Research on Machine Learning based House Price Prediction and Stock Data Visualization

Abstract

The mechanism and prediction of stock market volatility constitute a long-standing core research focus within financial engineering. Machine learning stands out due to its exceptional capacity for modeling nonlinear structures and has consequently become a principal technique in stock price prediction. It effectively uncovers complex market interrelations and underlying trends. A common limitation in existing research, however, lies in inadequate model interpretability and a lack of integrated visualization support. To bridge these gaps, this research selects Vanke A (000002)—a representative real estate firm—as the case study, builds a stock prediction model using the random forest algorithm, embeds visualization features, and ultimately develops an end-to-end decision support system. Empirical findings reveal that the model successfully tracks short-term stock price fluctuations and exhibits strong predictive consistency and robustness in stable market climates. Further analysis of feature importance underscores the persistent influence of volume-price indicators—such as opening price, historical trading volume, and turnover rate—as pivotal drivers of stock price movement, reaffirming the dominant role of transactional data in short-term forecasting. Theoretically, this work not only corroborates the efficacy of ensemble learning in modeling financial time series but also accentuates the critical role of visualization tools in enhancing model transparency and supporting investment decisions.