Title: Application of machine learning algorithms to predict survival of micro small and medium enterprises in India

Authors: R. Sujatha; B. Uma Maheswari; A. Mansurali

Addresses: PSG Institute of Management, Peelamedu, Coimbatore – 641 004, Tamil Nadu, India ' PSG Institute of Management, Peelamedu, Coimbatore – 641 004, Tamil Nadu, India ' PSG Institute of Management, Peelamedu, Coimbatore – 641 004, Tamil Nadu, India

Abstract: Micro, small and medium enterprises (MSMEs) play a crucial role in the economic development of any country. Therefore, survival of these MSMEs becomes very imperative. The objective of this study is to build machine learning models to predict the survival of MSMEs and identify the factors that influence the survival. The data for the study was extracted from Fourth All India Census of MSMEs conducted by Ministry of MSMEs, Government of India. Three machine learning algorithms such as logistic regression, decision tree and random forest are used to build models. Random forest algorithm provided the highest accuracy. Also the study identified outstanding loan, market value, purchase value, owner's social category, nature of activity, bank account, cluster type, power source, quality, and organisation type as the variables that significantly influence the firm survival. MSMEs can monitor those factors and frame appropriate policies that would help MSMEs to survive and sustain.

Keywords: micro; small and medium enterprises; MSMEs; survival; machine learning; logistic regression; decision tree; random forest; India.

DOI: 10.1504/IJDATS.2022.129176

International Journal of Data Analysis Techniques and Strategies, 2022 Vol.14 No.4, pp.317 - 335

Received: 30 Jun 2021
Received in revised form: 30 Apr 2022
Accepted: 22 Oct 2022
Published online: 27 Feb 2023 *

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