| Abstrakt: |
Using the Nifty 50 dataset, this research investigates the use of deep learning and ML algorithms for real-time stock market price prediction in India. We implement and compare a number of algorithms, such as SVM, KNN, Decision Tree, Random Forest, Ada Boost, Extreme Gradient Boosting, Naïve Bayes, Linear Regression, ANN, LSTMs, RNN, and GRU. The research focuses on predicting accuracy since stock markets are dynamic. The results show that LSTM performs better than other algorithms and is better at capturing complex patterns in stock prices. The thorough comparison using visual aids highlights how well LSTM manages the intricacies of the Nifty 50 dataset and highlights the technology's potential for precise and trustworthy stock market forecasts in unstable financial situations. [ABSTRACT FROM AUTHOR] |
