Retrieval-Augmented Generation (RAG) chatbot using ChromaDB and Perplexity API to answer natural language questions about the UChicago MSADS program.

Uchicago ADS Program ChatBot

This project demonstrates data analysis and predictive modeling through analysis of monthly stock trading data from the Chinese stock market. The system processes 892,080 records from the TRD_Mnth dataset, focusing on stock code 000001 as a case study, delivering insights through statistical analysis, visualizations, and machine learning forecasts.

This is the programming supplement for ADS at Uchicago application

Coding Supplement and Project Created for my application to UChicago MSDS

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The MSADS RAG Chatbot is a Retrieval-Augmented Generation (RAG) system designed to answer user questions about the University of Chicago’s MS in Applied Data Science program using only official program content. The system combines web scraping, semantic search, and large language models to generate grounded, source-attributed answers through an int

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Knowledge distillation code for UChicago MS-ADS MLOps class

the final project for our MS ADS GenAI class at UChicago

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Developing a RAG-based chatbot to answer student questions regarding the UChicago MS-ADS Program

Coursework and homework assignments for Statistical Modelling in the MS-ADS program at UChicago. All files are written in R

Coursework and homework assignments for time series & analysis in the MS-ADS program at UChicago. All files are written in R