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Built a Chrome extension integrating a contextual chatbot powered by a Retrieval-Augmented Generation (RAG) pipeline.
Used LangChain, FastAPI, and Gradio for backend services, with Mistral via Ollama for lightweight, local LLM inference.
Enabled web page-aware Q&A by extracting on-page content and embedding it with HuggingFace and ChromaDB for semantic search and personalized responses.
Tech Stack: FastAPI, LangChain, HuggingFace, ChromaDB
Air travel can often feel exhausting, with passengers struggling to find flights that match their preferences and needs.
We wanted to create a solution that not only simplifies flight planning but also makes it more convenient and personalized.
Inspired by the growing demand for tailored travel experiences, we built FlightPath to optimize how passengers interact with airlines.
Tech Stack: Python, Flask, SQLite, Sklearn
This project explores how the integration of IoT in public infrastructure, particularly EV charging stations, introduces cyber risks, but also generates valuable data.
By combining cybersecurity expertise with data science and machine learning, we aim to proactively detect and respond to threats through pattern analysis, predictive modeling, and automated defenses.
Tech Stack: Python, Pandas, Seaborn, Sklearn
This project aimed to classify Iris flowers into three species on petal and sepal dimensions.
It serves as an introduction to core ML concepts such as data preprocessing, exploratory data analysis, model selection, and performance evaluation.
Using the classic Iris dataset, I applied techniques such as feature scaling and data visualization to gain insights into the feature distributions and class separability.
Tech Stack: Pandas, NumPy, Sklearn, Matplotlib, Seaborn
This project analyzes player and team performance data from the 2023 Volleyball Nations League (VNL) using exploratory data analysis techniques.
The goal was to uncover trends, outliers, and performance insights through data visualization and statistical summaries.
Using Python, I cleaned and processed the dataset, performed visual analysis, and identified key metrics such as attack success rates, block efficiency, and serve performance.
Tech Stack: Python, Pandas, Matplotlib, Seaborn
A collection of C++ projects showcasing the implementation of fundamental data structures and algorithms.
Focused on algorithmic problem-solving, efficient memory management, and time/space complexity optimization.
Projects were developed using standard C++ features and the STL for containers and algorithms.
Tech Stack: C++