Ajai Mishra
ABSTRACT
Machine learning techniques are used for discovering the hidden patterns from the application-centric data analysis. Using these techniques various applications can be developed which can be used for supporting different business sectors. These patterns will be used by business administrators and managers to develop sustainable, growing, and withstand the global business challenges. In this context, the employment of machine learning techniques in business data analysis can become a fruitful tool that can assist and help to new start-ups businesses and entrepreneurs, to sustain and grow with time. Therefore, in this paper, we are conducting a review on existing machine learning techniques that are recently contributed to understand the need of start-ups, trends of business and can provide recommendations to plan their future strategies to deal with the business problems. Secondly, based on the observations we have proposed our future road map to design and develop an intellectual framework to support Start-up India-based entrepreneurs
- 2021 Startup India Hub, https://www.startupindia.gov.in/Google Scholar
- The Federation of Indian Chambers of Commerce & Industry, https://ficci.in/Google Scholar
- Pujari, Arun K., "Data Mining Techniques", Universities press, 2001.Google Scholar
- K. Żbikowski, P. Antosiuk, "A machine learning, bias-free approach for predicting business success using Crunch base data", Information Processing and Management 58 (2021) 102555Google Scholar
- B. Sharchilev, M. Roizner, A. Rumyantsev, D. Ozornin, P. Serdyukov, M. de Rijke, "Web-based Startup Success Prediction", CIKM 2018, October 22--26. Lingotto, Turin, Italy © Copyright held by the owner/author(s). ACM ISBN 123--4567.24-567/08/06Google Scholar
- A. Krishna, A. Agrawal, A. Choudhary, "Predicting the Outcome of Startups: Less Failure, More Success", IEEE 16th International Conference on Data Mining Workshops, 2375--9259.16© 2016 IEEEGoogle Scholar
- H. Janáková, "The Success Prediction of the Technological Start-up Projects in Slovak Conditions", Procedia Economics and Finance 34 (2015) 73--80.Google ScholarCross Ref
- G. Ross, S. Das, D. Sciro, H. Raza, "CapitalVX: A machine learning model for startup selection and exit prediction", The Journal of Finance and Data Science 7 (2021) 94--114.Google ScholarCross Ref
- U. Kaiser, J. M. Kuhn, "The value of publicly available, textual and non-textual information for startup performance prediction", Journal of Business Venturing Insights 14 (2020) e00179Google ScholarCross Ref
- Q. Zhang, T. Ye, M. Essaidi, S. Agarwal, V. Liu, B. T. Loo, "Predicting Startup Crowd-funding Success through Longitudinal Social Engagement Analysis", CIKM'17, November 6--10. 2017, Singapore, ACM ISBN 978--1.4503-4918-5/17/11Google Scholar
- A. Prohorovs, J. Bistrova, D. Ten, "Startup Success Factors in the Capital Attraction Stage: Founders' Perspective", Journal of East-West Business, DOI: 10.1080/10669868.2018.1503211Google ScholarCross Ref
- F. Corea, G. Bertinetti, E. M. Cervellati, "Hacking the venture industry: An Early-stage Startups Investment framework for data-driven investors", Machine Learning with Applications 5 (2021) 100062Google Scholar
- J. Arroyo, F. Corea, G. J. Diaz, J. A. Recio-Garica, "Assessment of Machine Learning Performance for Decision Support in Venture Capital Investments", Volume 7, 2019, IEEEGoogle Scholar
- T. Antretter, I. Blohm, D. Grichnik, J. Wincent, "Predicting new venture survival: A Twitter-based machine learning approach to measuring online legitimacy", Journal of Business Venturing Insights 11 (2019) e00109Google ScholarCross Ref
- D. McKenzie, D. Sansone, "Predicting entrepreneurial success is hard: Evidence from a business plan competition in Nigeria", Journal of Development Economics 141 (2019) 102369Google ScholarCross Ref
- M. Guerzoni, C. R. Nava, M. Nuccio, "Start-ups survival through a crisis Combining machine learning with econometrics to measure innovation", Economics of Innovation and New Technology, DOI: 10.1080/10438599.2020.1769810.Google ScholarCross Ref
- S. P. Jun, H. S. Yoo, J. Hwang, "A hybrid recommendation model for successful R&D collaboration: Mixing machine learning and discriminant analysis", Technological Forecasting & Social Change 170 (2021) 120871Google Scholar
- J. C. Kaminski, C. Hopp, "Predicting outcomes in crowdfunding campaigns with textual, visual, and linguistic signals", Small Bus Econ (2020) 55:627--649Google Scholar
- J. R. Saura, P. P. Sanchez, A. Grilo, "Detecting Indicators for Startup Business Success: Sentiment Analysis Using Text Data Mining", Sustainability 2019, 11, 917; doi:10.3390/su11030917Google ScholarCross Ref
- T. Bilen, M. E. Ozcevik, Y. Yaslan, S. F. Oktug, "A Smart City Application: Business Location Estimator using Machine Learning Techniques", 978--1.5386-6614-2/18/$31.00 ©2018 IEEE, DOI 10.1109/HPCC/SmartCity/DSS.2018.00219Google Scholar
- M. Cantamessa, V. Gatteschi, G. Perboli, M. Rosano, "Startups' Roads to Failure", Sustainability 2018, 10, 2346; doi:10.3390/su10072346Google ScholarCross Ref
- I. Afolabi, T. C. Ifunaya, F. G. Ojo, C. Moses, "A Model for Business Success Prediction using Machine Learning Algorithms", IOP Conf. Series: Journal of Physics: Conf. Series 1299 (2019) 012050 doi:10.1088/1742-6596/1299/1/012050Google ScholarCross Ref
- M. Bauer, C. van Dinther, D. Kiefer, "Machine Learning in SME: An Empirical Study on Enablers and Success Factors", AMCIS 2020 Proceedings. 3 [24] G. Perboli, E. Arabnezhad, "A Machine Learning-based DSS for mid and long-term company crisis prediction", Expert Systems With Applications 174 (2021) 114758Google Scholar
Index Terms
- Machine Intelligence for Predicting New Start-ups Success: A Survey
Recommendations
Machine Learning: The State of the Art
The two fundamental problems in machine learning (ML) are statistical analysis and algorithm design. The former tells us the principles of the mathematical models that we establish from the observation data. The latter defines the conditions on which ...
Business intelligence success: The roles of BI capabilities and decision environments
This study examines the role of the decision environment in how well business intelligence (BI) capabilities are leveraged to achieve BI success. We examine the decision environment in terms of the types of decisions made and the information processing ...
Comments