7. Euclidean Distance

Given two vectors $\mathbf{a}$ and $\mathbf{b}$ in $\mathbb{R}^n$, compute the Euclidean distance between them.

$d(\mathbf{a}, \mathbf{b}) = \sqrt{\sum_{i=1}^{n} (a_i - b_i)^2} = \|\mathbf{a} - \mathbf{b}\|_2$

This is the $L_2$ norm of the difference vector and is the most common distance metric in ML — used in $k$-NN, $k$-means, and similarity search.

Example

a = [0, 0],  b = [3, 4]
Output: 5.0   # classic 3-4-5 right triangle

Constraints

• Both inputs will always have the same length.
• Return a Python float.