Stock Reliance Power DataSet

Stock prediction holds significant value for sectors like banking, investment, and stockexchange operations. A vast amount of historical data is thus used for the prediction of stock prices. Variousfactors such as market news, daily opening and closing prices, significantly affect stock value changes.Investing in the stock market comes with both challenges and the potential for profit. However, because ofthe stock market\u2019s inherent complexity and irregularity, traditional models often fail to deliver consistentresults. This model introduces a stock forecasting approach using ARIMA which excels in stock priceprediction by effectively identifying time-based trends that models like LSTM and Linear Regression oftenmiss. Unlike LSTM, which may overfit on limited data, and Linear Regression, which struggles with nonstationarytrends, ARIMA handles structured time series more accurately. Its focus on autoregressive andmoving average components helps reduce prediction errors. To improve prediction accuracy, it is furthersupported by analyzing the sentiment in news headlines. This helps provide practical suggestions on whetherto buy a stock based on both price patterns and current market sentiment.