%matplotlib inline #imports from numpy import * import matplotlib.pyplot as plt

points = genfromtxt('data.csv', delimiter=',') #Extract columns x = array(points[:,0]) y = array(points[:,1]) #Plot the dataset plt.scatter(x,y) plt.xlabel('Hours of study') plt.ylabel('Test scores') plt.title('Dataset') plt.show()

#hyperparameters learning_rate = 0.0001 initial_b = 0 initial_m = 0 num_iterations = 10

def compute_cost(b, m, points): total_cost = 0 N = float(len(points)) #Compute sum of squared errors for i in range(0, len(points)): x = points[i, 0] y = points[i, 1] total_cost += (y - (m * x + b)) ** 2 #Return average of squared error return total_cost/N

def gradient_descent_runner(points, starting_b, starting_m, learning_rate, num_iterations): b = starting_b m = starting_m cost_graph = [] #For every iteration, optimize b, m and compute its cost for i in range(num_iterations): cost_graph.append(compute_cost(b, m, points)) b, m = step_gradient(b, m, array(points), learning_rate) return [b, m, cost_graph] def step_gradient(b_current, m_current, points, learning_rate): m_gradient = 0 b_gradient = 0 N = float(len(points)) #Calculate Gradient for i in range(0, len(points)): x = points[i, 0] y = points[i, 1] m_gradient += - (2/N) * x * (y - (m_current * x + b_current)) b_gradient += - (2/N) * (y - (m_current * x + b_current)) #Update current m and b m_updated = m_current - learning_rate * m_gradient b_updated = b_current - learning_rate * b_gradient #Return updated parameters return b_updated, m_updated

b, m, cost_graph = gradient_descent_runner(points, initial_b, initial_m, learning_rate, num_iterations) #Print optimized parameters print ('Optimized b:', b) print ('Optimized m:', m) #Print error with optimized parameters print ('Minimized cost:', compute_cost(b, m, points))

plt.plot(cost_graph) plt.xlabel('No. of iterations') plt.ylabel('Cost') plt.title('Cost per iteration') plt.show()

#Plot dataset plt.scatter(x, y) #Predict y values pred = m * x + b #Plot predictions as line of best fit plt.plot(x, pred, c='r') plt.xlabel('Hours of study') plt.ylabel('Test scores') plt.title('Line of best fit') plt.show()