Project Proposal

This project aims to revolutionize the way non-professional investors navigate the stock market. Leveraging cutting-edge data science and machine learning techniques, the proposal introduces a robust and accurate predictive model using the Random Forest algorithm. Through data sourcing, thorough cleaning, and preparation, this solution addresses the longstanding challenge of making informed investment decisions based on accurate stock market predictions. Additionally, Principal Component Analysis (PCA) is employed for feature reduction, and Grid Search Cross Validation fine-tunes model hyperparameters. The project not only builds upon the strengths of existing solutions but also fosters greater accessibility and democratizes quantitative trading, opening doors for non-professional investors to make informed and confident investment choices.