How to Predict the Stock Price with Best Accuracy: How the households are losing money in trading according to SEBI report
Keywords:
nse,stock price prediction, data science, visualizationAbstract
Goal: Using data science and machine learning methods, forecast the stock price of a smallcap firm with the highest degree of accuracy. Method: A dataset comprising a year's worth of data gathered from several sources, including Yahoo Finance and NSE. The process of predicting a stock price includes gathering data, preprocessing it, testing, training, and fitting an algorithm. Finally, machine learning techniques are used to identify the best accuracy in the stock price forecast. Results: This model achieves a 96% accuracy rate. Compared to all other algorithms, the LSM algorithm yields a higher accuracy rate. We can also use different algorithms to estimate the stock price. But as of right present, the LSTM algorithm alone provides the best accuracy rate. Novelty: This research helps determine which algorithm is most effective in predicting stock prices in real-time market scenarios. International market factors, such as war events, might occasionally cause us to make inaccurate stock price predictions. In this instance, the entire index will only display as negative. The true difficulty in making stock price projections for the future is this. Predicting a stock price under all circumstances and market conditions is still a difficult task. In order for it to be successful, we must develop a new algorithm and modify older ones in light of current market trends.
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