Different Types of Feature Engineering Encoding Techniques
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- Опубліковано 14 сер 2019
- In this video we will be discussing about the different types of Feature Engineering Encoding Techniques
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Dear All, if you are looking for feature engineering materials, please check my feature engineering playlist, all videos are available. Happy Learning!
if you don't mind will u reopen the link or provide your writen codes on github with link
@@yash20december all materials are available in feature engineering playlist
Thank you sir.
is there something more you provide for the paid ones. please let me know.
Sir, Can you please send me the all feature engineering technique file. it will be very helpful to me, if you send them. My email id is
ara007kumar@gmail.com
What a coincedence, today is also an Independence day, this really suprised me, I was following your youtube videos and suddenly you greeted, for a movement it got a smile on my face. Happy Independence day.
Just started watching your videos. You explain the concepts in a simple manner.Thanks
you are the best, greetings from an ecuadorian studying in Portugal.
Thank you for putting the time and efforts to create this video, also all other videos. Very helpful.!
There was doubt from so long about this that when there are more than 100 types of value then how to do encoding which is clear today thank you sir 🙏🙏
Hi
Hi Krish, It's the best video I have ever seen. Crystal clear.
No Words for education. Many Thanks and wishes for futures.
The video is quite informative and easy to understand. I really loved the video :)
Wao thank you soo much, sir you explained soo well. whenever I face any doubts your video saves my day.. God bless u .. Happy Learning
We need mentor like you... Great job👍
thanks a lot, this thing can't be explained better than how you explained it.
I just became Fan of your ML knowledge.
Hi
I liked the Mean Encoding technique and Target-guided encoding. We are preserving the normality of the data as well as not increasing the dimensions.
you are doing a wonderful job Kris...👏👏
Thanks alot for sharing such a absolutely amazing knowledgeable video...
you saved my day with mean encoding
Thank U so much Sir for such Huge help....
Clearly Explained, Thankyou!
Very useful information provided by u sir. Thank you.
thanks sir for listening to my request to create a video on mean encoding , i am really enjoying your videos , and i have learned a lot from that. Please continue to create such awesome videos.
Hi
Hi krish, nice way to collect the data free of cost.
thank you so much respected sir. Alot love for you from pakistan. this video was very helpfull. we are looking foreword to see others playlist like these from you. once again thanks
Thank you so much for sharing your knowledge with us
Thanks for sharing, the video is helpful!
Excellent Explanation Sir, Thanks a lot
Nice information about feature engineering. Thanks a lot
can you plz send it to me
Amazing and easy to follow explanations! Newly subscribed and loving it! Just curious how you recover the particular categories and make sense of your results if you use something like mean encoding. Do you have to trace back the original definitions for each mean and what happens if there are repetitions?
Hi
excellent job Boss. really helpful
Hey krish, nice video as usual... Filled the form and thanks for making motivational and additional support videos for encouragement. Kudos
Hi bro, could you please send me featuring document pls?
@@SanthoshKumar-dk8vs you can fork it from either mine or krish's GitHub account. Check Krish's video description for his GitHub link and you find all there
Hello bro, can you share zip file, bcz I watched it today so not able to fill form as you know.
Kaushalshivam2018@gmail.com
hi bro this is sarath..
I am a data scientist aspirant can you share me feature engineering notes..
mail id : sarath20994@gmail.com
Sir u r doing really great and I think under your guidance I will become a good data scientist soon...please help me sir
You are the best sir.
Great Video, Thanks!
Hi krish, thanks so much for shedding light on this topic of Feature Engineering. I'm at Beginner Level of learning DS/ML and I really fell in love with your way of teaching these techniques. I would really love to get that document on FE you mentioned about in this video. I tried to drop my details via the google form but I see it's closed. Kindly assist please. Thanks in advance!
Amazing explanation sir 🙏🙏
so happy I found your channel...wooh amazing lecture
Please send me the zip file with respect to feature engineering
thank you sir
will definitely join your channel.
Krish Sir the way you explain is easy to understand. Please reopen the form. Thanks 🙂
Hi Krish, I have a question.
So, we are normalizing all numerical fields which will make it in the range 0 to 1.
While encoding categorical variables, we "one hot encode" a few which will make them either 0 or 1.
However, there are a few categorical fields where we are "label encoding" as order matters. It would be encoded like 1,2,3,4,.... which is not in the 0 to 1 range unlike the first and second case that I had discussed. I would like your advice here whether I need to normalize after label encoding the data and range of this case alone won't be in 0 to 1 and normalizing would make it in 0 to 1 range.
Great help Krish... Thanks for your video man
Vishal Shukla. could you please share this docs with me on dolly.shukla7860@gmail.com
Hi
Thanks sir for all these free contents! :p
Can you send the zip file to me.
arifmollick8578@gmail.com
Hello sir...your way of teaching is really incredible.
I am studying through your lecture for past 1week and that's why unable to fill the form to get the materials which you have prepared for the same...
So if possible please enable the form link again...
Sir, please share the link once again. I saw your video and it's a very helpful for the student's like me. I want to know more about the feature engineering.
Thank you for making such an amazing lecture. Waiting for the feature engineering link.
Hi
Great teacher. Thank you so much
Hi
Please re-open the form for feature engineering techniques. Thank you.
Hi krish,
I started seeing your videos now and want the feature engg doc. Can you please open the link for the form?
Waiting for your response.
Really good one Krish
Hello Sir, could you please open the google form link, i need those feature engineering code snippets, it would be of great help.
Krish your way of explanation is just amazing....Thanks for these amazing videos and yes please share zip file
Hi
Hi
Thanks Krish!!!! :)
Thank You Sir!!things we can understand easily by your Videos.Sir could you pleasee reopen the link where we could get the Feature engg materials that could be more great
Hi Krish, thanks for this very good video. Just to clarify a bit, when you say Pincode, are you referring to the postal or zipcode? Because from where I am, when they say pincode it means bank's pass code or something like that.
I agree with all the encoding you treated, however I got doubts on assigning an ordinal label based on the mean value of the output. By doing this you are setting the distance between levels to one, while in reality, based on the output mean, some level might be close than others, with distances less than or more than 1. What do you think?
I would also point out the risk of data leak in case of mean encoding, for this reason I'd add a random noise to the mean
Thanks Krish Bhai..I have learned a lot from your videos
The forum no longer, how do I get the material?
Thank you so much Kris, your videos have been instrumental in my learning process. However, I wanted to find out if it were too late to get the feature engineering code. As it mentioned, the link was opened fir two days hence its been over a year. Or if you have it on your git, I can get it too. I've been struggling with feature engr, though I'm still learning, having that code would mean alot to me. Thank you so much in advance
In case of mean encoding and Target guided methods, how will i calculate the mean value when i have hundreds of class in the dependent variable to be predicted? (considering many-many relationship of categorical value of a feature with dependent classes)
Great Video
plz give demo also
This is very informational video. I would like to go through the feature engineering material mentioned here. Will it be possible for this material to be available?
thank you sir from tamil
Very good
Hi Krish, I have started liking your channel so much. Hats off for the great service you are doing for the aspiring and already experienced Datascientists. The form url which you have shared is no more available. Could yuo please share the material via google drive or reactivate the form.
You are awesome sir 🙏
Hi
Hi sir,
I want the feature engineering document. Can u open that link
Hey awesome video there.
Just one doubt in the KDD orange competition. They came up with top 10 categories. How exactly did they arrive at the figure 10?
Isn't covering certain variance of total categories would have made more sense?
What I mean is, let's say if there are 40 categories in a column and if I select 12 categories that covers a total of 80 percent or 90 percentage of all rows. Wouldn't that make more sense rather than choosing static figure of 10.
Sir please could you please tell us why the theory of computation is actually used and what are the application of these subjects please Sir make a video on that
Sir , Thankyou for this wonderful lecture , please share the study material
Hi
I already joined as a member
I would like to learn something about it
"will u reopen the link sir"
Hi Krish, the google form link is not opening to fill the details. Can you help because I need the zip file that contains the coding part. Thank you
SIr,I want the feature engineering doc. Can you please open the link again?
Thanks man! Great content The Lord bless you with more understanding and help you to know Him better and better
nice one
In case of multi class classification and linear regression problems how we can do target guided ordinal encoding or mean encoding?
@16:18 position you are saying to use 'one-hot encoding with multi-category' for an ensemble technique. But the beginning of the video you had explained ensemble techniques does not require feature scaling. Can you please clarify?
I came across this video today and i like to learn more on feature engineering
"if you don't mind would u reopen the link sir"
Hi Krish, I just saw this video and the link is no longer available. Is there any possibility that I can get the material?
Sir I have a dataset 5000 rows and 36 columns. It is a mixture of both string and interger values. There are are two columns Name and Company which should be encoded. Which method would be the best?
Thanks
Hi. Happy New Year Krish. Your content is awesome.
Can you pls reopen the link to the encoding document?
Hey Krish, can you please do share the ZIP file which you have mentioned in the video about the Feature engineering, as I am unable to open the Google url link. it will be more helpful if you help me with the file.
Hi @Krish, can you please share the Feature engineering materials if possible. Your videos are really impressive.
Good one Krish. How do you mean encode variables from the test set ?
Hi Krish,
We cannot perform Mean or Target encoding on test data because we don't have target column in test data. So how can we deal with such a situation where we have variable with multiple level in it?
I am talking in respect with Hackathon where we generally don't have target variable, this is something which we have to predict.
Would appreciate your help.
You already got the ordinal number or the float number for each category class from the training data . So you dont need to do it again in test data. You will simply use it.
You might already know this.
But I am answering if someone else has this doubt.
Hi krish.. google response link not active. how can I get the material
Awesome
I am not able to fill the form Please let me know how i can do the same so that i ca n rcv your material on Encoding
can someone share the feature engineering doc of krish pls? i missed filling the form.
Did you get the material? If yes, can you share it?
What if my target feature has more than two labels i,e 1,2,3... which technique should I go with?
@krish Naik I have a doubt. FOr mean encoding and target guided encoding we need labels for encoding but how would we encoded the data at test time. ?
Sir!, Your Notes are not available on google now. Form also does not exist there.
Hi Krish,
At 20:23 the Label for A - 0 and A - 1 will be different based on mean right ?
for example the mean will be calculated this way right ?
A - 1 => 0.73
B - 1 => 0.6
C - 1 => 0.4
A - 0 => 0.5
B - 0 => 0.35
C - 0 => 0.36
Then the ordering of feature will be as below right ?
A - 1 >> B - 1 >> A - 0 >> C -1 >> C - 0 >> B - 0
Hello Krish, how can we use both categorical data (one-hot encoding) and numeric data and feed into LSTM time series model
Could you please upload the forum again . ?
Thanks in advance :)
Sir please open the form enteries to get zip file for feature engineering
Hi Krish ,
Mean value will be replace with the category variable values in Mean encoding . will the same apply for o/p as numeric values . If the target variable is number , calculated mean will replace by categorical variable values ?
Hi krish,
Today only I saw this video.could you please kindly open that Google forum one more time.
Hi sir,
I want the feature engineering doc. Could you please open the link for the form?
Please
I've got a question...
Is feature engg and EDA same?
I just started with ML. Is there any way to get access to the feature engineeringmaterial? Thank-you.
My doubt is with the mean encoding
What if two values in one feature get the same mean ?
Hey Kris u r awesome 😎 dear, u have boost my knowledge n conference.
I m not able to get ur shared material, so please can you help me with this 🙏