Training a categorical classification example
For the first question, I would say, you don't need to convert it, but it would make the evaluation  on the test set easier.
Your classifier will output one hot encoded values, which you can convert back to string, and evaluate those values, however I think if you would have the test targets as 0-1s would help.
For the second one, you need to fit the standardscaler on the train set, and use (transform) that on the test set.
I am new to Machine Learning. I am currently solving a classification problem which has strings as its target. I have split the test and training sets and I have dealt with the string attributes by converting them by OneHotEncoder and also, I am using StandardScaler to scale the numerical features of the training set.
My question is for the test set, do I need to convert the test set targets which are still in string format such as I did with training set's string targets using the OneHotEncoder, or do I leave the test set alone as it is and the Classifier will do the job itself? Similarly for the numerical attributes do I have to use StandardScaler to scale the numerical attributes in the test set or the Classifier will do this itself once the training is done on the training set?