Project Abstract

Abstract
The application of machine learning techniques in predicting stock prices has become a prevalent research area in the field of finance. This study aims to explore the effectiveness of various machine learning algorithms in forecasting stock prices and to evaluate their performance against traditional stock prediction methods. The research will focus on developing predictive models using historical stock data and relevant financial indicators to forecast future stock prices accurately. The study will begin with an introduction to the topic, providing background information on the significance of stock price prediction in financial markets. A detailed literature review will be conducted in Chapter Two, examining existing research on machine learning applications in stock price prediction and comparing the performance of different algorithms. Chapter Three will outline the research methodology, including data collection procedures, feature selection, model development, and evaluation techniques. Various machine learning algorithms, such as linear regression, decision trees, random forests, and neural networks, will be implemented and compared based on their predictive accuracy and robustness. In Chapter Four, the findings of the study will be presented and discussed in detail. The performance of each machine learning algorithm will be analyzed, highlighting their strengths and limitations in predicting stock prices accurately. The research will also explore the impact of different features and parameters on the predictive performance of the models. Finally, Chapter Five will provide a comprehensive conclusion and summary of the research findings. The study will discuss the implications of the results, practical applications of the predictive models in real-world stock trading scenarios, and recommendations for future research in this area. Overall, this research aims to contribute to the growing body of knowledge on the application of machine learning in predicting stock prices and provide insights into improving the accuracy and reliability of stock price forecasts using advanced computational techniques.

Project Overview