Stock Price Prediction By Applying Machine Learning Techniques

Abstract

Stock Market Prediction is affordable access to find the future scope of company stock or any financial exchange. The successful prediction of the stock will maximize the profit of the investors that are associated with the company. This research paper proposed algorithms based on knowledge engineering to envisage the stock price of a brand's dataset. Three most prominent regression techniques namely Support Vector(SVR), Random Forest(RFR) and Linear Regression have been used for predicting the stock price. The model proposed in this paper is based on the historical data of the company. These machine-learning algorithms are very popular and efficient for finding accurate results. This model does the prediction and compares its accuracy through the mean squared error(MSE), Mean Absolute Error(MAE), and Root Mean Squared Error(RMSE) to classify the better result.