Little Bits of Artificial Intelligence

Data Type: Use if your features are numeric or can be converted to numbers.

Feature Count: Best with a small to moderate number of input features.

Dataset Size: Works well with small to mid-sized datasets (not too large).

Local Patterns: Ideal if similar inputs have similar outputs (non-linear relationships).

Distance Matters: Ensure distance makes sense — normalize features if needed.

No Missing Data: Clean or fill missing values before use.

Interpretability: Don’t use if you need feature importance or a formula.

Tuning K: Choose the best K using validation or cross-validation.

Prediction Time: Avoid if you need fast predictions on huge datasets.

Baseline Use: Good for benchmarking more complex models later.