STOCK MARKET ANALYSIS

The stock market is a complex and dynamic system, and predicting its behaviour is a challenging task. This research paper presents a comparative study of machine learning algorithms for predictive modelling of stock market analysis. We evaluate the performance of six machine learning algorithms, including Linear Regression, Decision Trees, Random Forest, Support Vector Machines, Artificial Neural Networks, and Gradient Boosting, on a dataset of historical stock prices. Our results show that the Gradient Boosting algorithm outperforms the other algorithms in terms of accuracy, precision, and recall. We also analyse the impact of feature engineering and hyperparameter tuning on the performance of the algorithms. The findings of this study can be used to develop predictive models for stock market analysis, which can aid investors and financial analysts in making informed decisions.