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الرئيسية > Predictive Analytics for Start...>الوصف

Predictive Analytics for Startups Success: Acquisition Prediction Based on Machine Learning Techniques

المصدر: مجلة العلوم الإدارية والاقتصادية
الناشر: جامعة القصيم - كلية الاقتصاد والإدارة
المؤلف الرئيسي: Mihoub, Alaeddine (Author)
المجلد/العدد: مج16, ع2
محكمة: نعم
الدولة: السعودية
التاريخ الميلادي: 2023
الشهر: أكتوبر
الصفحات: 99 - 115
ISSN: 1658-404X
رقم MD: 1433512
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: EcoLink
مواضيع:
التعليم الألي | الشركات الناشئة | المشكلات القانونية | الإنترنت
كلمات المؤلف المفتاحية:
Predictive Analytics | Startup Success | Machine Learning | Acquisition Prediction
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المستخلص: With the rise of the internet, startups number has substantially increased in the last two decades. Although many of them have succeeded to revolutionize their sectors, many of them have shut down a few months or even a few years after the foundation. In this paper, we propose a machine learning approach for predicting startups success based on historical data. Almost 840 startup data have been finely preprocessed to extract 35 features that characterize each a particular aspect of the studied startups. Afterward, computational models based on machine learning techniques were developed and tested using a cross-validation approach. The main objective is to predict the success of the startup, especially in terms of mergers and acquisitions. In particular, several models have been applied namely Artificial Neural Networks (a.k.a. ANNs), Support Vector Machines (a.k.a. SVMs), Random Forests, Bagging, Stacking, and Gradient Boosting. Overall results were very promising since the best model succeeded in the prediction with an accuracy rate of 85%. Furthermore, a feature importance study was also conducted to analyze the best predictors of a startup acquisition.
ISSN: 1658-404X

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