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@article{176080,
        author = {Ramakant Kori and Prof. Sanjiv Sharma},
        title = {Development Of Stock Price Prediction Model},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {7006-7011},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176080},
        abstract = {Predicting stock prices is a complex challenge due to the highly dynamic and volatile nature of financial markets. Traditional statistical models often fall short in capturing the intricate relationships between various market factors, resulting in suboptimal forecasting accuracy. With the rise of machine learning (ML), advanced computational models have emerged to analyze large-scale financial data, improving predictive capabilities.
This study explores the application of Artificial Neural Networks (ANN) and Random Forest (RF) for stock price forecasting. Following a structured ML workflow, this research demonstrates how ensemble learning enhances model stability and prediction reliability. The results indicate that ANN effectively identifies nonlinear market patterns, outperforming RF in accuracy.},
        keywords = {Stock Price Prediction, Machine Learning, Neural Networks, Random Forest, Ensemble Learning, Financial Forecasting},
        month = {April},
        }