Hello, great work! I am trying to learn through your code...
I have a question regarding your training sample, why you used only 5%. What I know is that it is normally 80:20, for training:testing set.
X_train_confirmed, X_test_confirmed, y_train_confirmed, y_test_confirmed = train_test_split(days_since_1_22[50:], world_cases[50:], test_size=0.05, shuffle=False)
In addition, from the beginning of the year the prediction of SVM is failing to predict well, while it was super before. What do you think is the reason? Overfitting? Thank you

Just wonder why did you pick "Polynomial Regression Prediction" over "Linear Regression Prediction" ? Won't Linear Regression Prediction will fit better with actual world_cases?

Not sure if I'm missing something but from the graph output ...your predictic values on the first 28 days are too far away from actual world cases value.

Earlier when you have summary for all countrys breakdown , for US, recoveries count is about 600K

but now when you breakdown by province/state they all are 0 recoveries count .

I haven't verify deaths, total case, active counts between summary by country vs by state.

In my opinion, instead of sorted by "number of confirmed cases", you might want to sort by Country follow by Province/State would be easily to spot out the 0's of all states of US.

Data source URLs and the format has changed somewhat. I had a great time with this though. Thanks!

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py:73: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
return f(**kwargs)
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\linear_model_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(

Not sure how, but I got DataConversionWarning at this the following codes cell:
(1)

svm_confirmed = svm_search.best_estimator_

svm_confirmed = SVR(shrinking=True, kernel='poly',gamma=0.01, epsilon=1,degree=3, C=0.1)
svm_confirmed.fit(X_train_confirmed, y_train_confirmed)
svm_pred = svm_confirmed.predict(future_forcast)

output(1)
/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
y = column_or_1d(y, warn=True)
(2):

bayesian ridge polynomial regression

tol = [1e-6, 1e-5, 1e-4, 1e-3, 1e-2]
alpha_1 = [1e-7, 1e-6, 1e-5, 1e-4, 1e-3]
alpha_2 = [1e-7, 1e-6, 1e-5, 1e-4, 1e-3]
lambda_1 = [1e-7, 1e-6, 1e-5, 1e-4, 1e-3]
lambda_2 = [1e-7, 1e-6, 1e-5, 1e-4, 1e-3]
normalize = [True, False]

bayesian_grid = {'tol': tol, 'alpha_1': alpha_1, 'alpha_2' : alpha_2, 'lambda_1': lambda_1, 'lambda_2' : lambda_2,
'normalize' : normalize}

bayesian = BayesianRidge(fit_intercept=False)
bayesian_search = RandomizedSearchCV(bayesian, bayesian_grid, scoring='neg_mean_squared_error', cv=3, return_train_score=True, n_jobs=-1, n_iter=40, verbose=1)
bayesian_search.fit(bayesian_poly_X_train_confirmed, y_train_confirmed)

Hi, I wasn't sure how to interpret the negative number that showed on about date 88 of your graph with title "China Daily increases in Recoveries" Can you explain why?