• Pavana
• Prof.Dikshit M B
• Prema Sharadi T
• Shabari

This project explores the application of linear regression, a fundamental machine learning algorithm, for stock price prediction. The study involves collecting and preprocessing historical stock data, selecting relevant features, and training a linear regression model. The model's performance is evaluated using metrics such as Mean Squared Error (MSE) and R-squared, and its accuracy is compared with other simpler baseline methods. The findings highlight the computational efficiency of linear regression but also reveal its limitations in capturing the complex and non-linear dynamics of the stock market. This research provides a foundational understanding of linear regression's applicability to stock price prediction and offers insights for future exploration of more sophisticated machine learning models to enhance prediction accuracy.

Machine Learning, CNN, OpenMP, VGG16

"STOCK PRICE PREDICTION USING LINEAR REGRESSION: A MACHINE LEARNING APPROACH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.413-418, July-2025, Available :http://www.jetir.org/papers/JETIRGX06077.pdf

"STOCK PRICE PREDICTION USING LINEAR REGRESSION: A MACHINE LEARNING APPROACH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp413-418, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06077.pdf