After normalising my data using DataPreparer while using random forest and SVM, why do my data values become negative?

Question

I am working on predictive modeling where I need to predict whether an online customer ends up purchasing a product on a website or not, and I am using Random Forest Classifier and SVM since it's a classification problem.

After creating the fitting splits for training, testing, and validation sets, I dummify, standardize and normalize my data. However, after I normalize the sets, their values become all negative. Is there a way to change that and why does it happen?

The code that I am using to normalize my fitting sets is as below:

data_preparer = DataPreparer(one_hot_encoder, standard_scaler)
data_preparer.prepare_data(fitting_splits.train_set).head()
data_preparer.prepare_data(fitting_splits.validation_set).head()

Answer 1

I think the documentation from sklearn.preprocessing.StandardScaler can help here:

The standard score of a sample x is calculated as:

z = (x - u) / s

where u is the mean of the training samples or zero if with_mean=False, and s is the standard deviation of the training samples or one if with_std=False.

Based on this equation, if x (the individual value currently being scaled) is less than the mean of the variable, then your scaled value will be negative.