PREDICTION STOCK PRICE BASED ON DATA SCIENCE TECHNIQUE

Abstract

Predicting how the stock market will perform is one of the most difficult tasks. It can be described as one of the most important processes to predict. This is a difficult task with a lot of unknown things. To avoid this problem in one of the most interesting (or potentially profitable) time-series data sets, machine learning techniques have been used. As a result, stock price forecasts have emerged as an important research topic. The aim is to anticipate greater accuracy of stock price estimation systems based on machine learning. Suggest a method based on machine learning to accurately predict the value of a stock price index using the results of a stock price index or the highest accuracy scenario by comparing surveillance to differentiate machine learning algorithms. In addition, the effectiveness of a few machine learning methods from the departmental traffic database provided for testing will be compared and discussed. A set of data with a test breakdown report, identify the confusion matrix, and separate the data from the essentials, and the result shows that the efficiency of the proposed machine learning algorithm method can be compared with the best accuracy with precision, memory, and F1 Score. Keywords: - Data Editing, Upcoming Stock Returns, Machine Learning Tips.