I think it is best that i post my whole code, so here it is:

```
import codecademylib3_seaborn
import matplotlib.pyplot as plt
def get_gradient_at_b(x, y, b, m):
N = len(x)
diff = 0
for i in range(N):
x_val = x[i]
y_val = y[i]
diff += (y_val - ((m * x_val) + b))
b_gradient = -(2/N) * diff
return b_gradient
def get_gradient_at_m(x, y, b, m):
N = len(x)
diff = 0
for i in range(N):
x_val = x[i]
y_val = y[i]
diff += x_val * (y_val - ((m * x_val) + b))
m_gradient = -(2/N) * diff
return m_gradient
#Your step_gradient function here
def step_gradient(b_current, m_current, x, y, learning_rate):
b_gradient = get_gradient_at_b(x, y, b_current, m_current)
m_gradient = get_gradient_at_m(x, y, b_current, m_current)
b = b_current - (learning_rate * b_gradient)
m = m_current - (learning_rate * m_gradient)
return b, m
#Your gradient_descent function here:
def gradient_descent(x, y, learning_rate, num_iterations):
b = 0
m = 0
for i in range(num_iterations): # loop that runs num_iter times
b, m = step_gradient(b, m, x, y, learning_rate)
return b, m
months = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
revenue = [52, 74, 79, 95, 115, 110, 129, 126, 147, 146, 156, 184]
#Uncomment the line below to run your gradient_descent function
b, m = gradient_descent(months, revenue, 0.01, 1000)
#Uncomment the lines below to see the line you've settled upon!
y = [m*x + b for x in months]
plt.plot(months, revenue, "o")
plt.plot(months, y)
plt.show()
```