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Would you please launch the third part of "XGBoost Regression" ? I hope can learn from you about hyperparameter tuning and how to find out the best prediction method. Thanks!

Is there another part? Really enjoyed it.

Excellent video. Please keep it up. Thank you. Are yo able to create a video for Nomogram for customer churn? Best wishes

the video in not visible to me Could you make it visible Sir?

xgb_model.fit(train_x, train_y) when i run this all the hyperparameters come out as none or false what should i do

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Can you please share the script?

Thank you for the tutorial, I have been looking for it for a long time❤❤

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You could read the empty columns then run janitor::remove_empty() to remove all empty rows and columns.

Very interesting topic, and very informative tutorial. 1. You could scan and catch errors by eye 👁 in a small data set. You need to relay on functions or formulas to hunt errors in a reliable and efficient way. 2. Changing chr to numeric. Why doing it by observation not by column? This is one would be straitfoward. df$colx <- as.numric(df$colx) # colx is column name

please put the video for part 3.

can you help me how to handle nonlinear relationship between features and target variable in Machine learning predictive models

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Thank you so much. You explained it so clearly.

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Thanks a lot, man!

how fix Error in library(ggradar) : there is no package called ‘ggradar’ ?

Thank you very much my friend!! Regards from Chile!

print(r2_score(y_train, pred_train)) = .9880135 print (mean_squared_error(y_train, pred_train))= 490.749 model.score(x_test, y_test) = 0.88860 print (mean_squared_error(y_test, pred)) = 3193.4547 will you please tell me what does it mean ?

Thank you so much for this video.

hey , my accuracy is around 0.96 for my data and MSE error is high too, what should I do to improve the accuracy and reduce error?

your channel is very underrated mate , keep it up i am your new sub. It would be great if you can upload video on working on project from scratch in linear , logistic and other r stuido algorithms.

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Great video! One small thing: x_text.loc[0] shows the persons information, but predictions[1:10] doesn’t include this person since Python is zero based indexing. Or am I wrong?

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Hey, great video! Particularly, I like how clear and detailed your explanations are as well as the depth of knowledge you have surrounding ML. Since I run a tech education channel as well, I love to see fellow content creators sharing, educating, and inspiring a large global audience. Keep up the great work!

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