R - Association Rules - Market Basket Analysis (part 1) 

I like the videos because the space is right and clarity is there and steps are well explained. person like me who is studying after 15 years has really found your videos very useful. keep up the good work

Great video, and still relevant all these years later.....

thank you very much! people like you make the youtube so practical and so so productive!!!!!

I have been going through your videos and I can really connect with the way you explain concepts. So, I wanted to take a moment to thank you for taking out time and making these videos. Kudos to you! :)

Thank you very much for your video. You have such a nice lecturing speech and make it seems easier than it really is. :)

Great lesson! in your video you mention that there are no "Brand" or "Category" information attached to the items just to make the example easy to understand. I wanna ask if there any tutorials about doing multi-level association rules? I need to generate rules by item brand or item category

It'a very valuable video for rule learning in R!

Great video. Why are you not making any new video on R? Will you please reply if you will make any video on R or not? Thank you.

Thanks a lot. To the point explanation. Looking for more videos on other areas of data science.

great simple and easy to understand. thnx

how did you calculate the value of the density?there is a formule of it?

Great video thank you so much

I have a csv with two columns with headers ID and Product as simple as that, but I just can't get it to work with arules. How should it be the read.transactions statement? > txn

Hi, I got some questions. Do we need to the quantity of each item in a transaction? For example, if transaction 1 has two bottles of milk, do we need to record sth like this is the row and column stucture: Transaction1: Milk, Milk?

I want to use the arules package of r for market basket analysis. The issue is I do not have data for each transaction. I have daily aggregated data. So I am thinking of comparing quantity sold for each product. How do I input this magnitude of quantity sold and use arules with it?

Awesome. Very well explained.

I have challenges in applying with data where the transactions are not given in one raw, but the information is given in two columns where the first colum contains an invoice number and the next column gives the item. There can be the same invoice number for different items but I am having challenges getting rules for that file

Thanks for the course. I am wondering where I can download the data you used in the lectures.

I liked the simple example...great.

You said you will put where we can get the CSV but i can not see under your lecture

would you mind to share the data transaction please? or where i can download the data for simulation?

This was really helpful video. Thank you :)

if the density is low, that means we have a lot of empty data entry. then why do we still use this dataset for analysis? seems like the dataset with low density wont provide ample info for analysis. thanks for replying in advance.

I am facing problems in downloading the arules package. The error message: package ‘arules’ is not available (for R version 3.3.1) What should I do? Thank you!!

why does your pencil have a tail??

good stuff, appreciate it!

groceries file is in notepad but whiles loading in R groceries.csv is used. Won't it be groceries.txt after read.transactions

When I am loading the csv/txt file (read.transactions("tst1.csv",sep=",")) seems to be all right. But then when I do the summary I get this result: transactions as itemMatrix in sparse format with 63 rows (elements/itemsets/transactions) and 41 columns (items) and a density of 0.02439024 most frequent items: LearnDoing;;;; DiscHrsWrk;;;; DisChangTsks;DiscPcWrk;;; 5 4 3 DiscPcWrk;DiscHrsWrk;;; DisChangTsks;;;; (Other) 3 2 46 So, it is not reading my file as expected. Does anyone know what I should do? Thanks!

where I can find dataset ?

where's the groceries.csv file ??

where can I find the dataset?

A little bit extra slow explanations makes it a little bit annoying to watch