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What I've been working on

Conference Management System

Developed a Conference Management System designed to streamline the organization of conferences and webinars, including features for paper submission, review management, and secure payment processing.
Technologies: ReactJS, TypeScript, Cloud Services, Database Management (SQL)

Graphical Convolotional Network - Food 101

Developed a Graphical Convolutional Network to evaluate and compare different food recognition models on the Food-101 dataset, focusing on the performance of Graph Convolutional Networks (GCN)
Technologies: Python, Machine Learning Methodologies (CNN, GCN)

System for Attendance Tracking with Class Registration and Management.

Developed a website for attendance marking and class registration, featuring real-time updates for seamless management. The platform also includes mentor registration and linking, allowing for efficient coordination between mentors and students.
Technologies: Java, SpringBoot, MySQL, DataBase Management

🧠 | Ensemble Methods for Model Robustness

This project explores advanced ensemble learning strategies to enhance classification accuracy and generalization. Models are trained, tuned, and evaluated on structured datasets using multiple ensemble paradigms.
Technologies: Python, Scikit-learn, XGBoost, LightGBM, Matplotlib

🖼️ Deep Learning for Image Classification

This project explores deep learning models for image classification using PyTorch and PyTorch Lightning , including regular CNNs, residual networks (ResNet-18, ResNet-50), and transfer learning across datasets.
Technologies: Python, PyTorch, PyTorch Lightning, Convolutional Neural Networks (CNNs)

🧠 XOR Classification with Neural Networks

This project explores solving the XOR problem using various neural network architectures, showcasing the power of non-linear modeling in supervised learning tasks.
Technologies: Python, Backpropagation, XOR Problem

🧠 | RGB vs Grayscale in Generative Models

This project evaluates the impact of input dimensionality on the performance of generative classifiers: , Gaussian Naive Bayes, Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA).
Technologies: Python, Machine Learning

🧠Supervised Classification

This project explores various classical supervised learning algorithms applied to structured datasets. The goal is to implement, compare, and evaluate models for classification tasks using both manual and library-based methods.
Technologies: Python, Machine Learning