Collected brainwave data, processed the data by using pandas and python numpy array.

Predicted music preference of people by using machine learning models:

Used sliding window, short term Fourier transformation and wavelet transformation to do feature extraction and turning the data from time series domain to frequency domain.

Used ICA to do feature reduction

Built SVM and logistic regression models to train data and making predictions of preference with accuracy over 90%.