政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/151537
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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/151537


    Title: 基於機器學習預測中古車車價之應用
    Application of predicting used car prices based on machine learning
    Authors: 林侃瑩
    Lin, Kan-Ying
    Contributors: 鄭宇庭
    Cheng, Yu-Ting
    林侃瑩
    Lin, Kan-Ying
    Keywords: 中古車價預測
    機器學習
    集成學習
    Date: 2024
    Issue Date: 2024-06-03 11:54:20 (UTC+8)
    Abstract: 台灣地區的中古車價格預估主要依賴專家的主觀判斷，這種方式存在一些缺點，包括耗費大量人力資源、資訊不對稱等問題。因此，本研究旨在探索機器學習方法，以提高中古車價格的預測準確性。此外，我們還研究了中古車價格的重要變數，以提供中古車市場決策之依據。
    本研究使用abc好車網(https://www.abccar.com.tw/)提供的中古車資料，包括廠牌、車款、里程數、出廠時間、燃料、排氣、顏色、變速系統、傳動系統、座位數、駕駛配備、內裝配備、安全配備、外觀配備以及價格等。我們採用不同的機器學習模型，包括決策樹、支持向量機、類神經網路和集成學習方法，以確定最適合的模型來預測中古車價格。
    我們進行不同降維方式包括主成分分析（PCA）降維、LASSO變數篩選和特徵值重要性分析等不同特徵選擇方法。結果顯示，使用LASSO變數篩選方法的模型表現優於其他方法，證明了LASSO的有效性。此外，我們分析了中古車價格預測的重要變數，包括車款、車齡和排氣量等因素對價格預測的影響。這些因素通常是消費者在選擇中古車時關注的主要考慮因素。此外，廠商、里程數、座位數、傳動系統和手自排變速系統等因素也對價格預測有重要影響。
    最後在不同模型和方法的比較，建議使用Light GBM和Hist Gradient Boosting這兩種模型，因為它們在整體性能上表現出色，同時在實際計算中也能夠維持較高的效率。
    總結來說，這項研究進行了多種模型和方法的比較，以找出最適合預測中古車價格的方式。我們的研究強調了模型選擇對預測性能的關鍵性作用。我們期望這些研究結果能夠為中古車市場相關的決策制定和價格預測提供有價值的參考依據。
    Reference: 英文參考文獻
    1.Asghar, M., K. Mehmood, S. Yasin & Z. M. Khan (2021). Used cars price prediction using machine learning with optimal features. Pakistan Journal of Engineering and Technology, 4(2), 113-119.
    2.Breiman, L., J. H. Friedman, R. A. Olshen & C. J. Stone (1984). Classification and regression trees (Wadsworth, Belmont, CA). ISBN-13, 978-0412048418.
    3.Hesterberg, T., N. H. Choi, L. Meier & C. Fraley(2008). Least angle and ℓ 1 penalized regression: A review. Statistics Surveys, Statist. Surv., 61-93.
    4.McCulloch, W. S. & W. Pitts(1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics,5, 115-133.
    5.Mitchell, T. M. (1997). Machine learning. McGraw-Hill.
    6.Monburinon, N., P. Chertchom, T. Kaewkiriya, S. Rungpheung, S. Buya & P. Boonpou(2018). Prediction of prices for used car by using regression models. In 2018 5th International Conference on Business and Industrial Research (ICBIR) , 115-119. IEEE.
    7.Narayana, C. V., C. L. Likhitha, S. Bademiya & K. Kusumanjali(2021). Machine learning techniques to predict the price of used cars: predictive analytics in retail business. In 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC) , 1680-1687. IEEE.
    8.Noor, K. & S. Jan (2017). Vehicle price prediction system using machine learning techniques. International Journal of Computer Applications, 167(9), 27-31.
    9.Pudaruth, S. (2014). Predicting the price of used cars using machine learning techniques. Int. J. Inf. Comput. Technol, 4(7), 753-764..
    10.Quinlan, J. R. (2014). C4. 5: programs for machine learning. Elsevier.
    11.Samruddhi, K. & R. A. Kumar (2020). Used car price prediction using k-nearest neighbor based model. Int. J. Innov. Res. Appl. Sci. Eng.(IJIRASE), 4, 629-632.
    12.Schapire, R. E. (1990). The strength of weak learnability. Machine learning, 5(2), 197-227.
    13.Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B: Statistical Methodology, 58(1), 267-288.
    14.Waldmann, P., G. Mészáros, B. Gredler, C. Fuerst & J. Sölkner(2013). Evaluation of the lasso and the elastic net in genome-wide association studies. Frontiers in genetics,4, 270.
    15.Wolpert, D. H. (1992). Stacked generalization. Neural networks, 5(2), 241-259.
    網站部分
    16.(N.d.). 交通部公路總局 統計查詢網. https://stat.thb.gov.tw/hb01/webMain.aspx?sys=100&funid=defjsp
    17.Cozzi, L., & Petropoulos, A. (2021). Global SUV Sales Set Another Record in 2021, Setting Back Efforts to Reduce Emissions. Iea. https://www.iea.org/commentaries/global-suv-sales-set-another-record-in-2021-setting-back-efforts-to-reduce-emissions
    18.Factors That Impact The Value of Your Car. (n.d.). CARCHASE. https://carchase.com.au/resources/car-valuation-guide/9-factors-that-impact-the-value-of-your-car/
    Description: 碩士
    國立政治大學
    統計學系
    111354008
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0111354008
    Data Type: thesis
    Appears in Collections:[Department of Statistics] Theses

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