I am really grateful for this video. I am doing research with my professor. And this is really an essential skill for me to conduct research with him. Thank you so much! I do appreciate your wisdom!
I loved to watch this video! it goes to the main point, your explanation was very clear and you've taken ur time to avoid letting any detail out. At the beginning I was considering if I should see ur video cause it lasted 13 minutes and I don't like to see videos longer than 5 minutes xd but I'll leave happy cause I've understood this topic and now I'll be able to apply this in futures data cleaning.
This video Really help me a lot for outliers. thankful to you and very clean and decent explanation, please do more videos on machine learning. Thanks a lot
I was doing something similar, with no results... Guess what: I used & instead of | when finding the lower and upper bounds. Thanks a lot for making this video!
thank great video i have question if i have about 446 feature how can i deal with it like in your example i tried to store the features in a variable X then use your code but it did not work any help please
I used the same technique for my dataset but outliers are still persistent any suggestions what to do? I tried rerunning the loop it removed some outliers but that reduced the original dataset i was working on. Anyone has any better suggestions?
index_list = [] for feature in ['feature1', 'feature2']: index_list.extend(outliers(data, feature)) index_list = [] ----- > For this i am getting an error : Boolean array expected for the condition, not float64 , How can i fix it ?
index_list = [] for feature in ['feature1', 'feature2']: index_list.extend(outliers(data, feature)) index_list = [] --> seem to have created two index_list so modify this line as index_list
great coding but operation should be column wise not row wise, you are removing a possible valid adjacent value by using the index, imagine a large dataset with 500 columns...
I am really grateful for this video. I am doing research with my professor. And this is really an essential skill for me to conduct research with him. Thank you so much! I do appreciate your wisdom!
Your voice, the music and the explanation: everything is amazing! Thanks a lot ♥
This video is excellent, I tried the method on another data set , it worked a treat.
Dear Eigen B, Please upload videos on machine learning & higher stats. I found this video, which helps me a lot. Your way of teaching is good.
I loved to watch this video! it goes to the main point, your explanation was very clear and you've taken ur time to avoid letting any detail out. At the beginning I was considering if I should see ur video cause it lasted 13 minutes and I don't like to see videos longer than 5 minutes xd but I'll leave happy cause I've understood this topic and now I'll be able to apply this in futures data cleaning.
Wow. Watched entire video. So peaceful. good job!!!!
thank you so much you saved my data mining project
Excelente video, estuve buscando bastante y tu lo explicaste super bien todo
excellent explanation and pace! so calm, will never forget these part #removing outliers
This video Really help me a lot for outliers. thankful to you and very clean and decent explanation, please do more videos on machine learning. Thanks a lot
Thank You! Very helpful !
Thanks alot Eigen B. Its really helpful.
Very nicely explained. great work. Thanks.
Awesome....Thanks I love the method of teaching and background music
this really helps me, thank you so much!
Nice work. Liked the simplicity and the soothing voice + music.
thanks, you helped me a lot!
This is amazing thanks for sharing and such a lovely explanation
Every thing is amazing ! , More than very helpful. thank you
Thank you!!!! you are amazing
Thank you! Your video was really helpful for me :)
Thank you so much!
I wish i could show you how much thankful am i
🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏
Genia me ayudaste mucho
Sweet voice....Nicely explained.... Thanks
Great tutorial
Thanks a lot!
Thanks for the help
thnx u so much.... really tqqq
बहुत अच्छा सिखाया बहिनी
Thanks!
I was doing something similar, with no results... Guess what: I used & instead of | when finding the lower and upper bounds. Thanks a lot for making this video!
Is there any way to replace those outliers rows with upper_bound or lower_bound please help
thank great video i have question if i have about 446 feature how can i deal with it like in your example i tried to store the features in a variable X then use your code but it did not work any help please
great video! One question though: what if you only wanted to drop the outlier values and not the whole row in which the outlier is found?
not possible.. but you can replace outliers with NaN but again.. no point of doing that
It won't be like that; we can't remove only outlier we can remove entire row only.
I used the same technique for my dataset but outliers are still persistent any suggestions what to do?
I tried rerunning the loop it removed some outliers but that reduced the original dataset i was working on.
Anyone has any better suggestions?
❤❤❤❤
i tried these codes and it doesn't work. it shows(an only compare identically-labeled Series objects)
How we can determine the value of the quantile?
what if data has no outlier. In that case we will loose tiny data? how to know if not outlier removal is needed in big dataset?
Hi. I have one error: "Name 'dt' is not defined" when i ran cell [9]. can you help me
This should be titled Pandas ASMR
what will be the output of In[8].. can anyone explain?
Thanks, can I get the test.csv file?
Instead of removing, how can we impute median values ?
index_list = []
for feature in ['feature1', 'feature2']:
index_list.extend(outliers(data, feature))
index_list = []
----- > For this i am getting an error : Boolean array expected for the condition, not float64 ,
How can i fix it ?
index_list = []
for feature in ['feature1', 'feature2']:
index_list.extend(outliers(data, feature))
index_list = [] --> seem to have created two index_list so modify this line as
index_list
No entiendo ingles, pero entendi el video :D
Error: TypeError: Cannot perform 'ror_' with a dtyped [float64] array and scalar of type [bool]
what is ft? here?
'ft' is short form for feature.
Could you share your code? Thanks
Dear Eigen B,
Instead of removing the outliers kindly help to code- how to replace them with mean value of respective column.
Hello, I write your code And nothing happend, thank you for the video anyway
Define outliers error is coming
great coding but operation should be column wise not row wise, you are removing a possible valid adjacent value by using the index, imagine a large dataset with 500 columns...
where vids mazafaka