Tutorial 22-Univariate, Bivariate and Multivariate Analysis- Part1 (EDA)-Data Science
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- Опубліковано 26 лис 2019
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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.
Hello Krish sir, the determination you have in making videos, that's commendable.
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
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
I was searching for this kind of video since a long time... value overloaded....Thank you krish for your wonderful content.
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.
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
awesome tutorial I was having problem with these concepts u made it so clear easily.
One of the best video
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
so useful and clear, saved me a lot of confused wikipedia surfing :)
thank you so much....waiting 2nd part☺️
awesome explanation as always! thank you so much Krish!
Hats off you sir... I cleared my concept through ur videos
Good useful video now i grasp the basic of how to apply the ML algorithm
Amazing, Thank you for making it very clear.
Very good work. Looking forward to next part
How can be one so much talented..
Great explanation..
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.
One of the best teachers out there
So who is your favorite teacher in your place of study other than krish?
I have progressed so much in short time following your tutorials. I hope one day to get a job of a data scientist.
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.
Thank you for the video. It’s clearly explained.
Very useful tutorial. Thanks for uploading this tutorial.
Wonderful Lecture.. Thank you very much..
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.
easy to understand way of teaching.
you explain so well. thanks,
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 .
You are a great Teacher
Thank you Krish
Practical example is required..waiting for next part..
Thank you for this video.
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.
Very much easily understandable sir
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!!!
literally
Good work bhai, best wishes.
dude. you are good at teaching
Excellent sir 👍👌
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 ..
Thank u sir for the valuable class
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?
thanks for give us for video brother. keep it up.
Thanks Krish
you are great bro .. thanks . very useful.. w8 for more video with lots of examples : ) thnk
Superb Krish 👍
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.
Grateful explained
Nicely explained keep it up
Thank you!
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.
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
Thank you sir
Thanks Krish..
great effort
it was great tutorial
Very nice explanation sir✨✌🏻💯❤
Sir, Is it permissible to perform multivariate analysis using the k-nn algorithm?
thank you
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!
Thanks!
good explanation sir
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 after a person is certified data scientist what are the other things he should learn to boost his career.
If you are interested in business, I would suggest getting a CFA or FRM(Meanwhile, keep improving your skills in ML). By applying advanced machine learning techniques, you are probably able to make unintuitively valuable suggestions and extrapolations, which worth some money. (easy 250k annual compensation)
How age and DOB are different features?
Logistic regression could have polynomial feature and many different features
thanks
Hi Krish, Why you have taken negative values the Y-axis ?
multivariate normality in assumptions and multivariate analysis are same
Sir what is mentioned in y axis grsph of univariate analysis how that number came??
Hi Sir
Could you please make a video about BI services
When we use these method before cleaning the date or after cleaning
Can take univariate for single input features and multivariate for multilabel classification in NLP?
Sir could you please also explain multivariate analysis in time series
Hi,
Please share the link of playlist of explanatory data analysis
7:10 pair plot in seaborn
Sir can I ask a question what makes multivariate statistics similar from univariate or bivariate statistics
Hi krish please add compleat oops concept videos
Is feature engineering a part of EDA??
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🌸
How do I do these analyses with categorial variables?
Please
, how to perform EDA on dataset with many one hot encoded features??
Are Pairplot / correlation matrix - bivariate or Multivariate?
supper explanation
Is it helpful to MBA RESEARCH METHODOLOGY AND STATISTIC ANALYSIS??
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
Sir, how to perform EDA on dataset with many one hot encoded features??
@@DarkShadow-tm2dk thank you😊
What are the parameters for univariate/bivariate/multivariate Gaussian models?
What if i have categorical data and want to plot a heat map/pair plot
Convert ino numerical and then plot
plots used for multivariate analysis like PCA, PCoA and NMDS, CCA any video on that?
thank you so much sir :-)
Please explain Cox regression analysis
Good
Hi Krish
U r doing great work. Would you please suggest some resources to understand probability and linear algebra resources.
Please refer Statistics for Management by Richard I Levin and David Rubin
@@mcbhuva007 any UA-cam channels
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
True
you rock
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