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Data Science - Part VI - Market Basket and Product Recommendation Engines

178 ratings | 30098 views
For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This lecture provides an overview of association analysis, which includes topics such as market basket analysis and product recommendation engines. The first practical example centers around analyzing supermarket retailer product receipts and the second example touches upon the use of the association rules in the political arena.
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Text Comments (26)
Sebastian Nielsen (1 month ago)
Speak up dude
Byron Olsen (5 months ago)
Thanks for putting this video up Derek. It answered everything I wanted to know and gave me great real world examples. I'm pumped to check out the rest of the series.
Raine Goin (7 months ago)
Hi! Could you please explain how did you get the 0.26 and 0.46? Hope you'll respond. Thanks
Manjunath Swamy (8 months ago)
How did you arrive at 0.26 of XUY??
Alla Demuth (10 months ago)
Aditya Tirtatjahja (1 year ago)
Nice vid. Could you explain how do you come up with 0.26 in the confidence analysis and 0.46 in lit analysis?
HerdingDogRescuer (1 year ago)
Thank God this is not in Hindi or with a thick Hindi accent!
Mardison Purba (4 months ago)
Hahaha.. agree with you
harshavardhan reddy (1 year ago)
It looks like the formula for Confidence and lift is wrong and so are the calculations (10:16). Please check I think it should be more like support(X ∩ Y)/ Support (X)
91drox (1 month ago)
The formula is correct. X and Y don't have anything in common. (X can be {Milk, Butter} and Y can be {Cereal}). Here, we try to find the probability of X and Y occurring, when only X occurs. ( X -> Y).
Bhavani Chatrathi (1 year ago)
Hi Derek, Nice Explanation, can you please give me your mail address so that i will send you an email to access datasets and r codes...Thank You
great video
Albert Sargsyan (1 year ago)
quite simple and helpful !
Yuyuan Pan (1 year ago)
thank you this is very useful!
Kush Gupta (1 year ago)
Thank you very much Derek, for this fundamental lesson.
Manjunath Swamy (8 months ago)
Kush Gupta : Could you please help me understand how X U Y = 0.26 ???
Grigory Rybalchenko (2 years ago)
Never saw more value added info in such concise way. Great thanks for your contribution!
Gururaj Cv (2 years ago)
Very useful. Thank you for sharing
Ramakanth Rayanchi (2 years ago)
Excellent video. Thank you
Sarnjit Beesla (2 years ago)
Excellent video. Complex ideas presented in a simple and easy to understand manner, look forward to watching the rest of this series.
Rakesh Sancheti (2 years ago)
very useful and well explained.. look forward to go through few other tutorials as well..
Derek Kane (2 years ago)
+Rakesh Sancheti Thank you for taking the time to watch the lecture and for the kind words. I appreciate this very much. Good luck and hope that you enjoy the other lectures too.
Adam Zíka (3 years ago)
Thank you for the video!
Derek Kane (3 years ago)
+Adam Zíka Thanks and I am glad that you enjoyed this one.
Rishi Chauhan (3 years ago)
Great job, Derek. Thanks for sharing!
Derek Kane (2 years ago)
+Rishi Chauhan Thank you for your kind words and I am glad that you are finding some value here too. Carpe Diem

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