This research examines various algorithms and techniques for stock price prediction. Utilizing historical stock data, we developed machine learning models, including linear regression, decision trees, and neural networks. The study evaluates which model demonstrates the best performance in terms of accuracy and reliability.After preprocessing the data, we trained the models and assessed their performance. Our results indicate that deep learning models, particularly recurrent neural networks (RNNs), are superior in predicting future trends in stock prices. These findings can be beneficial for investors and financial analysts looking to enhance their decision-making processes in the stock market.This research investigates various algorithms and techniques for predicting stock prices. By utilizing historical stock data, we developed several machine learning models, including linear regression, decision trees, and neural networks. The primary objective of this study is to determine which model exhibits the best performance regarding accuracy and reliability in forecasting stock prices.To prepare the data, we handled missing values, scaled features, and divided the dataset into training and testing sets. After training the models, we evaluated their performance using metrics such as mean absolute error (MAE) and root mean square error (RMSE).Our findings indicate that deep learning models, particularly recurrent neural networks (RNNs), outperform traditional models in predicting future trends in stock prices. Additionally, we analyzed feature importance, revealing which factors have the most significant impact on stock prices. These insights can be valuable for investors and financial analysts seeking to enhance their decision-making processes in the stock market.
Machine Learning, Deep Learning, Recurrent neural networks, time series analysis, feature engineering, mean absolute error, root mean square error Stock market trends, data processing, predictive analytics
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