Data Mining Fundamentals

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Data Mining Fundamentals

Рет қаралды 65,448

Dave Sullivan

6 жыл бұрын

Пікірлер: 29

@billford9863 4 жыл бұрын

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

@taniakuruwitage6040 3 ай бұрын

Thank you for the clear explanation...you are the best.

@pavankallem9041 5 жыл бұрын

thank u so much sir great explanation sir

@gustavo23991 4 жыл бұрын

Great explanation. Thanks very much.

@sujiththiyagarajan4290 3 жыл бұрын

Great vedio.... Thanks a lot sit....

@Mahmoud-ys1kt Жыл бұрын

Great info. Thanks a lot

@probotechnologies7956 7 ай бұрын

Super teaching

@agrinbestoon7241 5 ай бұрын

Genius

@otienokevin4990 Жыл бұрын

So powerful data mining skills

@sarahsamarrae4360 8 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.

@top5things968 2 жыл бұрын

Thanks Dave .

@manuelherrerahipnotista8586 3 жыл бұрын

This is so clear explained that it is almost unreal hahahaha thank you very much for such valuable video

@classofchandansir3406 3 жыл бұрын

Clearly explained

@missantrafalgar782 3 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?

@tenacious1621 4 жыл бұрын

Thank you

@adiflorense1477 3 жыл бұрын

6:59 Sir, In classification, the input label / class is first changed to numeric before being added to the model?

@azertpoiu7105 3 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)

@massimehrkhah607 5 жыл бұрын

thanks

@adiflorense1477 3 жыл бұрын

4:05 Sir, why are gender and student attributes not used in making predictions?

@brainiakuniversity 3 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.

@adiflorense1477 3 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.

@aholmes1397 3 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.,...

@JerseySlayer 3 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.