class_docstr: Base regression model. Models the relationship between a scalar dependent variable y and the independent 
  variables X. 
  Parameters:
  -----------
  n_iterations: float
      The number of training iterations the algorithm will tune the weights for.
  learning_rate: float
      The step length that will be used when updating the weights.
class_docstr_tok: ["Base","regression","model",".","Models","the","relationship","between","a","scalar","dependent","variable","y","and","the","independent","variables","X.","Parameters",":","--","--","--","--","--","-","n_iterations",":","float","The","number","of","training","iterations","the","algorithm","will","tune","the","weights","for",".","learning_rate",":","float","The","step","length","that","will","be","used","when","updating","the","weights","."]
code: def predict(self, X):
      # Insert constant ones for bias weights
      X = np.insert(X, 0, 1, axis=1)
      y_pred = X.dot(self.w)
      return y_pred
  
imports: from __future__ import print_function, division
  import numpy
  import math
  from mlfromscratch.utils import normalize, polynomial_features
label_desc: Use image node_id 4 for calling the Regression obj's underlying member method code with example usage: obj.predict(X) and returns: y_pred