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Predicting the price of second-hand vehicles using data mining techniques

Author

Listed:
  • Jafari Kang, Masood
  • Zohoori, Sepideh
  • Abbasi, Elahe
  • Li, Yueqing
  • Hamidi, Maryam

Abstract

The electronic commerce, known as “E-commerce”, has been boosted rapidly in recent years, and makes it possible to record all information such as price, location, customer’s review, search history, discount options, competitor’s price, and so on. Accessing to such rich source of data, companies can analyze their users’ behavior to improve the customer satisfaction as well as the revenue. This study aims to estimate the price of used light vehicles in a commercial website, Divar, which is a popular website in Iran for trading second-handed goods. At first, highlighted features were extracted from the description column using the three methods of Bag of Words (BOW), Latent Dirichlet Allocation (LDA), and Hierarchical Dirichlet Process (HDP). Second, a multiple linear regression model was fit to predict the product price based on its attributes and the highlighted features. The accuracy index of Actuals-Predictions Correlation, the min-max index, and MAPE methods were used to validate the proposed methods. Results showed that the BOW model is the best model with an Adjusted R-square of 0.7841.

Suggested Citation

  • Jafari Kang, Masood & Zohoori, Sepideh & Abbasi, Elahe & Li, Yueqing & Hamidi, Maryam, 2019. "Predicting the price of second-hand vehicles using data mining techniques," MPRA Paper 103933, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:103933
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    File URL: https://mpra.ub.uni-muenchen.de/103933/1/MPRA_paper_103933.pdf
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    More about this item

    Keywords

    Text mining; Topic modeling; BOW; LDA; HDP; Linear regression;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • Y10 - Miscellaneous Categories - - Data: Tables and Charts - - - Data: Tables and Charts

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