How important is clean data for training ML models?

[theh!ddenlayer]

I've spent years diving into business analytics, and let me tell you, datasets can be pretty chaotic, sometimes they're messy, sometimes they're incomplete, and often they're just plain inconsistent. A coworker once mentioned that when it comes to machine learning, having pinpoint accuracy isn't the be-all and end-all, it's more about having a sufficient amount of data to identify broader trends.

That really caught me off guard because I've always believed that having clean and accurate data is crucial. So, how accurate is that perspective? Can a model still perform well even with the noisy or imperfect data we often see in the real world?

 

[flappybird]

That makes sense, while clean data is great, many machine learning models can still work well with messy or imperfect datasets as long as you've got enough of it. Bigger datasets help the model pick up patterns even if some points are noisy, though really bad or missing data can still cause issues. Its usually about finding a balance between quality and quantity