Machine Learning Algorithm- Which one to choose for your Problem?

[@nandinidasgupta7781]

This is the only channel i guess who understands what students are expecting to know other than traditional algorithm knowledge.. Thank You so much..

[@hunainaghai3342]

Which algorithm did u choose, Nandini?

[@srikanththecoolhunk]

You have no idea how useful all your videos were to help me find a job. I cannot thank you enough Sir, please post more.

[@varunsagartheegala]

Your videos is a gift to all data science aspirants like me and working professionals too. Thank you for making our learning easy and fun. Please don't stop

[@lisamathur9206]

Ssly this is exactly what I've been wondering from so many days. Weeks actually. But didn't find even 1 proper Video on this . Thanks a lot......

[@harshj84]

Hi Krish, I really enjoyed your videos. The specialty of your videos are they fill the gap between theory and practical. I have watched tones of other videos, most of them are telling "how to plot the graph" but no one is explaining "how to leverage information from these plots?" Just like this video, we need to use knn as most of the points are overlapping. I would really love to such a great video for other algorithms as well. Not only me, but I believe your subscribers would also love them. Tons of thanks to you.

[@anandacharya9919]

This is your best and most important video of all. Thank you 🙏

[@chravi511]

Hi Krish, I've been following your channel for most of my learning. The way you explain the things gives a very familiar approach and giving out what is needed in the way everyone can understand. This let me watch any video of yours till the end and learn completely. Happy to learn from you. Thanks

[@deepuraju2456]

Hi sir , can u give any idea problem statement for loan approval prediction

[@puneettiwari2251]

Great session, Please make an elaborative video on this topic covering all the pros and cons of different algorithms and if possible with codes. Thanks for this session again sir🙏👍👍

[@nokiyem521]

Great, this is the video i was looking for to explain the difference from a basic mathematical perspective.

[@pravinkumar54]

@Krish Naik 11:30 if you double pick the pairplot in jupyter notebook. It zooms in

[@sambhavmishra1873]

Understood it very very clearly ❤ whatever doubts that were arising was getting explained in the next second.

[@akashchoudhuri3932]

Best video till now. Thank you Krish.

[@gargidixit5651]

Amazingly Explained, I have never got any satisfactory answer for this question Thank you so much for such knowledge sharing :)

[@ankitaB11]

Most important video. Explanation is just amazing.

[@amoghjain]

Such a practical channel with real world applications! Thankssss!!

[@rohinikalamkar5736]

This is what is required .. thank you so much for sharing this 👍👍 great work

[@nagupranav]

you are always great man.. anyone can understand by watching your video.

[@neelammishra5622]

I really found the answer of my most confusing question into so simplify terms. Thanks sir

[@navya-s3v]

Selecting the appropriate machine learning algorithm is determined by aspects such as problem type, data qualities, and desired results.

[@sahinur_zmn]

just awesome 💚💚💚i am just recharged after completing this video..thanks a lot.

[@izzycrux48]

Thank you Krish for the amount of effort and insights you put into your videos. Really helps a lot🙏❤️👍 May God bless you and keep you well🙏

[@vikasbohra3253]

You are AWESOME Krish not only from knowledge's point of view , but also in explaining the concepts in pretty well manner. Thanks.

[@himanshubisht9643]

Here I can understand how to choose the best algorithms to my dataset thanx sir👍👍

[@khalfanalkharusi6577]

Thank you for your contribution. Please note also that classification learner app in Matlab provides you such pair plots

[@learbasicsofpythonwithus-pe1kk]

I never saw a video like this description...

[@saunitmarolia3901]

Thank you Krish! All doubts cleared now.

[@chrissanjiv1753]

good explanation Krish, Very crisp and clear

[@chravi511]

Please Explain me, Scenario 1 : All Categorical, ordinal, nominal features - Categorical target Scenario 2 : All continuous features - Categorical target Scenario 3 : Combination of categorical and Continuous features - Categorical target Which model to use in these scenarios (particularly SCENARIO 1) ?

[@translatethis7765]

The thing is, you are looking at the pair-plot for only two dimensions. Although everything is overlapped when projected into pairwise space, it doesn't necessarily mean the data is not linear separatable right?

[@V_Wankhede]

Informative video , its all jam of each algorithm that you taught. Thank you for brief explanation. 👍

[@timetimetime3985]

If you want to predict the future or get the information for future use regression if you want to distinguish the data from other data use KNN, Random Forest etc, that collect BIAS

[@harshinimedha7807]

The best explanation I have ever seen ✨

[@aanalpatel5361]

Most of my questions were answered through this video!! Thank you a ton, sir!!

[@thongtech1984]

thanks a lot for this wonderful explanation, Krishna. You are my new hero now :)

[@chandramoulireddy9636]

Naik sir, thanks for giveing inforamtion about selecting algor,,. i am very happy to follow ur channels. thanks.

[@yogitapeshane6934]

It was really good video.. Please make more videos in DataScience...

[@sankarapandian.selvaraj]

All of your videos have a lot of useful information. Thank you...

[@satriogani3105]

In the pairwise plot, the points are only plotted on two dimensions. However, our data have dimensions more than two. This means that we do not plot the other features. It looks that we do some projection from higher dimensional space to two dimensional space. Can we conclude if the groups overlap with each other only by looking on two dimensional plots? Simple example, if we project the earth on two dimensional space, we can see that the norther and southern hemisphere will overlap on each other.

[@translatethis7765]

I have been looking for comments like this... I agree with you. I don't think one can conclude whether the data is linear separable, from just looking at pairwise plots.

[@itybahadur3625]

Excellent video for an initial understanding! Thanks!

[@swetapatra]

ok, so ideally, we decide on the algorithm based on the charts we have plotted?

[@MrDeepinaction]

Great video, friend!! Keep up the good work. Need to learn a lot through you.

[@anandhasrivi]

Best way is to try logistic regression right away and result is poor. Then we can conclude data is not linearly seperable. Knn will not be suitable for larger dimension data due to curse of dimensionality. I expected more from this vedio but vedio is very simple

[@faisalghazanfar169]

excellent explanation...really an easiest way to understand and clear confusion..:)

[@leoprinceznirp39]

Thank you so much. Your explanation gives good clarity. Great work. Thank you😃

[@chinmayjape4981]

Please make a video on how a beginner can participate in Kaggle competitions.

[@ritwickjoshi]

brother you need to dive in to find out Dont worry about the result see where you lack and work from it but remember not to go down from your baseline

[@mansiparikh8357]

You have explained in a great manner. Please make more such videos for data science. Very helpful

[@somalkant6452]

hi krish, it was an awesome video. just a doubt popping up in my mind, when there is overlap of datapoints, why cant we use SVM, bacause that will also take the points (of different classes) to other dimensions and divide the points using hyperplane. please correct me if my understanding is not correct. Than you so much for your awesome videos.

[@souravbiswas6892]

awesome work Krish, a big thumbs up

[@kamran_desu]

Hey Krish, great video - just shows how important it is to understand the geometry/maths behind ML. In regards to using KNN here for overlapping data points, would SVM (radial/polynomial) be a good choice as well, since it will use the kernel trick to apply a non-linear classifier in a higher dimension? And how would SVM compare to the tree methods in terms of computation complexity?

[@adityakishan589]

Same doubt. Krish please help us get clarity on this.

[@karandua6564]

Even logistic regression may work, he is just comparing two dimensions at a time, they may be linearly separable in higher dimensions .

[@danielschulz5447]

@@karandua6564 no, I think that is not true. LR will not work good, if single plots are pairwise highly overlapped and/or not dividible into straight lines.

[@abhinav02111987]

You saved my day Krish. Thank you.

[@devmaharaj4640]

One of best video's I have seen , won't forget to return u the favour....

[@amirhosseinrahimi3964]

Thanks a lot for such a great explanation 😊

[@kunikakhobragade6953]

great sir ... can u plz make a video on the comparison of every ML algorithm

[@olajideoluwasanmi4260]

You are the best Krish. Thanks for this.

[@Rajkumar-vc2pg]

You are my true datscience Guru 🙏🙏

[@karldtrumpeter]

Thanks so much Krish. This knowledge is pricelss.

[@KetanChaudharyTHE-GREAT-KETAN]

Thanks a lot Krish......it's really very helpful ......

[@vijaynale7893]

Nice ... Please make more videos for data science, beacuse it better to understand into that way...... Thank you Also make video for data cleaning, Solve any business problem

[@anupambarasia7865]

very well explained thankyou so much sir

[@ranjaniselvaraj6475]

Good Job! Clearly explained. Thank you so much for this video.

[@Sunnysinghbhatia]

This was very much helpful Thank you Krish

[@girish-kr]

Awesome Krish Sir

[@cseswadhinpanda]

Nice explanation , you explained us like teaching a parrot love you from india .

[@gangaholi8600]

Very nice presentation.. thank you..

[@KaranSingh5]

Thank you so much for this video! This solved such big confusion for me!

[@theindianrover2007]

Liked this video, but in case of large test set KNN might prove you costly at the time of prediction

[@MyTube6686]

how ? can you explain further Please

[@rajivturkar1671]

Great explaination Krishna.. I would like to know how pairplot will show categorical variable and how we will do the feature engineering for categorical variable?

[@sejalchandra2114]

Yes, this is my doubt also. Please help!!

[@manikaransingh3234]

@@sejalchandra2114 You first have to do the label encoding to your categorical features. and then use the pairplots.

[@Amansingh-tr1cf]

U made my day Krish sir

[@ainju1]

Informative. Thank you so much

[@gouravgangani]

Thanks Krish for such a nice explanation.

[@vineethp8925]

Superb Explanation can you please do on regression

[@anujasebastian8034]

Great insight!!! Thanks Krish!

[@roshanajadhav5854]

You are doing amazing job Sir, I am too late to land on your channel Keep it up You are providing knowledge free of cost where people taking money for this in thousands No need to join any private classes with huge pay if you will provide this material in sequential order Great job all the best👍💯

[@fuck30093]

Have you found an answer to your question?

[@siddhantkaushal7669]

It is a good video. Also explained in detail to understand pair plots. :) Thanks.. My Question is: When should we decide to use SVM and Naive by visualizing and analyzing the pair plots? :)

[@datahat642]

@siddhant Naive Bayes works well with higher dimensionality (such as text corpuses) while support vectors whpith its kernel is applied when the data is non linear but you want to apply a linear classifier by transformation

[@asankab.l.5166]

Your book is out of stock, please provide a way to buy. Great tutorial!

[@imteyazahmad9616]

Sir please make more video related to classification and regression .I have to much confusion in regression also,when to select which algorithm .

[@RaushanKumar-qb3de]

Thanks. I'm in need of this video

[@PawanSingh-pf2bl]

Nice video. But I like the way you are presenting at whiteboard. It feels like we are in actual class.

[@saichoz]

Great Vid. You made things clear. Thanks

[@pratiknaikwade95]

Very useful video krish sir🥰🥰

[@pietart3596]

Thanks for everything you do, your words are very motivating

[@connectsantech]

Very nicely elaborated !!! Thanks.

[@MarioLaudazi]

Thanks for the useful videos you post.

[@salihsarii]

This is amazing. Thank you Krish :)

[@folashadeolaitan6222]

Thank you for this great insight!

[@shreyasb.s3819]

Very nice explanation

[@samlo4166]

Awesome Video! Thank you for sharing this. I have one question. When there is a lot of overlap, can I use SVM to solve this? thank you

[@monkeyk8076]

start @ 1:23

[@prernamistri]

wonderful video. thank you.

[@jigarshah1883]

We should give a try to SVN as well as using kernel we can understand the data after plotting and predict it nicely..

[@syedsuhailahmed2868]

Good Explaination.But logistic regression is not linear its segmoid or S shaped

[@Crazyanya_]

Sir thank you so much you solve my problem

[@ninjaduck3534]

This was so excellently explained, thank you so much!!

[@karandua6564]

At 13:30, you are making an assumption the data is not linearly separated based on just two dimensions. That's not correct, since the data can be linearly separable in multiple dimensions.

[@muhammadzeerakkhan6300]

Sir if you could also make a video on the approach to select algorithms for unsupervised learning.

[@shreyjain6447]

How do we decide which algorithm to use in case of Regression task?

[@syedtasleem2827]

Sir, What about SVM U didn't mention on what kind of Data it can be used after understanding pairplot,..

[@AbhishekBalsara]

Great video, really cleared the concept 👏👏

[@ketanbutte3497]

excellent video! thanks

[@arjungoud3450]

please teach about the selection of regression too with an example. Thank you