Min-abs scaling is often used in situations where the signs of the original values are important and should be preserved, such as when working with financial data or when the scale of the original variables is not important and all that matters is the relative ranking of the values.

they way you teach every thing is just amazing i love it really. i used to learn from many platforms but you are the best of all in conveying the exact meaning in a beautiful way thanks a lot sir and stay bless.

That is most valuable things I ever come across, thanks for this great content.

@learnenglish699

2 жыл бұрын

hello i have some doubts

I have seen others explaining Data science topics ..but you are way far from everyone.. ❤️

Great teaching sir.. Thanks for such great content...

I realy love this channel what a great explanation

Very informative, best Ml explanation, step by step on KZread .

Added some useful knowledge today Thanks for this good work😀

free me bahot accha padhate ho sir app

This is very underrated channel!

Sir, I can understand Hindi little bit, but still can grasp maximum based on your skills. I am having one question. I have introduced 3 outliers records one each for 'Class label' and applied MinMaxScalar. As you teached values are scaled between 0 and 1. But when i compare using kdeplot before and after scaling still i see those outliers between 0 and 1 spread. I am thinking those 3 outliers will be mingled with other values and that is the way we are going to eliminate outliers. plz correct me if i am wrong. Thanks in advance for this stuff.

Sir can we do min max/standard scaling on y or target columns? If the target data is in continuous or regression form.

Thanks sir

Hello Sir! I want few minutes of yours. I am following this series and it's amazing. I just want to ask something that can you suggest me any book or any project source so that I can practice all concepts practically as well Thank you so much Please reply

Any detailed video on getting started with sk learn? Pls

This video was incredibly helpful

Sir, kya ho agar minimum ya maximum ya phir dono test data me ho...

thanks bro

Thanks

Hello! if we are splitting before applying MinMax Scaling, it is possible that maximum value of one feature say 250 end up in Test split. How would then MinMax scaling work considering we are only fitting it on Training dataset.

sir thanks for this amazing play list. students I face some issue while plotting the sns plot at 8:50 then i try this line of code. it helps and resolve. i put here for some help. sns.scatterplot(data =df, x='alcohol',y= 'malic_acid', hue=df['class_label'])

Great 👍

when to use standardization and normalization , but are sqeezing data but when to use which one.

Very nice explanation in Feature Scaling. I have one doubt though, as it is mentioned, Feature Scaling is applied very last once everything is done like handling missing data, categorical data, detecting and removal of ourliers etc. In that case, when we always handle outliers first and then apply scaling, why do we need of RobustScaling for scenarios like outliers? We would not need to think of outliers while applying scaling.

@manojrangera5955

2 жыл бұрын

If outliers is our dataset are more then that outliers play an important role in ML algorithm.. May be some important information that y we didn't remove outliers and use robust scaler... Am I correct?.. Just clarify it..

@osho_magic

Жыл бұрын

Outliers can’t always be omitted entirely

I'm Addicted to your channel ❤

@monikrayu2546

25 күн бұрын

ok

I'm Addicted to your channel ❤️

@monikrayu2546

25 күн бұрын

ok

Why in last graph the scale is not from 0-1 .. it shows value of -0.2 to 1.2 ?

finished watching

Hii, where can I find the OneNote for this playlist

awesome

Sir, where we got these notes, which are u teaching

💗💗💗

Suppose there is a data feature containing height, ranging from 10 to 50 now lets suppose we did split the data and according to the random seed we took the training set got the range of height from 11 to 48 but those data points having 10 and 50 heights went into test set, now the data is fit on 11 as min and 48 as max, now if we transform the test data these points will results in less than 0 and more than 1 values data points after transforming

@Adventurebhat

4 ай бұрын

Thats why the concept of seeding comes , while train test split , so that the train and test splitting can be random

can you please update the dataset?

Teaching at its best... superb sir 🙌

@campusX Just A Question should we scale the target variable also or it's only for the features that are inputs to the models

@shahinanjum5287

4 ай бұрын

Only for features (independent columns)

Hello sir, this is a suggestion, can you please make a video explaining the pipeline concept of the sklearn library. I have tried to learn from other videos from YT and official documentation, but I can't understand and implement pipelining in my code.

@campusx-official

3 жыл бұрын

Will do it in a few days

@shreejanshrestha1931

2 жыл бұрын

Yes sir its would be great. 😄 cause you explain the best

Sparse like in digit data

Love you sir from Pakistan

🎉

Great Videos Thank you 👍

X_train, X_test, y_train, y_test = train_test_split(df.drop('Class label', axis=1), df['Class label'], test_size=0.3, random_state=0) I DIDN'T GET first two steps of lines of train test split because sir ne pehle ke videos me (x,y,test_size) ye format bataya tha split ke liye ye class label drop kaha se a gaye?

@tanzeelmohammed9157

11 ай бұрын

df.drop('Classlabel',axis=1) is basically your X because you're dropping your feature variable, while df["ClassLabel] is your y

done

Thank you sir!!

Could you please confirm if we normalisation/standardisation of target variable should also be done along with input variables? Kindly explain with an explanation or link to resources which explain this question.

sorted

day2-date:10/1/24

I have one more question: why do we need to train_test_split first before applying scaling. What if Data is very huge and learning of mean, SD from training data would give wrong idea because test data set has some different observations which could hamper already learned mean and SD. I know this is very rare scenario but this could happen.

@manojrangera5955

2 жыл бұрын

That is my question also.. Some time we do train test split after that use scaling sone time we don't do and use in while dataset ... Y I that?... Can you explain me that

sir why do we do fit our train data only to scaler object and know we need to transform train and test data but why only train data is to be fit?

Could you please share the onenote link?

I have seen others explaining Data science topics ..but you are way far from everyone..

thank you sir