Comparative Performance Analysis Of Machine Learning And Deep Learning Models For Nse Stock Market Prediction
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Abstract
The ever-changing and unpredictable nature of financial markets makes examination of stock market predictions an attractive topic for research in finance analytics and artificial intelligence. Predicting the NSE stock market using ML/DL methods is the focus of this research. When making predictions, many models were employed, such as decision tree (DT), random forest (RF), Support Vector Machine (SVM), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The data set included information on the starting and closing prices as well as the highest and lowest prices, as well as the volume of trades. Before being used for modelling, the data in the database underwent cleaning, normalisation, and feature selection. We used Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and prediction accuracy to measure each model's performance. The experiment's findings demonstrate that when it comes to stock market prediction, deep learning (DL) models perform better than machine learning (ML) models. When compared to other models, the Transformer model provided the most accurate predictions and the smallest discrepancy between the actual and anticipated values, making it the clear winner for effective prediction.
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