I really need a tutorial about a Regression problem in R with the following points:

- there are numeric and categorical variables
- predicting a numeric variable
- show how to do the normalization of the data
- training a neural network
- analyze the results with the most popular metrics (MAPE, MSE, MAE, etc)
- show how to predict the target variable given a new observation

Note: My main concern here is that the new observation needs to be normalized in the same way as the training data so the predicted value makes sense.

I know there are many tutorials about similar things in R but I didn't find any yet that combines the 6 points above.

I really need this. I'm like in an emergency.

Thank you in advance!