Thesis Abstract

Abstract
The stock market is a complex and dynamic environment influenced by various factors, making it challenging for investors to predict trends accurately. In recent years, machine learning techniques have gained popularity for their ability to analyze large datasets and extract valuable insights. This thesis focuses on the development of a machine learning-based system for predicting stock market trends. The primary objective is to leverage historical stock market data and advanced machine learning algorithms to build a predictive model that can forecast future market trends with high accuracy. The research begins with a comprehensive literature review to explore existing studies on stock market prediction, machine learning applications in finance, and relevant algorithms. The study then delves into the research methodology, detailing the data collection process, feature selection techniques, model training, and evaluation methods. Various machine learning algorithms, including regression, classification, and ensemble methods, are implemented and compared to identify the most effective approach for stock market trend prediction. In the discussion of findings, the performance of the developed machine learning models is thoroughly analyzed, evaluating their accuracy, precision, recall, and other relevant metrics. The results demonstrate the effectiveness of the proposed system in predicting stock market trends, showcasing its potential to assist investors in making informed decisions and maximizing returns on their investments. The limitations of the study, such as data availability constraints and model complexity, are also discussed, providing insights for future research in this area. In conclusion, the thesis summarizes the key findings and contributions of the research, highlighting the significance of developing a machine learning-based system for predicting stock market trends. The study underscores the importance of leveraging advanced technologies to enhance decision-making in the financial sector and emphasizes the potential benefits of integrating machine learning into stock market analysis. Overall, this research contributes to the growing body of knowledge on machine learning applications in finance and provides valuable insights for both academic researchers and practitioners in the field of stock market prediction.

Thesis Overview