Volatility is a measure of a stock price's uncertainty and is used throughout quant finance, for instance in pricing options. However, volatility is not something that is easily observed in the market, and requires estimation techniques.
I propose a project where we forecast a stock's next-day (or next-month) volatility from the previous day's (month) volatility. We would use some high frequency data (possibly minute-by-minute prices scraped from yfinance) and use the data to train an auto-regressive (AR) model for volatility. We could also use other machine learning techniques to produce other forecasting models and compare them with the AR model.