new_contents: import numpy as np
  
  
  def getPearsonCorrelationCoefficient(x, y):
      length = np.min((len(x), len(y)))
      x = x[:length]
      y = y[:length]
      _x = np.mean(x)
      _y = np.mean(y)
      up = 0
      downX = 0
      downY = 0
      for i in range(length):
          tempX = x[i] - _x
          tempY = y[i] - _y
          up += tempX * tempY
          downX += tempX ** 2
          downY += tempY ** 2
      return up / np.sqrt(downX * downY)
  
  
  def polyfit(x, y, degree):
      results = {}
      coffes = np.polyfit(x, y, degree)
      results["polynomial"] = coffes.tolist()
      p = np.poly1d(coffes)
      yhat = p(x)
      ybar = np.sum(y) / len(y)
      ssreg = np.sum((yhat - ybar) ** 2)
      sstot = np.sum((y - ybar) ** 2)
      results["determination"] = ssreg / sstot
      return results
  
subject: add MLLib