Very helpful. When u said, u are putting up these useful tutorials in public domain so that people can learn and will help them for the Job in this Covid Situation... Man u just earned a lot lot of respect and blessings. Thank you Krish. 🤟🏻🙏🏻

Superb, thanks for the knowledge, session starts at 7:40, writing in order to save time

God bless Krish Naik for everything he has done so far on his channel. The channel is a gold mine. You are indeed a God sent.

Sir i am watching this video today.. and i really want to thank you ... you are a amazing person, teacher and a great motivator ... your you tube channel is a gold mine , really sir. If i really succeed in my life one day, there is a lot of contribution of yours sir🙏🏻🙏🏻.Please bless us as our guru.. and God bless you for doing such a amazing job everyday for all the needy students..

Its very helpful .You are an inspiration of mine whenever I stuck at any Topic first I remember u and ur videos that return my confidence. Thanks a lot.

Man is very honest, one can learn from here and very helpful

I cannot thank you enough for all your videos and efforts, sir. You are an AMAZING teacher, and I wish you all the best all the time. Many thanks and blessings to you! Thank you !!!

Very comprehensive!! I loved each and every bit of it

This content helps me quite well and i must say your way of teaching is amazing ❣️

Love your streams and makes me motivated to keep learning everyday and be better

Great job Krish. Your sessions are very much useful for many people.

Bless you brother. All nice instructive videos

Amazing! You are doing great work.

Thank You Krish, for providing such tutorials.

VERY VERY NICE SIR , U R DOING VERY GREAT JOB , HUGE RESPECT SIR

Thanks for your contribution it means a lot to us

Thanks 😊 for the video!.

Krish... Thank you for a wonderful video and free knowledge...

I LOve yOu siR .....kya padate ho aap...maze se aagaye ...interest or bad gaya data science m..

I m learning DS from different playlists in your channel, sir!..so nice experience.. but first time I knew that you speak hindi so nice😁❤..

Really good videos.. I am learning a lot from your tutorials

I really like your all video sir way of teaching is very good. Thankyou sir

Amazing Video Sir!! Thanks!!

You teach better than most professors at universities in the US

Thank you sir, its a great learning experience

Your channel is disturbingly fantastic

This is how you should learn things -- Agree !!

thanks for such wonderful sessions

Loved it ❤️

Wonderful session sir 🔥💖 thank you very very much

Had finals at my Uni, so happy to be back :D

U enumerate so well..

Hi Krish , i appriciate your work , thank you for this video

Awesome tutorial. Thanks bro .

Thanks a ton krish sir !!!! very informative

Thank you so much Sir ❤️🌟

Wonderful session Sir !

great great job hats off

Thank you so much, it is very helpful

thank you so much krish!!!

Thanks Krish!

MCAR -->> there is no relationship between the observed value and unobserved value. So the missingness is because of random Eg. We are visiting the library and go through the Entry book and we notice there is 5% of missing records. So in this case the 5% missing records can be of any record in the entry book. There is no particular answer why there are missing values. So probablility of missing value is same for all record's in dataset MNAR --> There is a relationship between missing values and the unobserved values. There is no relationship between observed value and the missing values.. Eg. We are conducting a survey of depression .It is each individual choice to poll so here missing value is depend on the individual who is unknown. So it is MNAR MAR --> there is a relationship between the observed value and missing values, not btw unobserved and missing values. And there can also a missingness in subgroup of the observed values. Eg. We are conducting a survey of salary. Here most of the men won't poll and some of women won't poll for salary.. So the missing values of salary has a relatiomln with the gender.. I hope this will clear the doubt. Don't rely on single source, Nobody knows everything, so don't trust anyone🤣

Very helpfull lecture sir ...

Great thank you

Thank u so much very helpfull this tutorial

tq soo much sir

@Krish Naik what are your views on Matlab I heard that Matlab will help you in understanding the ML algorithms and implementing them while Python comes with in built functions

nice... much needed, btw satik hindi punch Google pe sab mil jaata h 😁

Thanks very useful

Very helpful

Hey Krish: Can you do a video on how to model outliers for time series analysis?

Hi sir..can we handle missing values by finding mean/mode value of respective categories of another features for which it is Nan instead of replacing mean of whole value?

Does imputation result in overfit because, for e.g in this case where there are lot of nan values, they are replaced by the median. This means when plotted, lot of values will be close to or on the median?

Other disadvantage, u dont care about impact of otehr Xs Variables, so I prefer using multivariate imputation techniques like MICE. Thanks for your fideo

how we build code where all text which has already been predicted ... we will not redo it again .. we will only do the prediction on the newly added text ??? can you please suggest some ideas ?? how to implement this ?

We will get the notification sometime in between the video. After 30 min or so. Is there anything could be done so that we can get notification ahead

finshed watching

@KrishNaik So to help me better understand, the variables cabin and age are missing not at random because both are missing for the same reason which is because the passengers are not alive ? is this correct?

Doubt: Embarked NAN values are from the cabin value 'B28' does It have any relationship(cabin,Embarked).

million likes on this video.

ecxept using median to fill the nan of age can we use mode to fill na?

when i use the subplot part it says module is not callable..why?

sir can we replace NaN value of column by mean in such a way that if other parameter value is in a particular range than find the mean and replace . Example..if column BMI has NaN value then if age of that person is 45 then we first find the mean BMI of people with a age of range 40 to 50 and replace with this.Similarly,for other person have NaN BMI ... then first check the age of that person and set an interval age and find mean and replace...

Love ur videos..incase i get a job .il be giving first salary to ur channel/you

hi sir, if a unique id is missing in that situation how to handle that.(For example, a data set about the customer in that customer id is missing means what we want to do . (condition should not delete the row in the customer id column )

hi krish i am new to your videos so can you please guide me through which playlist i should go first

Hi Krish, We have to apply the mean/ median/mode to variables of MCAR not MNAR right, and as per the video Age is a type of MNAR in Titanic Dataset right ? Not sure if i have missed it

Not able to find this notebook in github repository

You should make a more distilled version of this video

54:02 we also studied recursion.

sir in this video at 1:06:16 if in the mean/median/mode method we have to replace NaN values with most occurring values then why we are using median instead, why we r not using mode here?? (bcz mode ultimately used for finding the most occurring value in the set). waiting for ur reply....

How to handle missing(NaN) values in column having binary data values i.e Just 0 or 1 ?

Hello Sir I am unable to buy the plan.Is that still there?

Hi krish, how do I get membership for your channel

Hello Sir, I am getting a bit confused while analysing the percentage of survival and death using groupby and cabin_null at 48:22. If we see the Survival column there are also values which shows survival = 1 but Cabin is Nan eg- 3rd row and 9th row. Can you please explain my doubt? Thank you, You are true mentor

What was the purpose of adding Cabin_null? Why didn't we do df['Cabin'].isnull().mean()? Editted: Yes got it. So if you do mean * 100, you get the perc.

Mean/median/mode use for MCAR missing dataset but age is not MCAR.. So i didnt get why we use it there?... Can anybody tell me.. I am confused...

Sir How to Embarked are related to Cabin and Age Missing values Sir Can you Give brief Explenation on it i am not clear at some points.

For most frequent occurrence, Mode is calculated and Median is the central value of any set of values. Sir, you said we will replace NaN values with most frequent occurrence of variables. But after this part of video you started calculating median. If I am confused, please do correct me. Thanks.

@19:00 - I think there is nothing like Discrete continuous data. Data can either be discrete or continuous. Make me correct if i wrong

The kernel of day1 is not in github

Mean imputation is not robust to outliers. Only median and mode are

how to take your membership ????

when we compute the mean or standard deviation of a feature having missing value (NaN) .So while doing computation this NaN value is igonred or replaced by Zero ?

Any one tell me please only feature eng previous video can i understand these live videos...please tell if any prerequiste video is there in playlist.

Krish I want to join your DS membership but am unable to join due to some technical issue. Can you provide the Gpay account?

bro can you add english subtitles to your videos. Just a feedback!

confusion in mean/median/mode technique. Not clear. Where we will use mean/median/mode in MCAR ?

Main content starts from 22:15

Ye hum , Ye Krish SIr hain aur ye humari Pawri ho rahi hai

high bais high variance cause overfitting

Video Starts around 7 minutes in

I am 15+yr exp in sys admin,learning ML,DL quickly,could i get success,will some one consider me ,i like DS a lot now.should i switch in this field

how is it cabin data is missing? when someone is on-board on a ship, i would assume that cabinet number is assigned. that data should not be missing. with each name, there should be cabinet number associated imo.

what is Telegram group name? Can someone share?

56:45

26:59

Hi Krish, I have couple of doubts. 1.Here Age feature is not MCAR right rather MNAR, it is missing because those passengers died. So why we have used mean/median imputation. 2. By median imputation, most of the data would lie at the center so the impact is more at kurtosis than spread/variance. Could you clarify....

watching the playlist in 2024

Nope m not getting notification