That was an outstanding explanation. I understand more from this video than all the others I have watched put together. Well done!
@mapa5000 Жыл бұрын
What a simple and understandable explanation for a deep topic. Thank you so much !!! You are a great instructor
@taniakuruwitage60403 ай бұрын
Thank you for the clear explanation...you are the best.
@pavankallem90415 жыл бұрын
thank u so much sir great explanation sir
@gustavo239914 жыл бұрын
Great explanation. Thanks very much.
@sujiththiyagarajan42903 жыл бұрын
Great vedio.... Thanks a lot sit....
@Mahmoud-ys1kt Жыл бұрын
Great info. Thanks a lot
@probotechnologies79567 ай бұрын
Super teaching
@agrinbestoon72415 ай бұрын
Genius
@otienokevin4990 Жыл бұрын
So powerful data mining skills
@sarahsamarrae43608 ай бұрын
Can you keep making videos, please! Why have you stopped?! You are so great lecturer.
@itv5610 Жыл бұрын
Most of the best tutorials look similar to this video. hehe. I can just feel that it's a good video before even starting.
@top5things9682 жыл бұрын
Thanks Dave .
@manuelherrerahipnotista85863 жыл бұрын
This is so clear explained that it is almost unreal hahahaha thank you very much for such valuable video
@classofchandansir34063 жыл бұрын
Clearly explained
@missantrafalgar7823 жыл бұрын
You are one in a million
@nowxdi7524 Жыл бұрын
Can you please tell me what is the best data mining process methodology (CRISP-DM, SEMMA, KDD) that can be used in heart attack prediction and why?
@tenacious16214 жыл бұрын
Thank you
@adiflorense14773 жыл бұрын
6:59 Sir, In classification, the input label / class is first changed to numeric before being added to the model?
@azertpoiu71053 жыл бұрын
If you're talking about "gender" - "age" or "student" ,then i assume they are not. The algorithm doesn't know and doesn't care what they mean since it doesn't need that info to process. If you're talking about the datas themselves then probably yes (fair : 0, good : 1, excellent : 2)
@massimehrkhah6075 жыл бұрын
thanks
@adiflorense14773 жыл бұрын
4:05 Sir, why are gender and student attributes not used in making predictions?
@brainiakuniversity3 жыл бұрын
Dude, go away. You may think you’re helping or showing your expansive knowledge but you’re just conflating and it’s not useful. Anyone with a brain knows you can substitute nominal for numeric if you want to but the example is useful in grasping the concept. Just like instead of using nominal values for credit it could be broken down into actual fico scores. We get it, you’re smart. Let it go. Or go make your own tutorial.
@adiflorense14773 жыл бұрын
@@brainiakuniversity thank you sir
@PrasanNavi Жыл бұрын
Getting Khan Academy vibes.
@plywoodcanadian Жыл бұрын
I think that's all too Sporadically laid out.
@aholmes13973 жыл бұрын
I dont get it , so if they are 65 they automatically go to the credit rating bubble ? Why? why not the student bubble? BEcause they more than likely wont be a student? So if theyre 35-65 , theyre automatically going to buy a computer? Why is age on the top why not student or credit rating? None of this makes sense , not enough explanation.,...
@JerseySlayer3 жыл бұрын
The sample data is only 5 instances. The decision tree might seem dumb, but it's because there isn't much information to work with. Out of the five people, anyone over 65 would not be a student. The people 35-65 all had a computer. The more instances (people) you have, the more accurate your analysis can be.