A Comparative Study of House Price Prediction Using Linear Regression and Random Forest Models

The accurate prediction of house prices is crucial for stakeholders in the real estate sector, financial institutions, and urban planners. It not only informs investment decisions but also aids in policy formulation and market analysis. This research paper delves into the comparison of two prominent predictive analytics techniques-Linear Regression and Random Forest—to ascertain their effectiveness in forecasting house prices. Using a Kaggle dataset, this study analyzes key house price predictors such as building classification, living area size, construction year, and land area. The analysis shows Random Forest outperforms Linear Regression in accuracy, emphasizing the importance of building classification and living area in price prediction. Detailed visualizations, like feature importance graphs and scatter plots, offer clear insights into model performance. This research contributes significantly to real estate predictive analytics, offering insights to guide investment strategies and policy-making. It also opens avenues for exploring alternative machine learning approaches and socio-economic factors for a more comprehensive understanding of housing market dynamics.