• Mr. Karan Wankhede
• Mr. Atharva Dekate
• Mr. Mihir Kanhekar
• Miss. Mayuri Ramteke
• Prof. Siddharth Ghosh

This study explores the application of machine learning techniques for predicting stock market prices. The stock market, known for its inherent volatility and complexity, presents a challenging environment for forecasting. By leveraging various machine learning models, such as decision trees, support vector machines, and deep learning algorithms, the research aims to identify patterns and trends in historical stock data that can enhance prediction accuracy. The study employs data preprocessing techniques, including normalization and feature selection, to improve the performance of the models. Through rigorous evaluation using performance metrics like accuracy, mean squared error, and R-squared, the research assesses the effectiveness of different machine learning algorithms in predicting future stock prices. The findings suggest that while no single model can guarantee accurate predictions, the integration of multiple techniques and features can improve forecast reliability, offering valuable insights for investors and financial analysts in decision-making processes.

"Price Prediction in Stock Market Using Machine Learning Techniques ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 8, page no.f13-f15, August-2025, Available :http://www.jetir.org/papers/JETIR2508504.pdf

"Price Prediction in Stock Market Using Machine Learning Techniques ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 8, page no. ppf13-f15, August-2025, Available at : http://www.jetir.org/papers/JETIR2508504.pdf