Project Abstract

Abstract
The use of Machine Learning (ML) algorithms in predicting stock market trends has gained significant attention in recent years due to its potential to enhance investment strategies and decision-making processes. This research aims to explore the application of ML algorithms in predicting stock market trends and assess their effectiveness in generating accurate forecasts. The study will focus on analyzing historical stock market data and implementing various ML algorithms such as Support Vector Machines (SVM), Random Forest, and Neural Networks to predict future trends in stock prices. The research will commence with a comprehensive review of existing literature on the topic, examining previous studies and methodologies used in predicting stock market trends. This literature review will provide a theoretical foundation for the study and highlight the current trends and challenges in the field of stock market prediction using ML algorithms. Following the literature review, the research methodology will be detailed, outlining the data collection process, variables considered, and the specific ML algorithms selected for analysis. The methodology section will also discuss the evaluation metrics used to assess the performance of the ML algorithms in predicting stock market trends and compare their accuracy with traditional forecasting methods. In the subsequent chapter, the findings of the research will be presented and discussed in detail. The analysis will include the comparison of prediction accuracy among different ML algorithms, the identification of key factors influencing stock market trends, and the evaluation of the overall effectiveness of ML algorithms in stock market prediction. The discussion will also address the limitations and challenges encountered during the research process and provide recommendations for future studies in this area. Finally, the research will conclude with a summary of the key findings, implications for investors and financial analysts, and suggestions for further research in the field of applying ML algorithms in predicting stock market trends. The study aims to contribute to the existing body of knowledge on stock market prediction and provide valuable insights into the potential benefits and limitations of using ML algorithms in this domain. In conclusion, this research seeks to advance understanding of the application of ML algorithms in predicting stock market trends and offer practical implications for enhancing investment decision-making processes. By leveraging the power of ML algorithms, investors and financial institutions can potentially improve their forecasting accuracy and make more informed decisions in the dynamic and competitive stock market environment.

Project Overview