Optimizing House Price Prediction Models: A Hybrid Approach using GWO-Feature Selection and CatBoost Tuning

This function is a very important function in real estate, as stakeholders such as investors, buyers, and sellers make the right decisions. Correct property values are thus required for understanding market dynamics and optimizing investment strategies, especially for fair pricing. However, remains a critical challenge because of the multiplicity of housing data-the location, attributes of a property, and economic conditions among others. This study aims to fill these gaps by developing an advanced predictive model for house price estimation. Here, the focus is on improving the feature selection process so that more impactful variables are identified and optimized for more accurate predictions. In this section, it makes use of advanced computational techniques, such as the Grey Wolf Optimizer (GWO) for feature selection and CatBoost with RandomizedSearchCV for model building. The key findings include the efficiency of GWO in selecting the most relevant features, which enhance model interpretability and decrease overfitting. Besides, CatBoost with the RandomizedSearchCV yielded a considerable improvement in accuracy of prediction, thus giving proof of the model’s ability to be robust enough in handling complex and large data. The results therefore point to the fact that when feature selection is integrated with optimized modeling techniques, the predictions for house prices will be more reliable and accurate. It concludes that the proposed method can help deliver useful information regarding the real estate decision-making process and offer an efficient solution to predict the house price across different markets. The findings indicate that integrating optimized feature selection with advanced machine learning models leads to more reliable and accurate house price predictions. This research provides a practical solution for real estate valuation and offers valuable insights to stakeholders for market analysis and investment decision-making.