text: import numpy as np
  import pandas as pd
  from gradient_descent import gradient_descent
  from hypothises import hypothises
  
  # MY datas for the model
  data = pd.read_csv('kc_house_data.csv')
  newData = data.head(1000)
  y = newData.price
  x = newData.sqft_living
  
  y = np.array(y).reshape(len(y), 1)
  
  x = np.transpose([np.ones(len(y)), x])
  theta = np.array([[0.3], [0.5]])
  params = gradient_descent(x, y, theta, 0.00000001, 3500)
  
  x2 = [2500, 2950]
  x2 = np.transpose(np.array(x2))
  x2 = np.transpose([np.ones(len(x2)), x2])
  y2 = hypothises(x2, params)
  
  for i in range(len(x2)):
      print(f"Size {x2[i]} : Prize {y2[i]} ")