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Table of Contents:
Chapter 1: Introduction
1.1 Background of the Study
1.2 Problem Statement
1.3 Research Objectives
1.4 Research Questions
1.5 Significance of the Study
1.6 Scope of the Study
1.7 Limitations of Study

Chapter 2: Literature Review
2.1 Overview of Machine Learning
2.2 Applications of Machine Learning in Stock Market Prediction
2.3 Previous Studies on Stock Market Prediction using Machine Learning
2.4 Theoretical Framework

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Analysis Techniques
3.4 Model Development
3.5 Evaluation Metrics

Chapter 4: Discussion of Findings
4.1 Data Preprocessing
4.2 Model Training and Testing
4.3 Performance Evaluation
4.4 Comparison with Existing Models
4.5 Interpretation of Results

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Conclusion
5.3 Contributions to the Field
5.4 Recommendations for Future Research
5.5 Practical Implications

Brief Overview:
Machine Learning has gained significant popularity in recent years due to its ability to analyze complex data and make predictions based on patterns and trends. In the context of stock market prediction, machine learning algorithms can be used to forecast stock prices, identify trading opportunities, and optimize investment strategies.

The objective of this thesis is to study the application of machine learning in stock market prediction and develop a predictive model that can accurately forecast stock prices. The study will review existing literature on the subject, analyze different machine learning algorithms, and propose a methodology for building and evaluating a predictive model.

The research will focus on collecting historical stock data, preprocessing the data, training and testing the predictive model, and evaluating its performance. The findings of the study will be discussed in detail, highlighting the strengths and weaknesses of the model and comparing it with existing approaches.

In conclusion, the thesis aims to contribute to the field of stock market prediction by demonstrating the effectiveness of machine learning techniques in forecasting stock prices. The study will provide valuable insights for investors, traders, and researchers interested in leveraging machine learning for stock market analysis.

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