Stock price prediction using machine learning on least-squares linear regression basis
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Predicting the future of a stock price is a difficult task due to the high level of randomness
in the movement of prices. This research aims to use a machine-learning algorithm to estimate the
closing stock price of a dataset to help aid in the prediction of stock prices leading to higher accuracy
in prediction. The intention of the model is for it to be used as a day trading guide. The algorithm
being used is called the least-squares linear regression model. It takes in a dependent variable, in this
case, would be our closing price of the stock and an independent variable, which is the day each stock
price was recorded.
Keywords
QA Mathematics