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A Review on Rural Women’s Entrepreneurship Using Machine Learning Models

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Innovations in Computational Intelligence and Computer Vision (ICICV 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 680))

Abstract

Rural women’s entrepreneurship has contributed significantly to the country’s 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 women’s 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 women’s 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 country’s economic growth.

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Author information

Authors and Affiliations

  1. Department of Computer Applications, Manipal University Jaipur, Jaipur, Rajasthan, India

    Shivani Pareek, Vaibhav Bhatnagar & Shilpa Sharma

  2. Professor, Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, Uttar Pradesh, India

    Ramesh Chandra Poonia

  3. Department of Computing Information Technologies, RIT Kosovo (A.U.K), Rochester Institute of Technology – RIT Global Campus n.n., Germia Campus, Pristina, 10000, Kosovo

    Debabrata Samanta

Authors
  1. Shivani Pareek
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  2. Vaibhav Bhatnagar
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  3. Ramesh Chandra Poonia
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  4. Shilpa Sharma
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  5. Debabrata Samanta
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Corresponding author

Correspondence to Vaibhav Bhatnagar .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India

    Satyabrata Roy

  2. Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India

    Deepak Sinwar

  3. Techno International New Town, Kolkata, India

    Nilanjan Dey

  4. Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Seri Kembangan, Malaysia

    Thinagaran Perumal

  5. Faculdade de Engenharia da, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Pareek, S., Bhatnagar, V., Poonia, R.C., Sharma, S., Samanta, D. (2023). A Review on Rural Women’s Entrepreneurship Using Machine Learning Models. In: Roy, S., Sinwar, D., Dey, N., Perumal, T., Tavares, J.M.R.S. (eds) Innovations in Computational Intelligence and Computer Vision. ICICV 2022. Lecture Notes in Networks and Systems, vol 680. Springer, Singapore. https://doi.org/10.1007/978-981-99-2602-2_29

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