A segmented approach to encouragement of entrepreneurship using data science
9202 Appleford cir, apt 248, Owings mills, MD 21117, USA.
Review
World Journal of Advanced Engineering Technology and Sciences, 2024, 12(02), 584–605.
Article DOI: 10.30574/wjaets.2024.12.2.0330
Publication history:
Received on 22 June 2024; revised on 30 July 2024; accepted on 02 August 2024
Abstract:
In the dynamic landscape of entrepreneurship, where opportunities abound and innovation thrives, Entrepreneurship: Navigating the Future with Data Science and AI" presents a groundbreaking approach to cultivating and empowering the next generation of business leaders. Authored with a comprehensive understanding of the intersection between technology and entrepreneurial endeavors, this paper offers a segmented approach that delves into the realms of data science, artificial intelligence, audience nurturing, and emerging trends.
In employed and self-employed worlds, emphasizing a paradigm shift towards discussing opportunities rather than individuals. The core premise revolves around leveraging artificial intelligence, data-driven marketing, and audience nurturing as pivotal tools for fostering entrepreneurship. The paper introduces a novel segmented model, markets, societies, and political landscapes by strategically promoting entrepreneurship.
Drawing on the computational power of data science, statistical methods, and computer science algorithms, the book advocates for the analysis of diverse and unstructured datasets to encourage risk-taking and entrepreneurial activities, particularly among students. The segmented model identifies and supports small risk-takers, utilizing specific data points sourced with consent from interested students, government schemes, private initiatives, and entrepreneurial supporting businesses.
As the narrative unfolds, readers are guided through the intricacies of implementing this segmented approach, involving industry experts, instructors, and mentors. The paper proposes few concepts on 'entrepreneurship,' any one can build a user-friendly ecosystem designed to connect entrepreneurs, investors, and trainers seamlessly. Key features include user segmentation, a resource hub, networking platforms, business counseling integration, and events and webinars calendar, among others.
The significance of artificial intelligence technologies is thoroughly explored, with a focus on resource acquisition, opportunity recognition, product development, organization creation, growth, and commercialization. Practical applications of AI in online communication, prototyping, and mentorship further underscore the transformative role of technology in the entrepreneurial journey.
The latter part of the paper introduces a proposed algorithm for connecting startups with potential investors, emphasizing the importance of factors like industry alignment, business stage, investment preferences, and expertise. The algorithm is presented in Python, providing a tangible and implementable solution for fostering successful collaborations.
A noteworthy addition to the narrative is the integration of machine learning in the matchmaking process. In this paper we will discuss the machine learning model to predict compatibility scores, showcasing a more dynamic and data-driven approach to pairing startups with investors. The step-by-step guide includes the generation of synthetic data, training the ML model, and using predictions to match startups with investors.
Entrepreneurship serves as a comprehensive guide for aspiring entrepreneurs, seasoned business leaders, and anyone intrigued by the transformative power of data science and artificial intelligence in shaping the future of entrepreneurship. It invites readers to embrace innovation, leverage technology, and navigate the complexities of the business landscape with strategic insight, ultimately contributing to the growth and success of ventures in the evolving entrepreneurial ecosystem.
Keywords:
Entrepreneurship; Artificial Intelligence; Machine Learning; Data Science; Python; Digital Marketing
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0