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

Here is a video which helps you understand which machine learning algorithm you should use for your use case.
You can buy my book of finance with ML
#Whichalgorithmtochooseforyourproblem
amazon url : www.amazon.in/...

[@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

[@pravinkumar54]

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

[@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.

[@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

[@anandacharya9919]

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

[@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......

[@akashchoudhuri3932]

Best video till now. Thank you Krish.

[@nokiyem521]

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

[@ankitaB11]

Most important video. Explanation is just amazing.

[@gargidixit5651]

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

[@sambhavmishra1873]

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

[@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?

[@amoghjain]

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

[@nagupranav]

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

[@vikasbohra3253]

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

[@rohinikalamkar5736]

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

[@sahinur_zmn]

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

[@MrDeepinaction]

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

[@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🙏

[@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.

[@harshinimedha7807]

The best explanation I have ever seen ✨

[@thongtech1984]

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

[@chrissanjiv1753]

good explanation Krish, Very crisp and clear

[@saunitmarolia3901]

Thank you Krish! All doubts cleared now.

[@swetapatra]

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

[@itybahadur3625]

Excellent video for an initial understanding! Thanks!

[@aanalpatel5361]

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

[@neelammishra5622]

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

[@sankarapandian.selvaraj]

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

[@faisalghazanfar169]

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

[@himanshubisht9643]

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

[@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?

[@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🙏👍👍

[@ranjaniselvaraj6475]

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

[@Vaishali_Wankhede]

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

[@souravbiswas6892]

awesome work Krish, a big thumbs up

[@abhinav02111987]

You saved my day Krish. Thank you.

[@khalfanalkharusi6577]

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

[@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.

[@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.

[@yogitapeshane6934]

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

[@chandramoulireddy9636]

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

[@mansiparikh8357]

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

[@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.

[@amirhosseinrahimi3964]

Thanks a lot for such a great explanation 😊

[@leoprinceznirp39]

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

[@devmaharaj4640]

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

[@navya-s3v]

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

[@olajideoluwasanmi4260]

You are the best Krish. Thanks for this.

[@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) ?

[@KetanChaudharyTHE-GREAT-KETAN]

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

[@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.

[@snehamonga2004]

You are the best teacher

[@learbasicsofpythonwithus-pe1kk]

I never saw a video like this description...

[@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

[@Sunnysinghbhatia]

This was very much helpful Thank you Krish

[@karldtrumpeter]

Thanks so much Krish. This knowledge is pricelss.

[@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

[@Rajkumar-vc2pg]

You are my true datscience Guru 🙏🙏

[@ninjaduck3534]

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

[@saichoz]

Great Vid. You made things clear. Thanks

[@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

[@gouravgangani]

Thanks Krish for such a nice explanation.

[@cseswadhinpanda]

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

[@syedtasleem2827]

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

[@anujasebastian8034]

Great insight!!! Thanks Krish!

[@connectsantech]

Very nicely elaborated !!! Thanks.

[@KaranSingh5]

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

[@gangaholi8600]

Very nice presentation.. thank you..

[@aksharasiri5195]

It's very helpful . Thank you soo much❤😊

[@AbhishekBalsara]

Great video, really cleared the concept 👏👏

[@anupambarasia7865]

very well explained thankyou so much sir

[@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

[@minime8745]

Shouldn’t we scale the features before plotting?

[@pratiknaikwade95]

Very useful video krish sir🥰🥰

[@salihsarii]

This is amazing. Thank you Krish :)

[@pietart3596]

Thanks for everything you do, your words are very motivating

[@Amansingh-tr1cf]

U made my day Krish sir

[@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

[@RaushanKumar-qb3de]

Thanks. I'm in need of this video

[@MarioLaudazi]

Thanks for the useful videos you post.

[@folashadeolaitan6222]

Thank you for this great insight!

[@kunikakhobragade6953]

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

[@asankab.l.5166]

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

[@ainju1]

Informative. Thank you so much

[@neon91111]

great video man .. luved it..

[@mithunkumar7063]

what if there are more number of categories..the graph takes time and will it be useful then???

[@fit_shaha]

Same ques....mere bhi mind m that Kon si algo...suru kru bt phir Maine ek tutorial ko follow kiya and dekho decision tree ko hi Laker 5 days ho chuke hai bt uska code m error show ho rhi hai

[@vineethp8925]

Superb Explanation can you please do on regression

[@shreyjain6447]

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

[@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

[@girish-kr]

Awesome Krish Sir

[@monkeyk8076]

start @ 1:23

[@sithalrao]

Hi Krish, if we have more numbers of features say 30, than in that case we wont be able to draw pair plot. What should be our approach in that scenario? Many thanks for your wonderful videos :)

[@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

[@shreyasb.s3819]

Can I use all kinds of all algorithms and choose which one give more accuracy? Is there any problem in this solution.?

[@manishsharma2211]

There is no problem, but it takes lot of time

[@muhammadzeerakkhan6300]

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

[@imteyazahmad9616]

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