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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 What does the following code do?
Step1:  What does the following code do?
Step2:  What does the following code do?
Step3:  What does the following code do?
Step4:  What does the following code do?
What the heck is linspace?
What are we plotting?
Step7:  Put together
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Python Code:
d = pd.read_csv("data/dataset_0.csv")
fig, ax = plt.subplots()
ax.plot(d.x,d.y,'o')
Explanation: What does the following code do?
End of explanation
def linear(x,a,b):
    return a + b*x
Explanation: What does the following code do?
End of explanation
def linear(x,a,b):
    return a + b*x
def linear_r(param,x,y):
    return linear(x,param[0],param[1]) - y
Explanation: What does the following code do?
End of explanation
def linear_r(param,x,y):                     # copied from previous cell
    return linear(x,param[0],param[1]) - y   # copied from previous cell
param_guesses = [1,1]
fit = scipy.optimize.least_squares(linear_r,param_guesses,
                                   args=(d.x,d.y))
fit_a = fit.x[0]
fit_b = fit.x[1]
sum_of_square_residuals = fit.cost
Explanation: What does the following code do?
End of explanation
x_range = np.linspace(np.min(d.x),np.max(d.x),100)
fig, ax = plt.subplots()
ax.plot(d.x,d.y,"o")
ax.plot(x_range,linear(x_range,fit_a,fit_b))
Explanation: What does the following code do?
What the heck is linspace?
What are we plotting?
End of explanation
def linear(x,a,b):
    Linear model of x using a (slope) and b (intercept)
    return a + b*x
def linear_r(param,x,y):
    Residuals function for linear
    return linear(x,param[0],param[1]) - y
fig, ax = plt.subplots()
# Read data
d = pd.read_csv("data/dataset_0.csv")
ax.plot(d.x,d.y,'o')
# Perform regression
param_guesses = [1,1]
fit = scipy.optimize.least_squares(linear_r,param_guesses,args=(d.x,d.y))
fit_a = fit.x[0]
fit_b = fit.x[1]
sum_of_square_residuals = fit.cost
# Plot result
x_range = np.linspace(np.min(d.x),np.max(d.x),100)
ax.plot(x_range,linear(x_range,fit_a,fit_b))
fit
Explanation: Put together
End of explanation
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