Predicting Stock Price using Data science technique

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.