Trying to predict the stock market is an enticing prospect to data scientists motivated not so much as a desire for material gain, but for the challenge.We see the daily up and downs of the market and imagine there must be patterns we, or our models, can learn in order to beat all those day traders with business degrees. Naturally, when I started using additive models for time series prediction, I had to test the method in the proving ground of the stock market with simulated funds. Inevitably, I joined the many others who have tried to beat the market on a day-to-day basis and failed. However, in the process, I learned a ton of Python including object-oriented programming, data manipulation, modeling, and visualization. I also found out why we should avoid playing the daily stock market without losing a single dollar
Before downloading the project install the following dependencies
1:) pip install cvs 2:) pip install numpy 3:) pip install sklearn 4:) pip install matplotlib 5:) pip install time