Comparative Analysis of the Effectiveness of Different Algorithms for House Price Prediction in Machine Learning

In today's economic climate, where housing is a major focus, predicting house prices has become essential for both buyers and investors. Accurate price prediction allows for more informed decision-making, helping to minimize losses and maximize potential profits. The paper explores the different machine learning algorithms to predict house prices, utilizing the classic Boston Housing dataset from Kaggle. The study compares the performance of four algorithms: Random Forest (RF), Decision Tree (DT), Gradient Boosting (GB), and Extreme GB (XGBoost). Experimental results demonstrate that GB and XGBoost outperform the other models, delivering the highest prediction accuracy. These algorithms excel in capturing complex, nonlinear relationships in the data, making them particularly effective for house price prediction. The findings suggest the models like GB and XGBoost can enhance the accuracy of prediction, offering valuable insights into the real estate market. The study also highlights the importance of continuously refining machine learning models to adapt to changing market conditions. By improving predictive models, the study contributes to an understanding of home price dynamics, which is critical for both home buyers and real estate investors, which is crucial for both homebuyers and real estate investors.