If the minimum value of both $c1$ and $c2$ is zero, then this is known as "min-max scaling": $$x' = \frac{x - x_{min}}{x_{max} - x_{min}}$$

This normalizes the vraible range to $[0,1]$. Note that, depending on the variable range, a linear transformation to the range $[0,1]$ might not be appropriate (another occasionally used scaling function is the exponential function).

Another normalization methods are "z score standardization", which normalizes to zero mean and variance one: $$x' = \frac{x-\mu}{\sigma}$$

If the minimum value of both $c1$ and $c2$ is zero, then this is known as "min-max scaling": $$x' = \frac{x - x_{min}}{x_{max} - x_{min}}$$

This normalizes the variable range to $[0,1]$. Note that, depending on the variable range, a linear transformation to the range $[0,1]$ might not be appropriate (another occasionally used scaling function is the exponential function).

Another normalization method is "z score standardization", which normalizes to zero mean and variance one (and thus SD one too): $$x' = \frac{x-\mu}{\sigma}$$