This project aims to develop predictive insights into library book checkouts using machine learning and time series analysis. Leveraging the "Checkouts by Title" dataset provided by the Seattle Public Library, our analysis addresses two main questions: (1) can we predict the number of checkouts in the first year for a new book based on a number of features, and (2) can we forecast future checkouts over several months by analyzing past checkout patterns?

Both questions have implications for both inventory management and future acquisitions. Our first question aims to help libraries, bookstores, and publishers determine the number of copies of a new book they should buy/release. Our time series forecasting will help these same stakeholders estimate demand, supporting strategic planning for resource allocation.