Data science has proven to be of great deal in solving various problems of organizations, majorly statistical. This includes alleviating risks involved in trading in the stock market. The contemporary trade market requires analysis of large data in a bid to predict the future trends and performance. Just as one would suspect, stock market trends are brought about by the change in prices which change the indices direction. The ultimate goal of any trader is make optimum profits out of the transactions and this greatly relies on their analysis of the specific stock behavior. Needless to say, it is close to impossible to conduct this analysis without the help of a supervised machine learning model.
These models are prepared through training and corrections whenever their predictions are wrong. They incorporate algorithmic trading which defines the basic trading rules. These elaborate the trade guidelines such as when to buy or sell a stock. This poses the greatest advantage of the supervised machine learning model as the algorithms has automation capability thus saving on time, alleviating associated risks and easily analysing large data.