Gangwani Divya, Zhu Xingquan, Furht Borko
Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431 USA.
J Big Data. 2023;10(1):48. doi: 10.1186/s40537-023-00723-6. Epub 2023 Apr 16.
The success of the business directly contributes towards the growth of the nation. Hence it is important to evaluate and predict whether the business will be successful or not. In this study, we use the company's dataset which contains information from startups to Fortune 1000 companies to create a machine learning model for predicting business success. The main challenge of business success prediction is twofold: (1) Identifying variables for defining business success; (2) Feature selection and feature engineering based on Investor-Business-Market interrelation to provide a successful outcome of the predictive modeling. Many studies have been carried out using only the available features to predict business success, however, there is still a challenge to identify the most important features in different business angles and their interrelation with business success. Motivated by the above challenge, we propose a new approach by defining a new business target based on the definition of business success used in this study and develop additional features by carrying out statistical analysis on the training data which highlights the importance of investments, business, and market features in forecasting business success instead of using only the available features for modeling. Ensemble machine learning methods as well as existing supervised learning methods were applied to predict business success. The results demonstrated a significant improvement in the overall accuracy and AUC score using ensemble methods. By adding new features related to the Investor-Business-Market entity demonstrated good performance in predicting business success and proved how important it is to identify significant relationships between these features to cover different business angles when predicting business success.
企业的成功直接推动国家的发展。因此，评估和预测企业是否会成功至关重要。在本研究中，我们使用了包含从初创企业到财富1000强公司信息的公司数据集，来创建一个预测企业成功的机器学习模型。企业成功预测的主要挑战有两个方面：（1）确定定义企业成功的变量；（2）基于投资者 - 企业 - 市场的相互关系进行特征选择和特征工程，以提供预测建模的成功结果。许多研究仅使用可用特征来预测企业成功，然而，从不同业务角度识别最重要的特征及其与企业成功的相互关系仍然是一个挑战。受上述挑战的启发，我们提出了一种新方法，基于本研究中使用的企业成功定义来定义一个新的业务目标，并通过对训练数据进行统计分析来开发额外的特征，这突出了投资、业务和市场特征在预测企业成功中的重要性，而不是仅使用可用特征进行建模。集成机器学习方法以及现有的监督学习方法被用于预测企业成功。结果表明，使用集成方法在整体准确性和AUC分数方面有显著提高。通过添加与投资者 - 企业 - 市场实体相关的新特征，在预测企业成功方面表现出良好的性能，并证明了在预测企业成功时识别这些特征之间的重要关系以涵盖不同业务角度的重要性。
