First, don't be too hard on yourself. Scholarship selections are often based on many factors beyond a single project, and without seeing the reviewers' feedback, it's impossible to know exactly why you weren't selected.
From what you've described, you've already covered many of the core technical steps of a Data Science project: data cleaning, preprocessing, exploratory analysis, feature engineering, model training, and evaluation. What often distinguishes stronger projects is demonstrating business impact, clearly explaining why certain features and models were chosen, comparing multiple algorithms, addressing class imbalance, performing robust validation, and presenting actionable insights rather than just model metrics.
To gain more practical experience, I'd recommend building additional end-to-end projects, participating in Kaggle competitions, creating dashboards to communicate results, and sharing your work on GitHub with detailed documentation. Focus on solving real-world problems and explaining your thought process, not just the code. Employers and scholarship reviewers often value problem-solving, storytelling, and reproducibility as much as technical implementation.
Keep learning and building, one scholarship outcome doesn't define your potential in Data Science or Machine Learning. Every project you complete strengthens your skills and portfolio, and consistency over time is what ultimately makes the biggest difference. 🚀
