Projects 

Developed a medical chatbot leveraging advanced natural language processing techniques to assist users in obtaining medical information and advice. The chatbot integrates OpenAI models for accurate and conversational interactions, while utilizing FastAPI for a robust backend and React for a seamless user interface. Designed to handle dynamic queries, it provides users with real-time responses and insights into medical topics, offering a user-friendly and scalable solution.

EruditeEssays is an intuitive platform designed to assist users in crafting well-structured essays and academic content. It provides features such as topic suggestions, organized outlines, and plagiarism detection to ensure high-quality submissions. Built with a FastAPI backend and a ReactJS frontend, the platform delivers a seamless and user-friendly experience for students and professionals alike, focusing on accessibility and productivity in the writing process.

Designed and implemented an advanced RAG system using a combination of OpenAI, vector storage, and microservices. The application integrates cutting-edge natural language processing techniques, providing a seamless interface for querying and managing large-scale data sources. With a flexible architecture built on FastAPI and React, the system efficiently handles content ingestion, storage, and dynamic querying, offering real-time insights and interactions. Its robust infrastructure ensures smooth scalability and continuous updates across various data pipelines.

Developed a sophisticated loan approval system centered around predictive modeling and risk assessment. The project involved comprehensive data processing and feature engineering to enhance the model’s accuracy. Leveraging advanced statistical techniques and machine learning, the system evaluates loan applications with precision, ensuring informed decision-making. The backend was architected with a robust API for seamless integration, while the frontend offers an intuitive interface for efficient user interaction. This solution is designed to optimize loan approval workflows and reduce potential risks in financial transactions.

The Monte Carlo Simulation project focuses on European option pricing using advanced probabilistic and statistical techniques. This project implements the Monte Carlo method to simulate the underlying asset paths and calculate the option price. Additionally, the Black-Scholes algorithm is used to provide a benchmark for the option pricing. Built using Python, the project leverages libraries such as NumPy for numerical computations, Pandas for data manipulation, and Matplotlib for data visualization. The project also incorporates Jupyter Notebooks for an interactive development environment, facilitating better understanding and analysis of the simulation results.

ISO-Renewables-Explorer is a data analysis project focused on exploring and visualizing renewable energy data from various Independent System Operators (ISOs). The project aims to provide insights into renewable energy generation, consumption patterns, and trends over time. Built using Python, the project leverages libraries such as Pandas for data manipulation, NumPy for numerical analysis, Matplotlib and Seaborn for data visualization, and Jupyter Notebooks for an interactive development environment. The project also includes energy profiling to predict times of low energy availability using the XGBRegressor model, providing actionable insights for decision-makers.

The LSTM-neural-network-model project focuses on stock price analysis and prediction using advanced machine learning techniques. The project involves pulling stock data from Yahoo Finance using the yfinance library, performing Long Short-Term Memory (LSTM) analysis to model the temporal dependencies in the data, and predicting future market prices. Additionally, the project includes sentiment analysis on stock prices to gauge market sentiment and portfolio performance evaluation. The project showcases my proficiency in machine learning, financial analysis, and data visualization.

The project focuses on analyzing customer reviews from Flipkart using Natural Language Processing (NLP) techniques. The project aims to extract insights from the reviews by performing sentiment analysis to classify them as positive, negative, or neutral. Built using Python, the project leverages libraries such as NLTK (Natural Language Toolkit) or for text processing and sentiment analysis. Additionally, the project includes techniques such as tokenization, stemming, and machine learning-based classification to accurately categorize the reviews. The project demonstrates my proficiency in NLP and ability to derive valuable insights from textual data.