Enhancing Business Success Prediction: A Data-Driven Machine Learning Mode
Business failure yields considerable economic and social repercussions, affecting employees, investors, and communities. Conventendeavorslure prediction models predominantly depend on financial measurements, restricting their relevance across many businesses and overlooking essential non-financial e...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | EPJ Web of Conferences |
| Online Access: | https://www.epj-conferences.org/articles/epjconf/pdf/2025/13/epjconf_icetsf2025_01022.pdf |
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| author | Deo Arpit Korde Manish Tiwari Anant Jain Anant Choudhary Akash |
| author_facet | Deo Arpit Korde Manish Tiwari Anant Jain Anant Choudhary Akash |
| author_sort | Deo Arpit |
| collection | DOAJ |
| description | Business failure yields considerable economic and social repercussions, affecting employees, investors, and communities. Conventendeavorslure prediction models predominantly depend on financial measurements, restricting their relevance across many businesses and overlooking essential non-financial elements. This study presents a machine learning model for predicting company failure, utilizing logistic regression, random forest, and neural networks. The model incorporates both financial and non-financial characteristics, solving research deficiencies concerning cooperative societies, governance, market rivalry, and external economic factors. Data preprocessing methods, including outlier detection, feature selection, and dimensionality reduction, improve model accuracy. The suggested methodology attains an accuracy over 94%, offering an early warning system for enterprises at risk of collapse. This study enhances financial risk evaluation by providing a flexible, sector-specific forecasting model. The methodology facilitates proactive decision-making, assisting organizations in risk mitigation, sustainability enhancement, and financial crisis prevention. Future endeavors involve augmenting datasets and investigating deep learning methodologies to improve predictive accuracy. |
| format | Article |
| id | doaj-art-a305476a0f8a47ae9f647874b22f4e45 |
| institution | OA Journals |
| issn | 2100-014X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | EPJ Web of Conferences |
| spelling | doaj-art-a305476a0f8a47ae9f647874b22f4e452025-08-20T02:36:31ZengEDP SciencesEPJ Web of Conferences2100-014X2025-01-013280102210.1051/epjconf/202532801022epjconf_icetsf2025_01022Enhancing Business Success Prediction: A Data-Driven Machine Learning ModeDeo Arpit0Korde Manish1Tiwari Anant2Jain Anant3Choudhary Akash4Department of Computer Science and Engineering, Medicaps UniversityDepartment of Computer Science and Engineering, Medicaps UniversityDepartment of Computer Science and Engineering, Medicaps UniversityDepartment of Computer Science and Engineering, Medicaps UniversityDepartment of Computer Science and Engineering, Medicaps UniversityBusiness failure yields considerable economic and social repercussions, affecting employees, investors, and communities. Conventendeavorslure prediction models predominantly depend on financial measurements, restricting their relevance across many businesses and overlooking essential non-financial elements. This study presents a machine learning model for predicting company failure, utilizing logistic regression, random forest, and neural networks. The model incorporates both financial and non-financial characteristics, solving research deficiencies concerning cooperative societies, governance, market rivalry, and external economic factors. Data preprocessing methods, including outlier detection, feature selection, and dimensionality reduction, improve model accuracy. The suggested methodology attains an accuracy over 94%, offering an early warning system for enterprises at risk of collapse. This study enhances financial risk evaluation by providing a flexible, sector-specific forecasting model. The methodology facilitates proactive decision-making, assisting organizations in risk mitigation, sustainability enhancement, and financial crisis prevention. Future endeavors involve augmenting datasets and investigating deep learning methodologies to improve predictive accuracy.https://www.epj-conferences.org/articles/epjconf/pdf/2025/13/epjconf_icetsf2025_01022.pdf |
| spellingShingle | Deo Arpit Korde Manish Tiwari Anant Jain Anant Choudhary Akash Enhancing Business Success Prediction: A Data-Driven Machine Learning Mode EPJ Web of Conferences |
| title | Enhancing Business Success Prediction: A Data-Driven Machine Learning Mode |
| title_full | Enhancing Business Success Prediction: A Data-Driven Machine Learning Mode |
| title_fullStr | Enhancing Business Success Prediction: A Data-Driven Machine Learning Mode |
| title_full_unstemmed | Enhancing Business Success Prediction: A Data-Driven Machine Learning Mode |
| title_short | Enhancing Business Success Prediction: A Data-Driven Machine Learning Mode |
| title_sort | enhancing business success prediction a data driven machine learning mode |
| url | https://www.epj-conferences.org/articles/epjconf/pdf/2025/13/epjconf_icetsf2025_01022.pdf |
| work_keys_str_mv | AT deoarpit enhancingbusinesssuccesspredictionadatadrivenmachinelearningmode AT kordemanish enhancingbusinesssuccesspredictionadatadrivenmachinelearningmode AT tiwarianant enhancingbusinesssuccesspredictionadatadrivenmachinelearningmode AT jainanant enhancingbusinesssuccesspredictionadatadrivenmachinelearningmode AT choudharyakash enhancingbusinesssuccesspredictionadatadrivenmachinelearningmode |