AI Based Diet Recommendation System

The AI-Based Diet Recommendation System is an intelligent platform designed to provide personalized diet plans and nutritional recommendations based on individual health and lifestyle information. Using Artificial Intelligence and Machine Learning algorithms, the system analyzes user details such as age, gender, height, weight, BMI, physical activity level, and food preferences to recommend suitable diet plans and daily calorie requirements.

Developed using Python, Scikit-learn, Pandas, and NumPy, the system uses Decision Tree Classification and K-Nearest Neighbors (KNN) algorithms to generate customized dietary recommendations. It aims to promote healthy eating habits, simplify diet planning, and make personalized nutrition guidance more accessible.

FACE ATTAIN AI is an AI powered classroom attendance management system that uses facial recognition technology to automatically detect and recognize students through a webcam. Developed using Python, OpenCV, and face recognition algorithms, the system identifies registered students and records their attendance with the corresponding date and time.

The project aims to reduce manual attendance work, minimize human errors, prevent proxy attendance, and improve classroom efficiency. It provides a digital approach to attendance management for schools, colleges, and other educational institutions.

Fine-Tuned Whisper Transcription with Syllabus Matched Summarization

This project is an AI-powered lecture analysis system that converts classroom audio recordings into text and generates meaningful summaries using Artificial Intelligence. It uses a fine-tuned Whisper-small speech recognition model for automatic transcription and a locally hosted Large Language Model (LLM) through Ollama to generate lecture summaries, extract key discussion points, and analyze syllabus topic coverage.

Developed using Python, Flask, Whisper, LoRA, CTranslate2, and Ollama, the system allows users to upload lecture recordings and syllabus documents to receive structured learning materials. It operates locally without sending data to external cloud services, providing a privacy-focused solution for students and educators.