Stock Market Prediction Using Machine Learning Techniques

Stock Market Prediction Using Machine Learning Techniques

Shital Kacharu Jadhav Prof. M. S. Nimase

Matoshree College of Engineering and research centre Eklahre Nashik

Abstract

The stock market is a very important activity in the finance business. Its demand is consistently growing. Stock market prediction is the process of determining the future value of     company          stock    or         other       financial instruments traded on a financial exchange. For some decades Artificial Neural Network (ANN), which is one intelligent data mining technique has been used for Stock Price Prediction. It has been trusted as the most accurate consideration. This paper surveys different machine learning models for stock price       prediction. We   have trained             the available stockdataofAmericanAirlinesforthisproject.Th eprogramminglanguagethatwe have used in this paper is Python. The Machine Learning (ML) models used in this project are Decision Tree (DT), Support Vector Regression (SVR), Random    Forest              (RF),and          ANN.       The datahereissplitinto70%fortrainingand30%fort esting.The dataset contains stock data for the last 5 years. From the simulation results, it is shown that Random Forest performs better as compared to others. Thus, it can be used in the real-time implementation.

Keywords

Machine Learning, Stock Price, Prediction, American Airlines, Support Vector Machine (SVR), Artificial Neural Network (ANN), Random Forest (RF), Decision Tree (DT).