Predicting Stock Prices using the Random Forest Classifier

Abstract

There is a current trend toward the use of artificial intelligence to get faster and more
accurate results , and we have found that investments are one of the areas that tend to
predict stock market prices. Therefore , the aim of this study was to predict the most
profitable sectors in the stock market and, accordingly, to make recommendations to the
best companies in this field. Depending on the stock price, closing price, opening price, and
stock value. Making it easier for an investor to choose the best, most profitable sector of the
year to invest. Dataset was selected from the famous site Kaggle and was characterized as
being modern because it is updated in seconds. Dataset has contained many global
companies, such as Apple and Microsoft. Furthermore , to predict the outcome number
of machine learning algorithms, we have chosen the straight line algorithm, random
forest, and support vector machine to compare the results and choose the best among them.
The random forest algorithm had the most accurate (94.12%) success rate because we were
able to increase the number of repeats in training, giving a higher accuracy ratio.