Predicting new values after feature scaling

Question

I am trying to do linear regression with one feature only: predicting height with weights. Gradient descent took too many epochs so I used a min max scaler and it converged to the optimum point pretty quickly.

However, predictions are now too high. What do I need to do to get correct predictions? Here's my code:

def min_max_scaler(arr):
    x = arr.copy()
    minimum = np.min(x,axis=0)
    maximum = np.max(x,axis=0)
    x = (x - minimum) / (maximum - minimum)
    return x
class LinearRegression:
  def __init__(self,theta):
    self.theta = theta
def predict(self,X):
    return X @ self.theta
def compute_cost(self,X,y):
    yhat = self.predict(X)
    m = len(y)
    return (1/m) * np.sum((yhat-y)**2)
def train(self,X,y,alpha,epochs):
    m,n = X.shape
    cost_history = np.zeros(epochs)
    for i in range(0,epochs):
      nabla = np.ones(n)
      for j in range(0,n):
        nabla[j] = (2/m) * np.sum((self.predict(X) - y)@X[:,j])
      self.theta -= alpha * nabla
      cost_history[i]  = self.compute_cost(X,y)
    return cost_history