Analysis of various machine learning algorithm and hybrid model for stock market prediction using python

With the up-gradation of technology and exploration of new machine learning models, the stock market data analysis has gained attention as these models provide a platform for businessman and traders to choose more profitable stocks. As these data are in large volumes and highly complex so a need of more efficient machine learning model for daily predictions is always looked upon. Therefore, in this work an extensive comparative analysis of already implemented models for stock trade market has been performed. On the basis of results obtained, new linear regression models are proposed which provide much significant error reduction. It was observed that appending two linear regression models where output of first block is fed to the input of second linear regression model gives the most efficient prediction model.