Optimum values of all predictors in logistic regression I am trying to figure out in logistic regression, with the help of coefficients of $x$ variables, we could figure out that on unit change in any $x$ variable what will be the change in probability of y variable.

But I want to understand what must be the values of different $x$ variables to be set so that we are most likely to get only desired results.

For example if we are trying to predict defaulters in loan dataset, now by running logistic regression and its coefficients, we would be able to get the idea that by unit change in $x_1$(or $x_2$ or $x_3$) what will be the change in probability of $y$ to be $1$.

Now if I want to understand that what must be the values of $x_1,x_2,x_3, \ldots$ etc to be set so that the chances of $y$ to be $1$ will be maximum?
Theoretically you won't be able to get the value $1$. However, suppose your variable is bounded and you can indepedently set the values of the variables, you can let it be as large as possible.

Look at the coefficients that you obtained. For those coefficients that you obtained, if they are positive, set the value to be as large as possible. For negative coefficients, set the variable to be as small as possible. If the coefficients is zero, any value is fine.