The project aims at predicting stock prices by filtering the time series data using signal processing methods such as the Discrete Cosine Transform (DCT), Discrete Fourier Transform which makes it easier to process the data The preprocessed data is then used to learn appropriate machine learning models like HMM,SVM,Random Forest which could predict future stock prices and fluctuations.
TODO: Adding detailed comparision of various machine learning models and adding multiple features instead of just using the stock price.