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研究生:胡程鈞
研究生(外文):Hu, Cheng-Jun
論文名稱:基於機器學習與深度學習之房價預測
論文名稱(外文):Housing Price Prediction Based on Machine Learning and Deep Learning
指導教授:呂桔誠呂桔誠引用關係林士貴林士貴引用關係
指導教授(外文):Lyu, Jye-CherngLin, Shih-Kuei
口試委員:呂桔誠林士貴盧秋玲張元晨江穎慧
口試委員(外文):Lyu, Jye-CherngLin, Shih-KueiLu, Chiu-LingChang, Yuan-ChenChiang, Ying-Hui
口試日期:2024-01-20
學位類別:碩士
校院名稱:國立政治大學
系所名稱:國際金融碩士學位學程
學門:商業及管理學門
學類:財務金融學類
論文種類:學術論文
論文出版年:2024
畢業學年度:112
語文別:英文
論文頁數:68
中文關鍵詞:房價預測機器學習深度學習深度神經網路生成對抗網路隨機森林XGBoost
外文關鍵詞:Housing Price PredictionMachine LearningDeep LearningDeep Neural NetworkGenerative Adversarial NetworkRandom ForestXGBoost
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房屋貸款是許多金融機構的重要業務,準確的房價預測對於這些金融機構是否能夠做出適宜的放款決策以及管控相關風險尤其重要。本研究運用機器學習與深度學習演算法(深度神經網路、生成對抗網路、隨機森林和XGBoost)以及線性迴歸(基準模型)來進行臺北市區域房價指數預測(Study 1),美國波士頓城鎮房價中位數預測(Study 2),以及臺北市住宅大樓每坪單價預測(Study 3)。本研究結果顯示生成對抗網路的預測成效優於線性迴歸和深度神經網路,而隨機森林和XGBoost的預測成效則更優於生成對抗網路。
Housing loans are important businesses for many financial institutions. Accurate prediction of housing prices is crucial for these financial institutions to make appropriate lending decisions and manage associated risks. This study employs machine learning and deep learning algorithms (Deep Neural Network, Generative Adversarial Network, Random Forest, and XGBoost) and Linear Regression (Baseline Model) to predict housing price indices of districts in Taipei (Study 1), median housing prices of towns in Boston (Study 2), and housing prices per ping of residential buildings in Taipei (Study 3). The results of this research indicate that the predictive performance of Generative Adversarial Network is superior to that of Linear Regression and Deep Neural Network. However, Random Forest and XGBoost exhibit even better predictive performance than Generative Adversarial Network.
摘要 i
Abstract ii
Table of Contents iii
List of Tables v
List of Figures vi
1 Introduction 1
2 Literature Review 6
2.1 Hedonic Pricing Theory 6
2.2 Manual Housing Valuation Methods 7
2.3 Housing Price Prediction Models 9
2.4 Generative Adversarial Network 12
3 Methodology 16
3.1 Linear Regression 16
3.2 Deep Neural Network 17
3.3 Generative Adversarial Network 24
3.4 Random Forest 26
3.5 XGBoost 30
3.6 Evaluation Metrics 34
4 Empirical Studies 37
4.1 Study 1 37
4.2 Study 2 43
4.3 Study 3 48
5 Conclusion and Future Work 56
5.1 Conclusion 56
5.2 Future Work 58
Reference 63
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