These are the common mistakes you like to keep an eye and avoid.
❌ Mistake 1: Overfitting
Model memorizes instead of learning
👉 Example:
- Works perfectly on training data
- Fails on new data
❌ Mistake 2: Bad Data
Garbage in → garbage out
👉 Example:
- Missing values
- Incorrect labels
❌ Mistake 3: Wrong Model Choice
Using classification for numeric prediction, etc.
🧠 Key Insight:
Good data + simple model > Complex model + bad data