Stock Price Prediction Using Machine Learning Models: A Study of NSE Stocks

Abstract

Abstract: This paper presents a study on stock price prediction utilizing the ARIMA and Linear Regression algorithms, focusing on companies listed on the National Stock Exchange (NSE) of India. The aim is to compare the predictive accuracies of these models while considering the lifetime data of stocks obtained through the Yahoo Finance API. Through comprehensive analysis, historical stock data spanning the entire lifespan of the stocks is utilized, enabling a thorough exploration of long-term trends and patterns. It was inferred that for NSE (Indian Company) stocks and Linear Regression prove to be more efficient than ARIMA. The research methodology involves data retrieval, preprocessing, and model training, with Python being the primary programming language for implementation. Findings indicate the effectiveness of ARIMA and Linear Regression models in forecasting NSE stock prices, with implications for financial decision-making and investment strategies. This study contributes to the understanding of machine learning applications in the stock market domain, emphasizing the importance of leveraging comprehensive historical data for enhanced predictive performance.