Exploring investor-business-market interplay for business success prediction
Abstract 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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-04-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-023-00723-6 |
| _version_ | 1797845889888288768 |
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| author | Divya Gangwani Xingquan Zhu Borko Furht |
| author_facet | Divya Gangwani Xingquan Zhu Borko Furht |
| author_sort | Divya Gangwani |
| collection | DOAJ |
| description | Abstract 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. Graphical Abstract |
| first_indexed | 2024-04-09T17:47:19Z |
| format | Article |
| id | doaj.art-96bb77dd93ad41f3a69c78228e66c451 |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-04-09T17:47:19Z |
| publishDate | 2023-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-96bb77dd93ad41f3a69c78228e66c4512023-04-16T11:16:49ZengSpringerOpenJournal of Big Data2196-11152023-04-0110112810.1186/s40537-023-00723-6Exploring investor-business-market interplay for business success predictionDivya Gangwani0Xingquan Zhu1Borko Furht2Department of Electrical Engineering and Computer Science, Florida Atlantic UniversityDepartment of Electrical Engineering and Computer Science, Florida Atlantic UniversityDepartment of Electrical Engineering and Computer Science, Florida Atlantic UniversityAbstract 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. Graphical Abstracthttps://doi.org/10.1186/s40537-023-00723-6Machine learning methodsInvestments-business-marketFeature engineeringSuccess prediction |
| spellingShingle | Divya Gangwani Xingquan Zhu Borko Furht Exploring investor-business-market interplay for business success prediction Journal of Big Data Machine learning methods Investments-business-market Feature engineering Success prediction |
| title | Exploring investor-business-market interplay for business success prediction |
| title_full | Exploring investor-business-market interplay for business success prediction |
| title_fullStr | Exploring investor-business-market interplay for business success prediction |
| title_full_unstemmed | Exploring investor-business-market interplay for business success prediction |
| title_short | Exploring investor-business-market interplay for business success prediction |
| title_sort | exploring investor business market interplay for business success prediction |
| topic | Machine learning methods Investments-business-market Feature engineering Success prediction |
| url | https://doi.org/10.1186/s40537-023-00723-6 |
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