Thesis Abstract

Abstract
The stock market is a complex and dynamic system influenced by various factors, making accurate prediction of stock prices a challenging task. This study explores the applications of machine learning techniques in predicting stock prices, aiming to enhance decision-making processes for investors and financial analysts. The research methodology involves a comprehensive literature review, data collection, feature engineering, model training and evaluation, and interpretation of results. Chapter One provides an introduction to the study, presenting the background, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. Chapter Two consists of a detailed literature review covering ten key aspects related to machine learning in stock price prediction. Chapter Three focuses on the research methodology, detailing the data collection process, feature selection techniques, model development, evaluation metrics, validation methods, and experimental setup. This chapter also discusses the challenges encountered and the strategies employed to address them. In Chapter Four, the findings of the study are analyzed and discussed in depth. The performance of various machine learning models in predicting stock prices is evaluated, highlighting the strengths and weaknesses of each approach. The impact of different features on prediction accuracy is also examined. Finally, Chapter Five presents the conclusion and summary of the project thesis. The key findings, implications, and recommendations for future research are discussed, emphasizing the importance of machine learning in enhancing stock price prediction accuracy. Overall, this study contributes to the growing body of research on the applications of machine learning in financial forecasting and provides valuable insights for stakeholders in the stock market. Keywords Machine Learning, Stock Prices, Prediction, Financial Forecasting, Data Analysis, Decision-Making

Thesis Overview