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Business success prediction in Rwanda: a comparison of tree-based models and logistic regression classifiers

Author

Listed:
  • Francis Kipkogei

    (University of Rwanda)

  • Ignace H. Kabano

    (University of Rwanda
    University of Rwanda)

  • Belle Fille Murorunkwere

    (University of Rwanda
    Rwanda Revenue Authority)

  • Nzabanita Joseph

    (University of Rwanda
    University of Rwanda)

Abstract

Businesses contribute immensely to economic growth. However, many enterprises started fail within a year of their operation. This study seeks to predict business success, elucidating on factors affecting the success of a business based on recent data for timely intervention. The study used Rwanda Revenue Authority data. Tree-based models were compared with logistic regression for the prediction of the business success. Log loss, Area under Receiver-Operating Characteristic Curve, accuracy, recall, and F1 score were used to evaluate the performance of each model in differentiating between successful and unsuccessful business. Tree-based ensemble models were more robust than other classifiers. However, gradient boosting was the most robust model. The results showed that the business industry (sector of the economy) is the most important factor determining business success. Other important factors are the nature of the business and type of ownership, duration of operation, and location of the business.

Suggested Citation

  • Francis Kipkogei & Ignace H. Kabano & Belle Fille Murorunkwere & Nzabanita Joseph, 2021. "Business success prediction in Rwanda: a comparison of tree-based models and logistic regression classifiers," SN Business & Economics, Springer, vol. 1(8), pages 1-19, August.
    • Handle: RePEc:spr:snbeco:v:1:y:2021:i:8:d:10.1007_s43546-021-00104-2
    DOI: 10.1007/s43546-021-00104-2
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    References listed on IDEAS

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