Data Science How do you find line of best fit for a 3d model in Python? I have a dataframe with columns x, y, and z. The values create a polynomial scatterplot. I used polynomialfeatures and fit to transform the data. I also did linear regression on the values per teachers instructions. I am not sure if it is correct since there are three axes instead of planar. When I try to make z-test data from x-test and y-test, I can't get the code to work. I am not sure what I am doing wrong. I really need some direction. # Plot Curve Fit x_test = np.linspace(-21,21,1000) y_test = x_test z_test= model.predict(x_test.reshape(-1,1))

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Time Complexity

In computer science, the computational complexity that is measured in terms of time is referred to as time complexity that defines how long it takes a computer to execute an algorithm. An algorithm's time complexity is measured by the the amount of time required to execute each statement of code in the algorithm. If a statement is set to execute repetitively, the number of times it will be N multiplied by the time it takes to run that function each time is equal .