, analytics dashboards, and real-time multimodal emotion detection for an interactive and intelligent user experience.

Music Therapy AI

Secure medical record management system with authentication & encryption for safe data storage and sharing. Technologies: Node.js, Express.js, MongoDB, Mongoose, MongoDB Atlas, React.js, Render, Vercel

PulseArchiver

Built a Stress Monitoring web application using Machine Learning and Flask that helps analyze and predict stress levels based on user data. Used Python libraries like Pandas, NumPy, Scikit-learn, and Plotly for data processing, model training, and visualization. Implemented ML algorithms such as KNN and SVM to improve prediction accuracy. Designed the frontend using HTML, CSS, and JavaScript to create a simple and interactive user experience.

Stress Monitoring

Built a Wearable AI Monitor System that tracks real-time health metrics like heart rate, step count, and SpO₂ levels using AI and wearable device integration. Developed a modern full-stack application using React, Vite, Flask, and MongoDB with interactive dashboards, analytics charts, and health history tracking. Implemented AI-powered health analysis to provide smart insights and recommendations based on user health data. Designed a responsive and user-friendly interface using HTML, CSS, and JavaScript for a smooth experience across devices.