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I replicated exactly what is shown in tutorial but I am continuously seeing internal server error. I used PyCharm, Codespace,VS Code. I am experiencing same error

Anderson Betty Clark Daniel Garcia David

The explanations kinda suck

Lind Walk

This was really helpful! 😀 Thank you so much for this wonderful video!

53087 Boyle Divide

747 Josiah Vista

Good overview about all methods. But why he did not used a classification report. I get horrible results for the minority class still.

I think this is what i was looking for. The only thing is that I need to build the tables with a specific stye: first row with another cell colour, specific cells in bold, etc. Any idea on how can I get this kind of things? Thanks!

Nicely explained!

Good stuff - could this be made "live" - like in a real time display of data being generated in real time.

YOU ARE AMAZING!!

Martinez John Rodriguez Daniel Walker Elizabeth

Brilliant tutorial - really helpful, thanks!

Wowwww

Brooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo, you are a savior!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

Thanks a lot for this great beginner course. I was struggling with FastApi tutorials and this yet the best I got from your course. Kudos to your valuable efforts and this course. Please add more content .

THIS IS AWESOME!!! Thanks for sharing. This is the best time series forecasting video that i've found.

Hi, Sir. Thank you so much for your explanation. Anyways, could you please give me the source of the materials from the video? I would like to use it as a reference for my bachelor thesis. Thank you in advance!

Thank you SO MUCH. Amazing video!

Does anyone else jsut get a completely flat forecast? When running forecast_test = model_fit.forecast(len(df_test)) df['forecast_manual'] = [None]*len(df_train) + list(forecast_test) df.plot() It tells me that the length of values does not match the lenght of index if I just df.plot() i can see the two, but it (the forecast) is just a straight line, which given the strong upward trend isn't liekly the result

Excellent tutorial! Tells me everything I need to know. Thank you very much!

thanks, nice explanation, thanks for article link

perfect for my last minute lab revision thanks for the effort

Thank you so much for this helpful tutorial

Why do you use Real or Interger on your hyperparameters? Thanks!!!

I wish you guys would've continued making videos.

Very good work !

good job bro

Love the tutorial and in depth explanation. Thanks

Thank you for explaining ARIMA so well with examples

rarely seen such a good video!

Great tutorial, but I have a question. Why did you change the result column to 0's and 1's if there's a target encoder? Can we keep them categorical?

and how do i add a new user? specially with authentication

Thank you ma'am!!!! instant subb!

this course is super packed wirh many information.. I tried to register for the courses In Udemy but they said the course is no longer accepting enrollment .. thanks a lot for this .. I love your teaching style

if both ACF and PACF has a significant spike then what to do ?

same please for R

I get a warning at the training step. np.int has been deprecated and removed, so I can't continue as it doesn't run (no warnings that could be ignored). What do I need to solve this? Thanks.

How do you interpret the prediction results? The results are all real numbers, can you look at each prediction on its own or do you have to evaluate as a whole? For instance person X target is 0.45, what does that tell me? Or negative values as the result what does that mean

at opt.fit the code blew up. How do you fix this?

the best!

The best!

Thank you so much bro..my question is if Can I use undersampling techniques before Splitting the dataset into training and testing?

Can we show or print the values of actual and predicted values

can i get the github repo for this

Thanks !

i wish i was smarter so i could understand this :(

are you using the logged data or the original?

Love your calm explanation style and right level of detail for a youtube tutorial - thank you!