During covid situation ur helping lot. Thanks a lot for ur help. Your simple superb and awesome topics and explanation.

Thank you very much. These classes are really helpful to me.

Very nicely explained sir, courses which are worth of thousands rupees don't teach like this. I really appericiate your work. Please keep doing these live sessions ,they are amazing

amazing Indian guy, you're doing great.

Thank you. It's amazing session.

@Krish thank you . Entire session was very much insightful

Amazing stuff Sir , keep it up .

Extremely Helpful Sir ✨Thanks A Lot ✨

You r awesome Krish !! Thank you.

Thank You So Much...Your Contents are really helpful

Awesome video...Thankyou very much

Very useful information .Thank you sir

Worth watching this session!

Very nice session... 👍

thanks a lot for the beautiful video....liked very much... could u please help me to understand what is Jhonson Transformation and when it is used and the python code to run the same...

You are the best ...😇😇😇

Do we have to standardise the data after converting data to Gaussian distribution by any transformation technique

Sir in my data there is some columns data are right skewed and some column data are normally distributed should i apply both gaussian transformation for both the columns or only for right skewed column

Hi. I see lots of pre-processing and processing steps involved in modelling. Is there any generic steps in order to do provided if needed. I meant can you pls provide a sequence in which steps like the following has to be done to get the well performant model from the lot?? missing values treatment polynomial features addition scaling normalization correlation / multicollinearity check pca/lda/da/fa dimension reduction modelling cross validation hyper-param tuning (grid/random search) model calibration report generation is the above order correct in sequence and is there any of the above steps which can be switched if needed and what all steps have strictly need to be in the specified order? Can you pls elaborate on this? Above was thought of from a regression problem standpoint, even though maybe some of them might apply to classification as well.

Hi Krish Do you gavd a video on encoding with ecxmes on writing the code. Will highly appreciate.

can we use different feature engineering methods to the same dataset for different columns?

Excellent session sir

only one word excellent

sir its amazing session

Linear regression or any other algorithms doesn't assume the feature's distribution to be normal. We convert it to normal just to avoid over fitting because of outliers.

We can also use df['fare_log']=np.log(df['Fare']+1) whenever we have zero values

@Abhisheksingh-sk2fn

3 жыл бұрын

as same as logp1 -real-valued input data types, log1p always returns real output.

Mast session tha

I have only one confussion that is in Exponential Transformation df["Fare_exp"]=np.exp(df["Fare"]) plot_data(df,"Fare_exp") I wanna apply this code instead of Krish's but there is a complete difference between them, what is the problem?

after gaussian transformation did we require to do scalling

finished watching

For right skewed use log transform.. And for left skewed use square transform

It's already done sir in a 20 minute video

Why are we transforming the encoded variable

Q-Q plot also impute outliers?

can you please tell what is range of standardscalar ?

@santhoshkumarmatlapudi2851

Жыл бұрын

-1 to 1

Why Dislike i dont understand

@joansaldanha5117

3 жыл бұрын

They r ungrateful...

could you please help me on this When to apply normalization and standardization before or after splitting the train & test data? still i didn't get correct answer from anyone. i hope you can give the answer for my question. And one request please do video on that. because many ppl applying the scaling method before splitting the data into train and test. it's ,y humble request to solve and give answer for my question.

@imantadatascience4827

3 жыл бұрын

before

It is right skewed sir when you will try to smooth the histogram so as to get probability density function we will find tail towards right side in case of fare column

Quantile is nothing but quarter or 1/4

Fare is right skewed.

Is Normalization and Scaling same?

@priyam66

Жыл бұрын

normalization is a type of scaling..:)

Is standard scalar ranges b/w from - 1 to 1

@krishnaik06

3 жыл бұрын

No it scales down the values based on standard deviation i.e between +3 and -3

@suryagangadhar1735

3 жыл бұрын

OK, thanks