Developed an end-to-end MLOps project to detect malicious URLs using XGBoost. Integrated robust pipelines for data ingestion, model training, deployment, and monitoring.

Built an end-to-end MLOps pipeline to predict the approval status of US visa applications. Implemented machine learning models, deep learning techniques, and automated deployment pipelines.

Predicted customer satisfaction scores for future orders using historical e-commerce data from the Brazilian E-Commerce Public Dataset by Olist. This project leverages multiple machine learning models like CatBoost, XGBoost, and LightGBM, built within a ZenML pipeline to create a production-ready solution.

An interactive web application designed to help users understand and experiment with machine learning algorithms visually. It offers dynamic visualizations for concepts like linear regression, allowing users to adjust parameters such as slope and intercept interactively. The platform aims to simplify ML concepts for learners through intuitive design and engaging tools.

Implemented a neural network from scratch using Python and Numpy. This project explores neural networks at a fundamental level, focusing on core concepts like backpropagation, gradient descent, and activation functions without relying on frameworks like TensorFlow or PyTorch.