text: Title: add feature scaling transformers 
  Body: Whenever I want to scale some numerical features I'm always importing transformers from sklearn. preprocessing.
  We know that the sklearn transformers don't take our required variable names to scale (like feature engine) and they don't return a data frame. It becomes somewhat frustrating when using a Pipeline to preprocess the data as the next transformer may need a data frame to transform the variables.
  
  It would be very useful to users in the data preprocessing Pipeline if we can add some feature scaling transformers like MinMaxScaler, StandardScaler, RobustScaler, etc...
  
  -> As a simple alternative solution, we can create a custom transformer as given below
  
  ```
  from sklearn.preprocessing import StandardScaler
  from sklearn.base import BaseEstimator 
  class CustomScaler(BaseEstimator):
    def __init__(self, variables):
      self.variables = variables
      self.scaler = StandardScaler()
  
    def fit(self, X, y=None):
       self.scaler.fit(X[self.variables])
       return self
  
    def transform(self, X):
       X[self.columns] = self.scaler.transform(X[self.columns])
       return X
  ```
  **Additional context**
  Feature scaling is also an important feature engineering step for linear models. 
  We can easily handle scalable variables in a preprocessing pipeline. 
  