Numpy, Matplotlib, and SkLearn are allowed

1. Generate 500 random X values from -3 to 3.

2. Generate 500 Y values using distribution "y=0.5*X⁵ - X³ - X² +2+a little bit randomness (both positive and negative) "

e.g.

m = 500 np.random.seed(seed=5) X = 6 * np.random.random(m).reshape(-1, 1) - 3 y = 0.5 * X**5 -X**3- X**2 + 2 + 5*np.random.randn(m, 1)

# y with both positive and negative randomness added

3. Use X and Y as the whole dataset and use 200 samples as testing + 300 samples as training.

Testing and training sets must be disjoint.

4. Try Linear Regression and Polynomial Regression (PolynomialFeatures + LinearRegression) in SKLearn from degree 2 to 25 to fit the training data samples.

5. Calculate the loss using mean squared error loss = average ( sum (prediction - y)² ) for all the training samples after the model was selected.

6. Calculate the loss using mean squared error loss = average ( sum (prediction - y)² ) for all the testing samples after the model was selected and used for testing data predictions.