Відео

How do you handle dates that are the same? So lets say I have multiple 1/1/2024 rows, how should I modify the data?

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wow great! Can TFT Model use for rainfall induced landslide prediction? with variables like rainfall , soil moisture , slope. Can it be used even if there's limited dataset? or it requires large dataset? thank youu

What a fucking fantastic video

Good intro ty

Thank you for clear explenation about it

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This was the first video that popped up when i searched how is cholera spread. Thanks!! Love your voice too.

Good advice for someone who is trying to break into this job market like me. Thanks for sharing.

Here from the Coursera course. 😁

Thank you for your good Explaination. I'm writing at the moment my Bachelor Thesis about this and the Video really helped me.

GAAARRN,🧘‍♂ i love this video. i was searching for TFT and for this kind of explanation, love it

Hi, will this be covered in your course? Thanks

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Thanks a lot.

explicação top mano, obrigado! Alguma sugestão de onde estudar mais a fundo, com demonstração matemática etc? Só pela pura curiosidade mesmo haha

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Thanks..keep going(:

This is sad to watch. I'm almost considering buying this shitty and overpriced microsoft poduct so the Devs don't realize they have not accomplished anything in their lives

What does RAG stand for?

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can it predict stock prices?

Best Python for data science tutorial ever. I'm using it to ramp back up on Python after a 5 year hiatus. Can't wait to see what the reward gift is! 😀

Sir, I have determined soil Exchangeable acidity along with 7 other soil properties for 850 soil samples. I want to check like you did in the video by keeping Exchangeable acidity as Dependent and 7 other soils properties using Random Forest. Could you kindly share your invaluable knowledge.

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Your video are great just one thing you need creativity to thumbnail,title and opening section so people get hook ,KEEP GOING

Diogo Resende, you're a true data hero. 😍

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Hello! What about the "effect" option? I found that in my code it is necessary to specify [effect = "twoways"] when accounting both for state and year fixed effects, otherwise it just accounts for the first specified (in this example "state").

Nice concepts; thank you for making this video. 👍

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I LOVE THIS!!!!!!!!

Hi! I need help for my codes, i had this code: # Determine the number of steps to forecast steps = len(filtered_x_future) # Handle cases where no exogenous variables are used if fit_sarima.model.k_exog == 0: return fit_sarima.get_forecast(steps=steps) # Construct the exog array for forecasting exog_shape = (fit_sarima.model.nobs + steps, fit_sarima.model.k_exog) exog = np.zeros(exog_shape) exog[:fit_sarima.model.nobs, :] = fit_sarima.model.exog # Ensure the filtered_x_future has the correct shape if filtered_x_future.shape[1] != fit_sarima.model.k_exog: # Adjust the filtered_x_future array to match the required shape if filtered_x_future.shape[1] < fit_sarima.model.k_exog: # Pad with zeros if there are fewer columns pad_width = fit_sarima.model.k_exog - filtered_x_future.shape[1] filtered_x_future = np.pad(filtered_x_future, ((0, 0), (0, pad_width)), 'constant') else: # Truncate if there are more columns filtered_x_future = filtered_x_future[:, :fit_sarima.model.k_exog] exog[fit_sarima.model.nobs:, :] = filtered_x_future return fit_sarima.get_forecast(steps=steps, exog=exog) But it kept showing errors even tho i asked chatgpt: cannot reshape array of size 320 into shape (16,10), Provided exogenous values are not of the appropriate shape. Required (16, 10), got (32, 10). Do you perhaps know how can i resolve this?

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If date column is not present in dataset?

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Hello author , i tried your experiment on my dataset, features are not printed in shap?

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very amazing tutorial! very informative and funny 😀

ultimate causal analysis ! loved the tutorial

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Great video. Do you have any video where you discuss hyper parameter tuning for xgboost in R?

HALLELUJAH

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