Developed a deep learning-based system to recognize and interpret handwritten calculus expressions using computer vision and symbolic computation. The project combines image preprocessing, convolutional neural networks (CNNs), and LaTeX-to-SymPy parsing to convert handwritten input into solvable mathematical expressions.

Build an automated machine learning (AutoML) web app that allows business users to upload their datasets, automatically preprocess the data, train multiple ML models, and generate insights—without writing any code. 

A real-time face mask detection web application using deep learning. Detects whether a person is wearing a mask or not from images or live video feeds, built with TensorFlow, OpenCV, and Streamlit. Ideal for safety monitoring and public health awareness. 

This project is a fuzzy logic-based web application developed using Streamlit, designed to assist users in identifying the most probable disease based on uncertain or imprecise symptoms. By incorporating fuzzy inference rules, the system mimics human reasoning to suggest likely conditions from a list of 42 diseases.

This is a Streamlit application designed to analyze and visualize data related to colleges in the USA. The dataset contains various attributes for colleges, such as graduation rates, faculty count, student enrollments, and more. The application provides interactive data visualizations to explore these relationships and gain insights into factors affecting graduation rates.