Project Abstract

Abstract
The dynamics of stock prices have always been a subject of great interest and challenge in the financial market. With the advent of machine learning techniques, there has been a growing interest in utilizing these advanced algorithms to predict stock prices more accurately. This research project aims to investigate the application of machine learning in predicting stock prices by conducting a comparative analysis of different machine learning models. The study begins with an introduction that highlights the significance of the research topic in the financial domain. The background of the study provides a comprehensive overview of the existing literature on stock price prediction and the role of machine learning in this domain. The problem statement identifies the key challenges and limitations faced in traditional stock price prediction methods, setting the stage for the research objectives. The primary objective of the study is to compare the performance of different machine learning models in predicting stock prices accurately. To achieve this, the research methodology section outlines the data collection process, feature engineering techniques, model selection criteria, and evaluation metrics used in the analysis. The scope of the study defines the specific stocks and time period considered for the comparative analysis. The literature review delves into the theoretical frameworks and empirical studies related to stock price prediction using machine learning algorithms. It explores the various machine learning models such as support vector machines, random forests, neural networks, and gradient boosting machines that have been applied in predicting stock prices. The research methodology section details the data sources, preprocessing techniques, feature selection methods, model training procedures, hyperparameter tuning, and cross-validation strategies employed in the study. The evaluation metrics used to assess the performance of the machine learning models include accuracy, precision, recall, F1 score, and mean squared error. The findings from the comparative analysis reveal the strengths and weaknesses of each machine learning model in predicting stock prices. The discussion in Chapter Four interprets the results, identifies the factors influencing the predictive performance, and highlights the implications for investors and financial analysts. In conclusion, the study summarizes the key findings, implications, and contributions to the field of stock price prediction using machine learning. The research underscores the importance of leveraging advanced algorithms to enhance the accuracy and efficiency of stock price forecasting, thereby assisting market participants in making informed investment decisions. Overall, this research project provides valuable insights into the application of machine learning in predicting stock prices through a comparative analysis of different models, offering a roadmap for future research in this evolving field.

Project Overview