Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{173520,
        author = {Chiranjeevi M R and Meghana C N and Hemalatha B M},
        title = {Stock Price Prediction using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {698-703},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173520},
        abstract = {In the current era stock price prediction plays a key role for prediction of future data with respect to training the past data by using machine learning or deep learning technologies. Building a model then passing the past data as input that is as training data to the model based on the results acquired needs to consider an algorithm which gives better accuracy and response time and segmentation. In this paper for estimating the stock values we are considering LSTM and regression models for machine learning. Factors considered are opening values of stock; closing values of stock; lower and higher values of stock and volume. Predicting stock market prices is a challenging task in the financial sector where the Efficient Market Hypothesis (EMH) posits the impossibility of accurate prediction due to the inherent uncertainity and complexity of stock price behaviour. This study employs advanced machine learning models that can predict stock price movements with the right level of accuracy if the correct parameter tuning and appropriate predictor models are developed.},
        keywords = {Stock prediction, LSTM, EMH, predict stock price movements.},
        month = {March},
        }