Naive Bayes Classifier Could someone please explain to me how and why can we go from equation $4.3$ to equation $4.4$:

$$\hat{c}= \arg\max_{c \in \mathcal{C}}P(c|d) = \arg\max_{c \in \mathcal{C}}\frac{P(d|c)P(c)}{P(d)}\tag{4.3}$$

$$\hat{c}= \arg\max_{c \in \mathcal{C}}P(c|d) = \arg\max_{c \in \mathcal{C}}P(d|c)P(c)\tag{4.4}$$
We are trying to select the optimal $c$, here $d$ is fixed and hence $P(d)$ and $\frac1{P(d)}$ is just a positive constant.

Multiplying an objective function with a positive constant doesn't change the optimal solution, hence we can drop $P(d)$.