To explore the use of machine learning algorithms in predicting stock price movements in the education sector.
To analyze the impact of educational data on stock prices and identify key factors influencing stock price movements.
To develop a predictive model that can accurately forecast stock price movements based on educational data.
Collect and preprocess education-related data such as government funding for education, student enrollment numbers, and educational performance metrics.
Identify relevant features that may impact stock prices in the education sector.
Implement and compare various machine learning algorithms such as linear regression, decision trees, and neural networks for stock price prediction.
Evaluate the performance of the predictive model using metrics such as accuracy, precision, and recall.
Write a comprehensive research report detailing the methodology, results, and conclusions of the study.
