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Data Mining Fundamentals

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So powerful data mining skills Comment from : OTIENO KEVIN

Getting Khan Academy vibes Comment from : Prasan Na

Great info Thanks a lot Comment from : Mah moud

I think that's all too Sporadically laid out Comment from : Sean Morry

Most of the best tutorials look similar to this video hehe I can just feel that it's a good video before even starting Comment from : IT V

What a simple and understandable explanation for a deep topic Thank you so much !!! You are a great instructor Comment from : Marco Portillo

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? Comment from : Nowxdi 7

Thanks Dave Comment from : TOP 5 THINGS

What a lucid explanation Thanks for this! Comment from : AarthiA

You are one in a million Comment from : Missan Trafalgar

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, Comment from : A Holmes

Great vediobrThanks a lot sit Comment from : sujith thiyagarajan

Clearly explained Comment from : Class of Chandan Sir

This is so clear explained that it is almost unreal hahahaha thank you very much for such valuable video Comment from : Manuel Herrera Hipnotista

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

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

Great explanation Thanks very much Comment from : Gustavo Capuan

Thank you Comment from : Tenacious

That was an outstanding explanation I understand more from this video than all the others I have watched put together Well done! Comment from : Bill Ford

thanks Comment from : Massi Mehrkhah

thank u so much sir great explanation sir Comment from : Pavan Kallem

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