Hi everyone 👋

I’m a data scientist currently working on building data-driven solutions and community-focused projects, and I’ve been reflecting on one of the biggest challenges we face in practice — data quality and bias in real-world datasets.

While many tutorials use clean and structured data, real production environments often involve missing values, inconsistent records, and hidden bias that can affect model fairness and performance.

I’m curious to learn from this community:

What strategies or frameworks do you use to identify and reduce bias during data preparation?

How do you balance model performance with ethical and responsible AI practices?

Are there any tools, workflows, or lessons learned from projects that significantly improved your outcomes?

I’d really value hearing practical experiences, challenges, or even mistakes you’ve learned from — especially in production or community impact projects.

Looking forward to learning from everyone 🙌