4 Common Machine Learning Mistakes (and How to Stop Making Them)

Machine Learning isn’t magic. It’s not “plug and play.” And if you think it’s about throwing data at an algorithm and hoping for the best, I’ve got bad news for you: that’s not how it works.

Building effective models means avoiding rookie mistakes. Unfortunately, these errors happen more often than memes on Twitter. But fear not — I’m here to break them down and show you how to stop sabotaging your own work.

Ready? Let’s dive in.

1️⃣ Skipping Data Preprocessing

Think of raw data like a messy room. Sure, you can live in it, but good luck finding anything or making sense of what’s there. That’s what happens when you skip preprocessing: garbage data goes in, garbage predictions come out.

What goes wrong:

• Ignoring missing values, as if they’ll magically fix themselves.
• Failing to normalize features. Mixing pounds with kilometers? Good luck.
• Letting categorical variables sit there, untouched, like a forgotten sock.

How to fix it:

• Clean your data. Yes, it’s tedious, but it’s critical.
• Handle missing values by filling them in with mean, median, or…