Machine Learning, Data Science, Deep Learning, Artificial Intelligence, Regression.
1 / (1 + np.exp(-Beta_1*(x-Beta_2))) return y beta_1 = 0.10 beta_2 = 1990.0 #logistic function Y_pred = sigmoid(x_data, beta_1 , beta_2) #plot initial prediction against datapoints plt.plot(x_data, Y_pred*15000000000000.) plt.plot(x_data, y_data, 'ro' Our task here is to find the best parameters