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ABSTRACT
Persuasion of the topic is aimed at predicting the stability of future markets
stocks.The movement of future market evolution has been studied by many
researchers. Data fluctuates in stocks, so it becomes a source of efficiency. The
purpose of this article is to propose a machine-learning-based model for predicting
the stock price Index value from comparing supervise classification machine learning
algorithms by predicting stock price increases or stable states. Moreover, the aim of
this study is to compare and discuss the performance of various machine learning
algorithms based on the data from the transport traffic department. For this project,
logistic regression, random forest, decision trees, and naive bayes theorem were
used to calculate stock price predictions. An evaluation of the proposed machine
learning algorithm technique has been made with regard to identifying the confusion
matrix, categorizing data based on priority and comparing it with the best levels of
precision, recall and the F1 score.
