A Review on Rural Womens Entrepreneurship Using Machine Learning Models

Title

A Review on Rural Womens Entrepreneurship Using Machine Learning Models

Creator

Pareek S.; Bhatnagar V.; Poonia R.C.; Sharma S.; Samanta D.

Description

Rural womens entrepreneurship has contributed significantly to the countrys economy. Entrepreneurship rates have fluctuated in recent years, according to a variety of reasons including economic, social, and cultural influences. Therefore, machine learning models are used to assess the features to make better business decisions. In this research paper, papers from 2009 to 2022 were studied and found that machine learning models are being used to improve womens entrepreneurship. In this paper, nine machine learning models have been described in detail which include multiple regression, lasso regression, logistic regression, decision tree, Naive Bayes, clustering, classification, deep learning, artificial neural network, etc. In the study of all these models, it was found how accurately this model has been used in womens entrepreneurship work. It has been observed that by using different machine learning models with the data acquired from rural entrepreneurship, women entrepreneurs may use a new way of understanding the dynamics of rural entrepreneurship. Various machine learning models have been studied to improve rural development for women working in rural areas. Thus, we have proposed a comparative study of various machine learning models to predict entrepreneurship-based data. The findings of this study may be used to assess how rural women entrepreneurs may change the decisions made in several domains, such as making use of different economic policies and promoting the long-term viability of women entrepreneurs for the countrys economic growth. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

Source

Lecture Notes in Networks and Systems, Vol-680 LNNS, pp. 375-395.

Date

2023-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Machine learning model; MSME; Rural entrepreneurship; Women

Coverage

Pareek S., Department of Computer Applications, Manipal University Jaipur, Rajasthan, Jaipur, India; Bhatnagar V., Department of Computer Applications, Manipal University Jaipur, Rajasthan, Jaipur, India; Poonia R.C., Professor, Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Uttar Pradesh, Ghaziabad, India; Sharma S., Department of Computer Applications, Manipal University Jaipur, Rajasthan, Jaipur, India; Samanta D., Department of Computing Information Technologies, RIT Kosovo (A.U.K), Rochester Institute of Technology RIT Global Campus n.n., Germia Campus, Pristina, Kosovo, 10000

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-981992601-5

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-99-2602-2_29

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175971169&doi=10.1007%2f978-981-99-2602-2_29&partnerID=40&md5=1070665800980cf1064aaf0319a406ae