预测迪拜房地产价格的机器学习算法比较分析

Abdulsalam Elnaeem Balila, A. Shabri
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引用次数: 0

摘要

预测房地产价格是房地产市场的一项重要任务,而机器学习算法为准确预测提供了宝贵的工具。在本研究中,我们全面比较了八种著名的机器学习算法,即集合经验模式分解(EEMD)-随机(S)+确定性(D)-支持向量机(EEMD-SD-SVM)、支持向量机(SVM)、梯度提升、随机森林、K-近邻(KNN)、线性回归、人工神经网络(ANN)和决策树。重点是预测迪拜的房地产价格,主要目的是评估这些算法在这一特定市场背景下的预测性能:评估基于四个关键性能指标:R 方 (R2)、均方误差 (MSE)、均方根误差 (RMSE) 和平均绝对百分比误差 (MAPE)。这些指标有助于深入了解预测误差、以百分比表示的准确性以及自变量解释的物业价格变异比例。本研究比较了每种算法在预测迪拜房地产价格方面的优势和局限性,并根据决策边界的性质、复杂数据的处理、局部模式的捕捉以及可解释性,强调了某些算法的优势所在。EEMD-SD-SVM 和 SVM 在需要精确决策边界的情况下表现出色,而梯度提升和随机森林在处理复杂和高噪声的房产价格数据时表现稳健。KNN 能有效捕捉局部模式,线性回归适用于简单的回归任务,ANN 在处理大量数据集时表现出色,而决策树在理解影响房地产价格的因素时具有可解释性。此外,还讨论了实际应用中的计算效率、模型可解释性和可扩展性等实际问题。比较分析为利益相关者提供了有价值的指导,包括房地产专业人士、数据科学家以及对选择最适合预测迪拜房地产价格的机器学习算法感兴趣的利益相关者,重点关注 MSE、RMSE、MAPE 和 R2 等基本评估指标。本研究深入探讨了不同机器学习算法在预测迪拜房地产价格方面的适用性和性能。房地产中介、买家、卖家或投资者等利益相关者可以利用这些见解,在迪拜房地产市场上做出明智的决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparative analysis of machine learning algorithms for predicting Dubai property prices
Predicting property prices is a crucial task in the real estate market, and machine learning algorithms offer valuable tools for accurate predictions. In this study, we introduce a comprehensive comparison of eight well-known machine learning algorithms, namely, ensemble empirical mode decomposition (EEMD)–stochastic (S) + deterministic (D)–support vector machine (EEMD-SD-SVM), support vector machine (SVM), gradient boosting, random forest, K-nearest neighbors (KNN), linear regression, artificial neural networks (ANN), and decision trees. The focus is on predicting property prices in Dubai, with the primary objective of assessing the predictive performance of these algorithms within this specific market context.The evaluation is based on four key performance metrics: R-squared (R2), mean squared error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). These metrics provide insights into prediction errors, accuracy in percentage terms, and the proportion of variance in property prices explained by independent variables. The study compares the strengths and limitations of each algorithm for predicting property prices in Dubai, highlighting scenarios where certain algorithms excel based on the nature of decision boundaries, handling complex data, capturing localized patterns, and offering interpretability.Findings from the comparative analysis shed light on the performance of each algorithm in predicting property prices in Dubai. EEMD-SD-SVM and SVM excel in scenarios requiring precise decision boundaries, while gradient boosting and random forests demonstrate robust performance with complex and noisy property price data. KNN captures localized patterns effectively, linear regression is suitable for straightforward regression tasks, ANN excels with extensive datasets, and decision trees offer interpretability in understanding factors influencing property prices.The study emphasizes the significance of model tuning, feature selection, and data pre-processing to enhance predictive power. Additionally, practical aspects such as computational efficiency, model interpretability, and scalability in real-world applications are discussed. The comparative analysis provides valuable guidance for stakeholders, including real estate professionals, data scientists, and stakeholders interested in selecting the most suitable machine learning algorithm for predicting property prices in Dubai, with a focus on the essential evaluation metrics of MSE, RMSE, MAPE, and R2. This study offers insights into the applicability and performance of different machine learning algorithms for predicting property prices in Dubai. Stakeholders such as real estate agents, buyers, sellers, or investors can leverage these insights to make informed decisions in the Dubai real estate market.
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