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@article{176956,
        author = {Aman Niyazi and Asif Khan and Mohd Farzan and Md Sahil Ali},
        title = {Stock Price Pridiction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {5010-5022},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176956},
        abstract = {Stock exchange trade is an important and major activity when talking about financial markets. Given the inevitable uncertainty and volatility of stock prices, investors are always looking for ways to avoid losses and predict future trends to achieve the greatest possible profit. However, so far, there cannot be denied that there is no technology that can completely accurately predict future trends in the market, but several methods have been considered to improve the model as much as possible. Due to recent progress in machine learning (ML) and deep learning (DL), many algorithms have been used to predict stock prices. This article examines five algorithms. That is, we examine K-nearest Neighbor, linear regression, vector regression support, decision tree regression, and long-term short-term memory for predicting stock prices from 12 major companies in the Indian stock market. After a comprehensive study of various aspects of the application of ML in the stock market, a comprehensive implementation was implemented as part of this research work. The study has collected and used stock price data records for 12 companies over the past seven years. This paper also illustrates other efficient and robust techniques used to predict stock exchange trends. In detail, the methodology for achieving results was gradually discussed. Additionally, we performed a detailed comparative analysis of the above services for predicting stock prices, and results displayed in easy-to-read and graphical formats to better analyze them. The conclusions of this new data-comprehensive study were presented and concluded that the DL algorithm predicts stock prices or time series beyond all other algorithms, providing results that provide a wide range of accuracy.},
        keywords = {Stock market, Machine learning, K-Nearest Neighbour (K-NN), Linear Regression (LR), Support Vector Regression (SVR), Decision Tree Regression (DTR), and Long Short-Term Memory (LSTM)},
        month = {May},
        }