ML Pipeline Reveals Financial Barriers and Mentorship Gaps Holding Nepal Graduates Back

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A data science project surveyed SEE and +2 graduates in Nepal to identify what is preventing them from feeling career-ready, using survey design, data cleaning, a custom scoring model, and machine learning. The dataset, collected via Instagram and skewed toward urban Bagmati province respondents, yielded 167 usable responses after cleaning. Financial problems were the most commonly cited barrier, affecting 54% of respondents, followed by lack of mentorship and limited networking opportunities. The average career-readiness score came in at 50.5 out of 100, placing most students in a mid-range 'engaged but unprepared' band. Feature importance analysis found that mentorship and networking gaps were stronger predictors of low readiness than limited access to skills or course content.

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Codename One Adds Cross-Platform Health API With Privacy-Aware Design

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ProgrammingDEV Community ·

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ProgrammingDEV Community ·

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ProgrammingDEV Community ·

Eight Webhook Providers, Four Hidden Ways They Each Sign Requests Differently

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