How Machine Learning Can Unveil Challenges Faced by Women Entrepreneurs in Pakistan

Abstract

Machine Learning has emerged as a prominent force in entrepreneurship, particularly amidst the evolving economic landscape of Pakistan and the global challenges posed by inflation. This qualitative study, employing thematic analysis complemented by sentiment analysis, focuses on insights derived from women entrepreneurs for whom entrepreneurship serves as the primary source of family income. These women exhibit a tech-savvy approach, utilising algorithms to optimise their Key Performance Indicators (KPIs). The research explores key questions regarding the informational needs of women entrepreneurs, central to the study's objectives. Participants uniformly advocate for increased governmental support to foster entrepreneurial growth, alongside a collective call for women to assert their rights within the entrepreneurial domain. Additionally, proactive training initiatives are emphasised, with a shift towards internal initiatives driving change rather than relying solely on external interventions. Incremental progress is highlighted as crucial, given the societal barriers impeding long-term aspirations. Recommendations stemming from the study advocate for multifaceted interventions to cultivate an entrepreneurial ecosystem conducive to women's empowerment. These include the establishment of government-funded youth training centres, comprehensive educational seminars to foster entrepreneurship awareness, flexible educational schedules accommodating extracurricular activities, and provision of supplementary certifications aligned with entrepreneurial lifestyles by educational institutions. These recommendations aim to foster systemic changes empowering women entrepreneurs, facilitating their transition into influential stakeholders within Pakistan's entrepreneurial landscape. Furthermore, sentiment analysis informs predictive modelling for Machine Learning, enriching insights into the entrepreneurial experiences of women in Pakistan.