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Hi Edureka, thank you for the tutorial very helpful. I am trying this with my dataset and when running the last part of the code I am getting the following error: AttributeError: 'generator' object has no attribute 'shape' and I am not able to see the resulting apriori table. Can you please help with this.
Thank you for explaining this so well! I had been assigned another video in my course which didnt make any sense so I was searching for something better - and I found it! Thank you so much :) Keep up the great work
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Is there a reason we are ignoring valid 2-itemsets while creating the association rules since those could get us new rules that support the required confidence?
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It was such a nice and informative video above all Association rules along with Apriori Algorithm. It would be perfect if you could please share me the Python code for more understanding. Pl reply. Thankyou! :-)
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Lift(A -> B) refers to the increase in the ratio of sale of B when A is sold. Lift(A -> B) can be calculated by dividing Confidence(A -> B) divided by Support(B).
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Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Edureka Python Machine Learning Course curriculum, Visit our Website: bit.ly/2FBUtO7
this is by far one of the best explanations for Apriori algorithm i have come across online. perfect guys. keep up the great job.
Very well explained, Edureka is one of the best educative youtube channels, I love you guys, thank you
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Thank you very much. One question: why is the minimum confidence value 60%?
Apriori algorithm actually begins at 7:45. TML
Thanks for the explanation on Association Rule Mining using the the Apriory algorithm
Thanks your great explanation ❤.. love from sri lanka 🇱🇰
great explaination..thank you Edureka ❤❤
This was really great
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Awesome voice which associate the concept to understand,learn and implement quickly.
Thank you so much 👍
You’re welcome 😊 Keep learning with us..
Totally loved it
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very, very nicely articulated. Great video
Thank You 😊 Glad it was helpful!!!
Hi Edureka, thank you for the tutorial very helpful. I am trying this with my dataset and when running the last part of the code I am getting the following error: AttributeError: 'generator' object has no attribute 'shape' and I am not able to see the resulting apriori table. Can you please help with this.
Nice❤
very well explained!
Thank you for explaining this so well! I had been assigned another video in my course which didnt make any sense so I was searching for something better - and I found it! Thank you so much :) Keep up the great work
We are super happy that Edureka is helping you learn better. Your support means a lot to us and it motivated us to create even better learning content and courses experience for you . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Thank you
You're welcome 😊 Keep learning with us...
Is there a reason we are ignoring valid 2-itemsets while creating the association rules since those could get us new rules that support the required confidence?
Great explanation! Thanks Edureka.
Thanks for watching! subscribe for more such videos
Thanks for the articulate explaination!
Great videao!
how do I choose the min_support value?
Really helpful! Can you please share the python code and dataset for the same.
Hi Muskan, kindly drop in your email id to help us assist you with the required data sets .Cheers :)
By far , best explanation
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thank you very much
Very Good Explanation
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Nice video. Can I get the dataset and python file?
Hi Sagar, kindly mention your email id in the comments below to help us assist you with the required set of source codes.
Very clear explanation ! Thanks !
Thanks for the video it's so helpful
Please could you help me with the dataset?
Please mention your email id (it will not be published). We will forward the dataset to your email address.
It was such a nice and informative video above all Association rules along with Apriori Algorithm. It would be perfect if you could please share me the Python code for more understanding. Pl reply. Thankyou! :-)
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thanks
please tell the dataset you have used in this video
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Good explanation
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Superb explanation
superb lesson
can I please have access to the dataset?
Yes kindly drop your mail id
Great ! Thank you Edureka
Well explained
Many thanks, it is very helpful, pls can I get the note book file and the dataset?
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Thanks a lot. Nice explanation
can you kindly provide me with the pdf you have used.
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Kindly explain about Lift
Lift(A -> B) refers to the increase in the ratio of sale of B when A is sold. Lift(A -> B) can be calculated by dividing Confidence(A -> B) divided by Support(B).
How can i get the code and excel file he used? anyone help Thanks
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can I get the dataset and .ipynb file?
Please share your email id with us (it will not be published). We will forward the dataset to your email address.
I just love your accent. very soothing. thanks for the video
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can i get dataset for this code
Hi Sri Teja, kindly drop in your email id to help us assist you with the required data sets, cheers :)
Can I get the dataset and .ipynb file?
Hi Vatsal, kindly specify your email id with the comment, to help us provide you the required datasets, cheers :)
Can I get the dataset and .ipynb file?
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can I get the dataset and .ipynb file?
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can i get the dataset and .ipynb file?
Good to know our contents and videos are helping you learn better . We are glad to have you with us ! Please share your mail id to send the data sheets to help you learn better :) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )