Integrating Machine Learning Techniques for Real Estate Analysis

Abstract

Machine learning can be applied to prediction models. The study delved into house price prediction, an area of significance to both individual participants and policymakers within and beyond the real estate market. The advanced predictive model is a tool that informs quality decision-making. This study synthesizes and builds on existing research to implement and enhance house price prediction methods, assessing the performance of Convolutional Neural Networks (CNNs), Decision Trees, and K-Nearest Neighbors (KNN) which is of focus on Most Correlated Features (KNN-MCF). Although these techniques have been developed and refined over the years, they still face challenges such as overfitting, noises existing in the dataset, and the complexity of modeling both linear and non-linear relationships. By integrating the resources, the study aims to provide objective guides for addressing the common challenges and predicting house prices, illustrating the appropriate analytical methods to apply based on characteristics of a database, extra space for improvements, and critical concerns of their practical applications. Ultimately, the research strives to bridge the gap between theoretical models and real-world applications to develop practical tools for reliable house price prediction.