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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/150175


    Title: 基於機器學習與深度學習之房價預測
    Housing Price Prediction Based on Machine Learning and Deep Learning
    Authors: 胡程鈞
    Hu, Cheng-Jun
    Contributors: 呂桔誠
    林士貴
    Lyu, Jye-Cherng
    Lin, Shih-Kuei
    胡程鈞
    Hu, Cheng-Jun
    Keywords: 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
    Housing Price Prediction
    Machine Learning
    Deep Learning
    Deep Neural Network
    Generative Adversarial Network
    Random Forest
    XGBoost
    Date: 2024
    Issue Date: 2024-03-01 13:45:23 (UTC+8)
    Abstract: 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.
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    Description: 碩士
    國立政治大學
    國際金融碩士學位學程
    111ZB1009
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0111ZB1009
    Data Type: thesis
    Appears in Collections:[國際金融碩士學位學程] 學位論文

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