Explore my journey in machine learning and AI through these innovative projects that demonstrate practical applications of cutting-edge technologies in healthcare, natural language processing, and intelligent systems.
Healthcare AI • Machine Learning
Developed a machine learning web application for diabetes risk assessment achieving clinical-grade accuracy using validated algorithms on medical datasets with 8 clinical parameters evaluation. Deployed enterprise-grade application on Streamlit Cloud with interactive risk visualization and real-time analysis.
Medical AI • Statistical Modeling
Built an AI-powered diagnostic system for early cardiovascular disease detection using classification algorithms on clinical datasets. Performed extensive exploratory data analysis and correlation studies to identify key biomarkers and risk factors, implementing feature scaling and handling class imbalance using SMOTE techniques.
GenAI • Natural Language Processing
Developed a feature-rich web application leveraging Google Gemini AI for intelligent story generation with customizable parameters including genre, length, character types, and multi-language support across 5 languages. Implemented advanced features including story continuation, text-to-speech integration, and PDF/TXT export functionality.
Full-Stack Development • React
Developed a comprehensive food delivery platform using React and Supabase architecture with modern UI/UX design and real-time order management functionality. Features include user authentication, menu management, cart functionality, order tracking, and responsive design optimized for all devices.
Flask • Machine Learning
This project implements a robust rainfall prediction system that combines meteorological data analysis with machine learning to forecast whether it will rain on a given day. The system achieves 82.43% accuracy using XGBoost classification and provides predictions through an intuitive web interface.
Flask • Machine Learning
The Car Price Predictor is a web application that uses machine learning to predict the price of used cars based on various features and specifications. This project integrates a Linear Regression model with a Flask backend and an intuitive HTML/CSS/JavaScript frontend to provide users with accurate price estimates.