This is a Jupyter notebook-based activity to explore machine learning models using data from the periodic table.

This project demonstrates how to uncover dominant cyclic patterns in any trending time series data using Fast Fourier Transform (FFT). While applied to BTC/USD market data here, the methods are generalizable to other datasets. The analysis identifies both known and hidden periodic cycles, offering insights for machine learning applications.