Tutorial 25- Probability Density function and CDF- EDA-Data Science

[@howtotipsandtricks4381]

Institution of Data science claims that they have good content for freshers also but it is no there, i always have to come in your channel for many topics clarification which I could never learn from there. You have a great skill of teaching😊

[@HarshitSharma-k3p]

you are much better than many online courses in market ...thank you please keep going.

[@hadishaaben3665]

Man you do not know how much i learned from you , your explanation is AWESOME

[@jhondhelpago1638]

He explained two topics in less than 10 minutes, yet its so clear and informative

[@mischievousmaster]

Krish loves the word PARTICULAR a ton! 😁

[@sunnyks108]

PRETTY MUCH too :)

[@srujankumar637]

you are an awesome teacher; seems like completely dwelling over the concepts... simply i take a bow

[@Ankurkumar14680]

Thanks for sharing sir, I always admire your teaching style, knowledge and helping nature. Small clarification, in normality plot, values on y-axis does not tell us area under the curve. In this way, y-axis corresponding to mean value on the x-axis will always be .5 but that is not the case. Actually, it is the gradient value of the CDF function (graph).

[@tensorthug6802]

The y-axis of pdf is gradient of cdf, the higher the gradients, more the density is at that particular point. The y-axis of CDF is the percentage population of a particular point.

[@MuhammadAbdullah-lr7sd]

Yes, that is what I'm thinking but in the video it creates some confusion.

[@t.saishodhanrao9519]

this comment should be pinned...... It creates lot of confusion for those who don't about this

[@im_tanmay_g]

Most simplest and non-confusing video on PDF & CDF. Thank you for the same.

[@cherubyGreens]

Feeling amazing with Krish Naik!

[@phalgunaa2157]

Your explanation is too good bro

[@nvsyashwanth918]

The way you explain concepts is amazing.

[@livebiochemistry]

One of best video of PDF and CDF..thanks sir

[@amiysrivastava1444]

best explanation of cdf when compared with other youtube videos.

[@johan-mattias]

learning about machine learning for google spreadsheets, this helped understand the CDF so much thank!

[@Vidi_111]

Thank you sir .. the way you explain is so easy to understand...

[@sapnilpatel1645]

Amazing video sir. Thank you so much.

[@bluestar2253]

Excellent explanation of PDF and CDF

[@VVV-wx3ui]

Simply explained. Good going Krish.

[@oliullah.mahmud]

Thank you. I like your teaching style!

[@TariqueMahmud313]

Too many clear concepts in just 7 minutes !!! Thanks man!!

[@chaos8514]

hello are you also trying to learn data analysis?

[@VVV-wx3ui]

Thanks Krish for sharing your knowledge. Please keep it going.

[@bandhammanikanta1664]

Thanks for this video Krish. We will be very happy to see atleast a single reply to any comments in youtube as well as issues in his github.

[@krishnaik06]

I usually see the comments and make a note on it to create videos...github videos will be coming up soon

[@bandhammanikanta1664]

@@krishnaik06 Thank you Krishna. Waiting to see your updates.

[@himanshubansal2701]

@@krishnaik06 sir what is benefits of cdf over pdf ? bcoz we will be analysing same precentage with pdf also.

[@padduchennamsetti6516]

wow you are awesome,the best

[@kabyabasu]

Krish is the God of data science

[@navinofficial5439]

Crystal Clear!

[@ahmedel-bahnihi346]

When you explain the PDF, you said, it is the area under the curve till that point. I think this is the CDF, not PDF. Thanks a lot for your effort nd videos

[@dharunsainath322]

that is correct...he is probably talking about CDF

[@madhuprasath6193]

A query,then how do you interpret a pdf?

[@dattamalpote2005]

i think krishna sir had explain it right.

[@mitultandon5227]

@@madhuprasath6193 It basically gives you the probability of that point. PDF would answer a question like these:- What would be the chance of weight of a person to be 90kg?. Answer to this as per the above graph in the video would be "only 25% chance ( or 0.25 probability )". Basically PDF tells us the exact probability for every point.

[@srujankumar637]

area under the curve in particular (definite integral) range is pdf:: total must be unity

[@santamsaha9415]

this video is a soul saver

[@NR_Tutorials]

nice videos thanks krish naik sir

[@Ks-oj6tc]

Well explained, Thanks a lot Krish.

[@brayansereno4249]

Hi Krish, thank you so much, I speak Spanish but I understand you, I really need an explanation of this topic and I don't found in Spanish, you're great bro

[@mahalerahulm]

Excellent !! Very nice explanation.

[@AJ-et3vf]

awesome video sir! thank you!

[@Emotekofficial]

As far as I know CDF is Cumulative Distribution function. It can also be calculated for Probability Mass Function. But you can say in this Scenario as Cumulative Distribution function of given Probability Density Function.

[@saurabhtripathi62]

thanks your series is great , u made this very easy.

[@jaheerkalanthar816]

Thanks for the video sir, I learned lot of things in this video

[@BalaguruGupta]

I've commented on your other videos also, there I understood the love you had on the technology. From this video I understood that, actually you're so much passionate on teaching sir. The way you explained PDF and CDF is really amazing sir. Thank you so much. :)

[@muhammadyasirbutt3631]

very well brother your are great teachning

[@sagarkhande4412]

Ty sir your video was really helpful..👍

[@vidyasurbhi3084]

Very nice 👌.

[@shadiyapp5552]

Thank you sir ♥️

[@kpratik5551000]

Very good explanation.

[@tsaurav18]

thankyou sir.

[@DataAI_junction]

thank you for the video

[@aimenbaig6201]

you are the best

[@mohammedabdulahmed8808]

Simply amazing explanation 😍😍 Thanks alot and keep doing sir!!!

[@magedrefat1658]

Sir, your explanation is amazing ^_^

[@xeysus5907]

Great one

[@TheAl217]

Thank you for clarifying these functions.

[@ArchnaVijay]

amazing video

[@aditisrivastava7079]

Thanks to wonderfull video..............i will simply add through pdf we can find the probababilty for a point or a range whereas cdf tell about the less than probability

[@hbk788dbz]

PDF values can be greater than 1 ! (your y-axis can be greater than 1 ) . Area under the curve cannot be greater than 1 . Please correct me if im wrong @krish naik

[@ranjanpal7217]

Amazing...plz make a video on how to determine the distribution of a dataset using Python.

[@PriyaAmar848]

Why do I need to use this distribution ? In which cases of data it's helpful ? Also we have uniform distribution, binomial and poisson. Where to use these. Appreciate if practical examples are included. Great explanation with graphs. Keep up the enthusias,

[@tsrnihar]

Hi Krish - Appreciate your effort in putting together the videos. Want to redflag something. You mixed the concepts of PDF and CDF - You are using PDF but conveying the meaning of CDF. point on a PDF indicates probability of the point in the distribution. Whereas point on a CDF indicates the cumulative probability up to the point. This is also area under the graph.

[@shrimaykher2978]

PDF doesn't indicate probability of the point in the distribution. In fact, it is very troubling to believe but probability with which continuous variable takes exact value is zero. We always talk about probability of random variable falling into an interval using PDF by finding area under curve. Moreover as sir explained, i would like to make a little correction that pdf can go beyond 1. Y-axis of PDF is probability density not % distribution. PDF is not actually probability, but rather a density function which tells amount of probability per unit length, therefore it can go beyond 1 unlike PMF. Story of discrete and continuous variable is different and we cannot mix up the theory.

[@equbalmustafa]

Nice one

[@mukundsudharsan1294]

In 3.08 you mentioned that the y-axis in the normal distribution represents the % of distribution below that point. If that statement holds true then shouldn't the graph be continuously increasing and it would be cdf? So what does the y-axis indicate for the normal distribution falling in the right half? Please correct me if I am mistaken, but would like to understand this better.

[@kantafcb1]

y axis shows the %age distribution of intervals

[@muhammadsaqib2961]

Good explanation

[@schuf1738]

Thank you !!

[@deepakbhaiya.shorts]

Old is gold

[@KalyanGk0]

Great explanation krish😊 .please make video on practical implementation of this concepts using python.

[@saddamshaikh9285]

Sir please make a video on navie byes algorithm.

[@sudhirBhalekar007attaboy]

Why do we calculate CDF as PDF is already giving you % of distribution for required data analysis, through this CDF, we are getting added (C.values) but what is the significance of this concept?

[@ahmed96616]

Excellent !

[@nightowl1596]

elite explanation, gg

[@sharathchandrakarnati4615]

We can use logistics regression right based upon cdf ?

[@ibrahimahmethan586]

thank u so much . god bless u

[@nehasaroha2505]

Very informative video....can you suggest some simpler kaggle datasets on which we can perform EDA using PDF,CDF and multivariate analysis. I have already done on iris, Titanic and Haberman's dataset, but was thinking about getting more practice.

[@muhammadyasirbutt3631]

hello pretty friend

[@menakask6050]

Hi krish, kindly let me know pls explain how do you say at point 130 in X axis with 90% in distribution in Y axis is "less than" since the CDF is straight it is increasing and you are mentioning less than 130kg is there in 90% of the dataset. How do you predict it is less or high using CDF?

[@MScFabianoBriao]

Buenos! Do you have videos of real cases showing inferential statistics to test (validate) models?

[@rakhijha8911]

Simply amazing i read so many articles on cdf but everyone was calculating the value no one explained it so well can I plzz connect you on LinkedIn

[@lokeshchoraria6559]

best explanation

[@prachetade3163]

Hi Krish, I think you meant to say that cdf is the value of integration of the pdf uptil a point x and not a summation of pdf as you explained at 4.47. Great videos btw, really appreciate your efforts.

[@amolkabugade3728]

integration is actually addition of those values

[@amitmaurya6179]

If you are smoothening the curve, why count changes to percentage of distribution.

[@গোলামমোস্তফা-শ৮থ]

But that area is not 1 according to your curve. Because if the interval is 10 and and suppose height of any bar is 0.25 then 10*0.25 > 1. May be The concept exactly came from CDF function and in pdf the values are called likelihood and these values are the slopes(tan@) of different points from cdf.

[@naveenreddy7421]

Sir it is clear but please make a video on same concept in jupyter note book

[@Thanusree234]

Sir is this subject exploratory data analysis and statistics subject the same sir please reply 🙏

[@rakeshenjapuri3143]

how will calculate the above 60% in pdf and how will take the cumulative percentage in cdf give with mathematical explanation sir

[@bavalpreetsingh4664]

do make video on all the distributions

[@pushkarsaini7653]

sir may u please make video on hypothesis.

[@karthikvijayasarathi89]

Small correction - Probability cannot go over 1 , but probability density function can go. Correct me if I am wrong

[@wealth_developer_researcher]

Amazing :)

[@ratulghosh3849]

Good going Sir keep up the good work :)

[@ankitchakraborty1126]

Hi sir. Thanks for sharing awsome content like this. I have 1 question. Can we calculate percentile and median from CDF?

[@SandeepGurjar-ko5ju]

Hi, nice video but I think while you were explaining PDF you were actually talking about CDF.

[@Nikhil-jj7xf]

Krish pls provide you're online course registration link

[@RishikeshGangaDarshan]

If data is not in form of gaussian distribution then the pdf or cdf will work or not

[@seriouscoder1727]

Cdf the chart that apple used in theiŕ comercial to show sales, Lying with statistic

[@ujjwalmv9697]

what if cdf over a period of time is not being constant and hits 90 degree and then goes constant, why is that straightline coming in cdf?

[@adityapathania3618]

dont you think the cumulative total will go above 1? at 3rd 4th value itself ?as the probabilities are getting added ?

[@anto1756]

Nice 😁 could u do a comparison about survival function, inf and all other methods please

[@lahari1512]

hai krish , ur vedios are really helping me to learn machine learning very easily , can u please upload svm and xg boost vedios please its a request

[@snehalhon]

Krish sor plz send me playlist link for this tutorial series

[@saurabhtripathi62]

please add a video that clears how to do this with python , practically

[@shreyasaxena5169]

sns.distplot(df['weight])

[@0505Arjun]

What scenarios we will use CDF and PDF in machine learning..?

[@gauravsaini728]

I have the same query. Can you please give any domain specific example and show is how PDF and CDF curves will help the data scientist to take certain decisions..

[@alphonseinbaraj7602]

yeah ..same query me too

[@rajatchaturvedi7379]

Don't know its application in ML yet, Since I have started learning recently but one use of it is in EDA Which is the exploratory data analysis.Exploratory means you don't know anything about the data set from before. Your task is to extract some basic yet critical information about the dataset before implementing ML algorithms.Its important to get an idea about the dataset .PDF, CDF various plots such as 2D, pair plot , etc are some aspects of EDA.There are other stuff also.You can google it to understand more .

[@umesh789s]

can you please explain about T-score and T distribution

[@dude5697]

In 3.05, you shade the area even outside of the bell curve, but in 3.31 you shade the area only within the bell curve. I can't understand , Pls can you explain me Krish sir?

[@pravalikamucherla139]

Hi You have used only weight one feature to determine pdf n CDf can we do with two or more features

[@PriyaAmar848]

Great question, expecting answer from any DS enthusiast

[@kantafcb1]

no, its univariate analysis

[@ravitanwar9537]

amazing as always

[@enchanted_swiftie]

But sir, when I plot KDE plots with seaborn, I often get the values on the y-axis more than 1. What the interpretation will be then? Or KDE plots are different from Density Curves?