Project Abstract

Abstract
The utilization of machine learning techniques in predicting stock prices has become increasingly popular in recent years due to its potential to enhance investment decision-making processes. This research project aims to investigate the effectiveness of machine learning algorithms in predicting stock prices and to evaluate their performance against traditional forecasting methods. The study will focus on developing and implementing predictive models using historical stock data and various machine learning algorithms such as support vector machines, random forests, and neural networks. The research will be structured into five main chapters. Chapter one will provide an introduction to the research topic, present the background of the study, define the problem statement, outline the objectives, discuss the limitations and scope of the study, highlight the significance of the research, and provide a structure of the overall research. Additionally, chapter one will include a definition of key terms to ensure clarity and understanding of the research context. Chapter two will consist of a comprehensive literature review, covering ten key aspects related to the application of machine learning in predicting stock prices. This section will explore existing research, theories, and methodologies used in the field, providing a solid foundation for the research project. Chapter three will detail the research methodology employed in the study. This chapter will include discussions on data collection methods, data preprocessing techniques, feature selection strategies, model development, and model evaluation procedures. Additionally, it will outline the criteria for selecting machine learning algorithms and explain the process of training and testing the models. In chapter four, the research findings will be presented and discussed in detail. This section will analyze the performance of the developed machine learning models in predicting stock prices and compare them with traditional forecasting methods. The discussion will include insights into the accuracy, efficiency, and robustness of the predictive models, highlighting their strengths and limitations. Finally, chapter five will provide a conclusion and summary of the research project. This section will offer a comprehensive overview of the key findings, discuss the implications of the results, and suggest recommendations for future research in this area. The conclusion will also reflect on the significance of the study and its potential impact on the field of stock price prediction. Overall, this research project aims to contribute to the growing body of knowledge on the application of machine learning in predicting stock prices. By evaluating the performance of machine learning algorithms in this context, the study seeks to provide valuable insights for investors, financial analysts, and researchers interested in leveraging advanced computational techniques for stock market forecasting.

Project Overview