Stock Market Analysis and Prediction usingRandom Forest.

The Stock Price Visualizer and Predictor system success- fully demonstrates the integration of machine learning, data visualization, and financial analytics for short-term stock market forecasting. The study aimed to develop a model capable of predicting stock price movements using historical data, engineered features, and an interpretable ensemble-based algorithm. Through systematic experimentation, the research validates that Random Forest Classifier, combined with fea- ture enhancement techniques, can moderately capture market trends despite the inherent volatility of financial time-series data. The implementation of this system involved a complete workflow encompassing data acquisition from Yahoo Finance, preprocessing to handle missing values, feature engineering using indicators such as Price Change and Simple Mov- ing Averages (SMA-5, SMA-10), and model training using Scikit-learn. The data was divided into training, testing, and validation subsets to ensure robust evaluation. The trained Random Forest model achieved an average accuracy of 51.52 percent, aligning with the expected benchmark for short-term predictions where market randomness significantly influences outcomes. A major contribution of this research lies in its user- centric visualization interface, developed using Node.js and Express.js, which allows users to visualize historical trends, observe model predictions, and verify outcomes in real time. The automated export of prediction results to Google Sheets ensures transparency and continuous monitoring of model performance, making it practical for academic, research, and investment use. The findings reaffirm that ensemble learning models such as Random Forest strike a balance between accuracy, interpretability, and computational efficiency. However, the un- predictability of financial markets restricts the model’s up- per bound of performance, suggesting that even the most sophisticated algorithms can provide probabilistic insights rather than deterministic forecasts. Future work can explore hybrid deep learning architectures (such as LSTM-Random Forest combinations), sentiment-based features, and real-time predictive streaming to enhance accuracy and adaptability. Additionally, integrating cloud deployment and automated retraining pipelines can extend the system’s scalability and operational efficiency. In conclusion, this research contributes to the growing body of work at the intersection of artificial intelligence and finance, establishing a strong foundation for intelligent stock analysis platforms that blend prediction, visu- alization, and decision support into a single integrated system.

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