Developing a Smart, Integrated Stock Price Prediction Model for Enhancing the Efficacy of Forecasting Stock Price

Drishti Arora

Volume 6, Issue 2 2022

Page: 38-41

Abstract

Determining the future value of a company's stock is the primary objective of stock price prediction, which is influenced by various factors such as industry trends and market conditions. However, the complexity of stock data poses a challenge for machine learning models due to the high dimensionality and correlated attributes, which can impact the accuracy of predictions. Principal Component Analysis (PCA) is employed to address this challenge by reducing dimensionality, thereby improving the suitability of linear regression algorithms for predicting future stock prices. This study investigates the impact of PCA on Tesla stock price data both before and after applying linear regression algorithms. The resultsdemonstrate that PCA enhances the performance of machine learning models by reducing correlation and selecting principal components that capture essential information while minimizing data redundancy. Evaluation metrics such as root mean square error and R-square value are utilized for assessment.

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