Thesis Abstract

Abstract
The rapid advancement of technology has revolutionized various industries, including the financial sector. In recent years, the application of machine learning techniques in predicting stock prices has garnered significant attention due to its potential to enhance decision-making processes in the financial markets. This thesis explores the effectiveness of machine learning algorithms in predicting stock prices and aims to provide insights into their practical application. The study begins with a comprehensive introduction that outlines the background of the research, presents the problem statement, objectives, limitations, scope, significance, and the structure of the thesis. The literature review in Chapter Two critically examines existing studies on machine learning in stock price prediction, highlighting key concepts, methodologies, and findings. This chapter aims to provide a solid theoretical foundation for the research. Chapter Three details the research methodology employed in this study, including data collection methods, variables, and the selection of machine learning algorithms. The chapter also discusses the data preprocessing techniques, model evaluation methods, and the criteria used to assess the performance of the predictive models. Chapter Four presents an in-depth discussion of the findings obtained from applying machine learning algorithms to predict stock prices. The results are analyzed, interpreted, and compared with existing literature to evaluate the effectiveness of the models in predicting stock prices accurately. The chapter also discusses the implications of the findings and identifies areas for future research. Finally, Chapter Five provides a comprehensive conclusion and summary of the project thesis. The key findings, implications, and limitations of the study are summarized, and recommendations for future research and practical applications are discussed. Overall, this thesis contributes to the growing body of knowledge on the application of machine learning in predicting stock prices and provides valuable insights for researchers, practitioners, and policymakers in the financial industry. Keywords Machine Learning, Stock Prices, Prediction, Financial Markets, Data Analysis, Algorithm, Research Methodology.

Thesis Overview