Thesis Abstract

Abstract
This thesis investigates the effectiveness of various machine learning algorithms in predicting stock prices. The stock market is a complex and dynamic system influenced by a multitude of factors, making accurate predictions challenging. Machine learning algorithms have shown promise in analyzing large datasets and identifying patterns that can potentially improve stock price predictions. This research aims to compare and evaluate the performance of different machine learning algorithms in predicting stock prices and to provide insights into their effectiveness. The study begins with an introduction, providing background information on the stock market, the importance of stock price prediction, and the role of machine learning algorithms in this context. The problem statement highlights the challenges faced in predicting stock prices accurately, emphasizing the need for more advanced analytical techniques. The objectives of the study are outlined to guide the research process, followed by a discussion of the limitations and scope of the study. A comprehensive literature review in chapter two explores existing research on stock price prediction and the application of machine learning algorithms in this domain. The review covers various algorithms such as linear regression, decision trees, random forests, support vector machines, and neural networks, highlighting their strengths and weaknesses in predicting stock prices. Chapter three details the research methodology, including data collection, preprocessing, feature selection, model training, and evaluation. The methodology section also describes the dataset used for analysis and the performance metrics employed to assess the predictive accuracy of the machine learning algorithms. In chapter four, the findings of the study are presented and discussed in detail. The performance of each machine learning algorithm is evaluated based on metrics such as accuracy, precision, recall, and F1-score. The results are compared to identify the most effective algorithms for predicting stock prices accurately. Finally, chapter five provides a conclusion and summary of the thesis, highlighting the key findings, implications, and recommendations for future research. The study contributes to the existing body of knowledge on stock price prediction by providing insights into the effectiveness of different machine learning algorithms in this context. Overall, this research aims to enhance our understanding of how machine learning algorithms can be utilized to improve stock price predictions and inform investment decisions in the financial markets.

Thesis Overview