Thesis Abstract

Abstract
This thesis presents a comprehensive study on the development of a machine learning model for predicting stock market trends. The aim of this research is to leverage machine learning techniques to analyze historical stock market data and forecast future trends with improved accuracy. The project involves investigating various machine learning algorithms, data preprocessing techniques, and feature engineering methods to build a robust predictive model. The introduction section provides an overview of the research problem, highlighting the importance of accurate stock market predictions for investors, financial institutions, and policymakers. The background of the study delves into the existing literature on stock market prediction using machine learning and identifies gaps in current research. The problem statement outlines the challenges faced in accurately predicting stock market trends, such as market volatility, unpredictable events, and data noise. The objectives of the study include developing a machine learning model that can forecast stock prices with high precision and exploring the impact of different features on prediction performance. The limitations of the study are discussed, acknowledging potential constraints such as data availability, model complexity, and inherent uncertainties in financial markets. The scope of the study defines the boundaries within which the research will be conducted, focusing on specific stock market indices or sectors. The significance of the study emphasizes the potential benefits of accurate stock market predictions, including improved investment decisions, risk management strategies, and economic forecasting. The structure of the thesis outlines the organization of chapters, from literature review to research methodology, findings discussion, and conclusion. The literature review chapter synthesizes existing research on stock market prediction models, machine learning algorithms, and data analysis techniques. It explores the strengths and limitations of different approaches and identifies key factors influencing prediction accuracy. The research methodology chapter details the data collection process, feature selection methods, model training and evaluation techniques, and performance metrics used to assess the predictive model. It also discusses the experimental setup, including dataset sources, preprocessing steps, and model validation procedures. The discussion of findings chapter presents the results of the machine learning model evaluation, including accuracy metrics, prediction errors, feature importance analysis, and comparison with baseline models. It interprets the implications of the findings and discusses potential areas for further research. In conclusion, this thesis contributes to the field of stock market prediction by developing a machine learning model that demonstrates improved forecasting capabilities. The study highlights the importance of feature selection, data quality, and model interpretability in enhancing prediction accuracy. Future research directions include exploring ensemble learning techniques, deep learning architectures, and alternative data sources for stock market analysis. Keywords Stock market prediction, Machine learning, Data analysis, Feature engineering, Financial forecasting.

Thesis Overview