Project Abstract

Abstract
The use of machine learning in predicting stock prices has gained significant attention in recent years due to its potential to enhance financial decision-making processes. This research project aims to explore the application of machine learning algorithms in predicting stock prices and evaluating their performance compared to traditional methods. The study will focus on developing and testing machine learning models using historical stock price data and relevant financial indicators. Chapter One Introduction 1.1 Introduction 1.2 Background of Study 1.3 Problem Statement 1.4 Objectives of Study 1.5 Limitations of Study 1.6 Scope of Study 1.7 Significance of Study 1.8 Structure of the Research 1.9 Definition of Terms Chapter Two Literature Review 2.1 Overview of Stock Price Prediction 2.2 Traditional Methods in Stock Price Prediction 2.3 Machine Learning Algorithms in Finance 2.4 Applications of Machine Learning in Stock Price Prediction 2.5 Performance Evaluation Metrics 2.6 Challenges and Limitations 2.7 Recent Developments in the Field 2.8 Comparative Analysis of Methods 2.9 Data Preprocessing Techniques 2.10 Feature Selection and Engineering Chapter Three Research Methodology 3.1 Research Design 3.2 Data Collection 3.3 Data Preprocessing 3.4 Model Development 3.5 Model Evaluation 3.6 Performance Metrics 3.7 Experimental Setup 3.8 Validation Techniques Chapter Four Discussion of Findings 4.1 Analysis of Results 4.2 Comparison of Machine Learning Models 4.3 Interpretation of Model Performance 4.4 Impact of Feature Selection 4.5 Robustness of Models 4.6 Insights from Predictions 4.7 Implications for Financial Decision Making Chapter Five Conclusion and Summary 5.1 Summary of Findings 5.2 Conclusion 5.3 Contributions to the Field 5.4 Recommendations for Future Research In conclusion, this research project will contribute to the existing knowledge on the application of machine learning in predicting stock prices. By developing and evaluating machine learning models, the study aims to provide insights into the effectiveness of these methods in the financial domain. The findings will have implications for investors, financial analysts, and researchers seeking to leverage advanced technologies for stock price prediction.

Project Overview