Hello Krish sir, the determination you have in making videos, that's commendable.

U are the life saver I have already explored many many videos related to data science and ML and all of your videos are very understandable and go straight to the points . Thanks youtube and Krish Naik for such great tutos.

One of the best teachers out there

I am impressed with your energy and sound knowledge of your subjects. I always look out for your you tube video for detailed explanations. keep it up

I am new to the channel. I'm taking statistics courses in college right now and these videos are very helpful with making things easier to understand. Thank you. Subscribed

Ur teaching is little bit hard...m unable to understand in a proper way...I always want to see ur videos but when it starts.. after some time ..m exhausted

Thank you for making these informative videos. Being a student of data science your videos are gem and you are the asset to students learning the subject! Please keep uploading! Thanks

Krish, I am iNeuron student .. and I must say so beautifully you have explained this topics with a lot of clarity ... TY

Something that no one usually mentions but took me a while to grasp is that a high correlation between your output and indep. variable/feature is good for your model, but correlation between 1 indep. variable with another one is not great for model, and it's in that case where we need to work with them.

Sir ,Kindly revert data preprocessing videos..Because it was removed ..pls ..and upload overfitting and underfitting oriented real time program explanation ...thank u very much ..

How can be one so much talented.. Great explanation..

You are very energetic . I randomly clicked this video, but ended up subscribing this channel..

Your explanation is easy to catch. Worth listening. Accent is good. Giving the basic things along with really helps. Pliz keep doing this thing.. thank you

you are a good speaker. Things to be corrected in video -> sigmoid function is non linear! hence logistic regression is non linear. Svm as you mentioned is not a non linear classifier and it is a linear classifier.

so useful and clear, saved me a lot of confused wikipedia surfing :)

I was searching for this kind of video since a long time... value overloaded....Thank you krish for your wonderful content.

sir app best teacher ho bohot acha samaj aata hai appse plz keep sharing your knowledge with us and we will support you and learn new concepts of data science

Krish : Amazing lecture ..you are making me understand the fundamental of statistics so easily.. god bless you.

sir after a person is certified data scientist what are the other things he should learn to boost his career.

thank you so much....waiting 2nd part☺️

Hi Krish, I have one doubt with respect to Pre Data Processing techniques. I know it is very difficult to generalize but could you please suggest the most common Pre - Data Processing techniques. I'm not sure if it is a candidate for one of your videos.

Is it helpful to MBA RESEARCH METHODOLOGY AND STATISTIC ANALYSIS??

I guess you should use word multivariable as you are using one dependant variable,in case of multivariant there are more than one target variables

easy to understand way of teaching.

awesome explanation as always! thank you so much Krish!

I have progressed so much in short time following your tutorials. I hope one day to get a job of a data scientist.

Excellent sir 👍👌

Good useful video now i grasp the basic of how to apply the ML algorithm

Very good work. Looking forward to next part

You are a great Teacher

dude. you are good at teaching

Hi Krish, I'm seeing this video a bit late. But many things with respect to Uni and Multi variate analysis have become clear to me. Thanks Krish!!!

awesome tutorial I was having problem with these concepts u made it so clear easily. One of the best video

Thank you for the video. It’s clearly explained.

in class what ever they explianed for 3 hrs, you could tell that in 15 minutes .. Content is too good ... This is the first ever time i am commenting on some video bcz i couln't resist .

Very much easily understandable sir

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multivariate normality in assumptions and multivariate analysis are same

Practical example is required..waiting for next part..

Amazing, Thank you for making it very clear.

Thanks much Kris. Feature = variable. May you just do away with Y-component and only have horizontal line for weight. That is consistent with saying there is no Y component on Uni-variate analysis.

Hi krish please add compleat oops concept videos

you explain so well. thanks,

Good work bhai, best wishes.

Very useful tutorial. Thanks for uploading this tutorial.

Thank you Krish

Hi, hope you're doing well. Sorry I have a question. Is there any multivariate dataset in the internet that the variables are labeled?!!!!! As far as I've checked the multivariate dataset that I've seen, are labeled based on observations( for example observation 1 suffer from cancer, 2 do not and....) Now I want the variables have lables. Is there any data set? I'll be bery thankfull if you help me. Thanks in advance🌸

Hi Krish U r doing great work. Would you please suggest some resources to understand probability and linear algebra resources.

thanks for give us for video brother. keep it up.

Nicely explained keep it up

Can take univariate for single input features and multivariate for multilabel classification in NLP?

good explanation sir

plots used for multivariate analysis like PCA, PCoA and NMDS, CCA any video on that?

Hats off you sir... I cleared my concept through ur videos

Thank u sir for the valuable class

When we use these method before cleaning the date or after cleaning

Hi Sir Could you please make a video about BI services

Superb Krish 👍

Very nice explanation sir✨✌🏻💯❤

How age and DOB are different features?

it was great tutorial

Hi Krish, thanks a lot for your help, I have been learning a lot from you. Just wanted to know if you have a video that explains high-level end to end DS projects. I saw one that you had for Feature Engineering and wanted to know if you have one for the whole process?

you are great bro .. thanks . very useful.. w8 for more video with lots of examples : ) thnk

Following your plan of full data science and reached here till now. Lot more to go and will complete also for sure. Enjoying a lot. Thanks a lot Sir!

Logistic regression could have polynomial feature and many different features

What if i have categorical data and want to plot a heat map/pair plot

Great video. I have a problem related to the topic which I want some help with. Can anyone answer which one is correct and little explanation on how to solve it? Here is the problem: There is an email marketing template and we want to replace it with a better template. A is the control template. We also test email templates B, C, D, and E. We send 100,000 emails of each template to different random users. We want to figure out what email gets the highest click-through rate. Template A gets 10% click-through rate(CTR). B gets 7% CTR. C gets 8.5% CTR. D gets 12% and E gets 14% CTR. We want to run our multivariate test till we get 95% confidence in a conclusion. Which of the following is true: a) E is better than A with over 95% confidence. B is worse than A with over 95% confidence. You need to run the test for longer to tell where C and D compare to A with 95% confidence b) Both D and E are better than A with 95% confidence. Both B and C are worse than A with over 95% confidence c) We have too little data to conclude that A is better or worse than any other template with 95% confidence

what is the difference between regression analysis and path analysis

Wonderful Lecture.. Thank you very much..

What are the parameters for univariate/bivariate/multivariate Gaussian models?

Hello @KrishNaik sir, Many thanks for creating such a wonderful content. The links on correlation i.e. playlist for statistics, covariance and Pearson correlation are missing in the description.

Are Pairplot / correlation matrix - bivariate or Multivariate?

Krish, you are great. I was searching for videos related to these 3 analysis but couldn't find a good explanation like this. Thank you!

Sir what is mentioned in y axis grsph of univariate analysis how that number came??

Grateful explained

Quick question here, where do we use univariate analysis then?

Is excel also used in data analytics?

Thank you sir

Thank you for this video.

Please , how to perform EDA on dataset with many one hot encoded features??

Hi Krish, Why you have taken negative values the Y-axis ?

Sir, Is it permissible to perform multivariate analysis using the k-nn algorithm?

i regret that i underestimated you and your channel when i came across many times before. Sorry I judged the book by it's cover :( Thank you so much.

Sir can I ask a question what makes multivariate statistics similar from univariate or bivariate statistics

7:10 pair plot in seaborn

Hi suur I want to ask how to avoid overfitting.. Know that I used dropout(0.5) and data augmentation with fine tuning Please, any help!!

Please explain Cox regression analysis

Thanks Krish

Sir could you please also explain multivariate analysis in time series

How do I do these analyses with categorial variables?

Thanks Krish..

7:00 multivariate

thank you

sir can you send the this videos playist link plz sir

Thank you!

thanks

Hi, Please share the link of playlist of explanatory data analysis

Thanks!

Can you please let me know about Bivariate Demand

great effort