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@article{175303,
        author = {Bhoomi Narhire and Omkar Jagtap and Pranay hasabe and Tansihq yeshwante and Nripesh Narayan vatsa},
        title = {STOCK PRICE PREDICTION USING PYTHON},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {2058-2063},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175303},
        abstract = {Stock market prediction is still difficult despite developments because of its intrinsic volatility and outside influences like geopolitical and economic policies. A number of variables, such as company performance, macroeconomic indicators, market sentiment, and world events, affect stock prices. Social media and news sentiment analysis are now crucial parts of contemporary stock prediction models. But issues like overfitting, noisy data, and market volatility continue to be major roadblocks. Because financial markets are unpredictable, no model can guarantee perfect accuracy, even with advances in predictive accuracy. By creating hybrid models, applying reinforcement learning, and leveraging different data sources, future research seeks to improve accuracy. Even though AI-powered stock forecasting is still developing, investors need to use risk management techniques in conjunction with technology breakthroughs to make the best choices. This study examines a number of stock price prediction techniques, such as deep learning methods (like Long Short-Term Memory (LSTM) networks), machine learning algorithms (like Random Forest and Support Vector Machines), and statistical models (like ARIMA). To increase accuracy, these models make use of sentiment analysis, trading volumes, historical stock prices, and macroeconomic variables},
        keywords = {Stock price prediction, Machine learning, Pandas, Matplotlib},
        month = {April},
        }