Citation:

Madan Mohan Tito Ayyalasomayajula, Santhosh Bussa, Sailaja Ayyalasomayajula, 2021. "Forecasting Home Prices Employing Machine Learning Algorithms: XGBoost, Random Forest, and Linear Regression", ESP Journal of Engineering & Technology Advancements  1(1): 125-133.

Abstract:

Accurate forecasting of home prices is crucial for all stakeholders in the real estate market, including buyers, sellers, and investors. This study examines the efficacy of various machine learning algorithms in predicting house prices by analyzing large datasets that encompass diverse property attributes such as size, location, and bedroom count. Linear Regression is a baseline among the models investigated due to its simplicity and interpretability. Random Forest, known for its capability to model complex, non-linear relationships between features, provides a robust ensemble approach. Enhancing prediction accuracy further, XGBoost, a gradient-boosting technique, demonstrates superior performance. Implementing these models utilizes Python with libraries such as Scikit-learn for model development and Pandas for data processing. Model performance is evaluated through metrics like Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The comparative analysis reveals that while Linear Regression offers straightforward interpretability, XGBoost consistently surpasses Random Forest in prediction accuracy. The study emphasizes the significance of feature engineering in enhancing model performance and highlights the importance of selecting the appropriate model for reliable home value forecasting. These insights hold practical value for the real estate sector, contributing to more precise and effective predictive models.

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Keywords:

Machine Learning, Real Estate Analytics, Linear Regression, Random Forest, XGBoost, Predictive Modeling, Feature Engineering, Python, Data Preprocessing, Scikit-learn, RMSE (Root Mean Square Error).