Predicting House Prices Using Machine Learning Models

Abstract

Predicting house prices is crucial for stakeholders such as buyers, developers, financial institutions, and policymakers. This paper explores how machine learning models can help with house price prediction. Using the Ames Housing Dataset, we compare three models: linear regression, decision tree, and random forest. The goal is to understand which model gives better results in terms of accuracy and how well it works with new data. Data preprocessing, including imputation for missing values, standardization, and feature selection, was performed to ensure compatibility with the models. The models were tested using three common evaluation tools: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the R² score. The results show that the random forest model performs the best. It has the lowest error and the highest R² score among the three. This is because random forest uses many decision trees and combines their results, which helps reduce overfitting and improves prediction. The study highlights the potential of machine learning in improving house price prediction accuracy and discusses future avenues for model optimization. These include incorporating real-time data, enhancing model adaptability, and tailoring predictions for specific stakeholder needs. The findings suggest that machine learning models, particularly random forest, can provide valuable insights into housing market trends and support more informed decision-making.