💥 Learning R programming is easy for Excel users! 💥 📺 ruclips.net/video/MNpsyjSuR20/видео.html 👩🔬👨🔬 Learn to use your basic Excel skills to analyze the business like a Facebook data scientist: 📺 ruclips.net/video/xIXymabyFIM/видео.html
I'm interviewing with one very soon. And i love Excel. This is very interesting stuff and gives me so much insight, confidence, and further learning focus to develop my skills and career!
This is incredible and I don't know why we didn't learn this in school (or even in grad school) but all the better to learn from you Dave! Thank you for sharing your knowledge!
I love how you solve this type of analysis with Excel cause you can see everything step-by-step. I read and followed an article to do it with R but I didn't know what was happening behind the algorithm and was pretty difficult to understand till now. Thanks Dave!
Thank you for the explanation! I’m planning on using python for my mba, but seeing you explain it using excel really helped me understand the concept in high level way
TBH I was a little skeptical to start a 20 min video on this but this was a very efficient way of explaining each and everything. I am really thankful to RUclips algorithm that I it showed on the top of my list. LIKED, SUBSCRIBED (for all notifications). also hyped to learn R with @david. Thanks Man! Happy new year!
Awesome stuff David. I’ve used countifs in excel to associate two different criterias. One main criteria with a table of others. Then I create another column of total frequencies with a final hit percentage. I used a data link to change the main criteria. Using solver I could dig even deeper. Maybe I’ll look at R to get a full count down from best to second best, etc.
Thank you for the feedback, much appreciated! Could you provide some more context on this? Are you referring to video context focused on the techniques/algorithms in the abstract? For example, in this video I don't cover the apriori algorithm that is a common market basket analysis implementation.
@@DaveOnData sure! I was trying to say that the videos that I enjoy the most are the ones that you provide a bit of context on a particular technique/algorithm e do a quick showcase in it`s application. Another example was the "Classification Tree Intuition" video. I really liked this Market Basket Analysis video in particular, because your formalized for me a type of analysis that I've done before, but wasn't even aware that it had a name. So it was particularly useful for me, to have a more formal grasp of the technique and be able to google further into it! Of course, it is just my opinion. I'll keep a look out for your future content either way! Keep it up! 🖤
Awesome, thanks for the clarification! I'm thinking you might like my latest video on K-means cluster analysis with Excel: ruclips.net/video/YuxwkchSAW4/видео.html
Hey David, Great video! I wanted to point out that you don't mention that the formula for the occurrences needs to be entered as an array and it doesn't show that in the video either. Took me a long time to figure this out and get the formulas to work correctly :/
David, excellent video, so logical and clear, as with your other videos. I see that you base it on binary values of 0 or 1 but what if the characterstics have multiple values such as a likert based scale or for example such as a person's age?
Thanks for a great video! I actually tried your model to analyze some data from my own company, and set it up with 4-ways lift and 5-ways lift. When trying the 5-ways lift I encountered a problem with the Solver, it actually picked the same decision variable twice. In the Titanic Dataset this could for example be "Female", "Firstclass", "Istriplet", "Female" and "Survived" - that is "female" is choocen twice. Do you happen to know how we can make a constraint that ensure unique decision variables? Thanks in advance for any help.
Outstanding tutorial! You present very well and your method of teaching is excellent. I have used Python for amateur data analysis but I have not used R. Do you see a big difference between the two? Keep in mind I'm not a mathematician or computer science guy. 👍👍
Thanks, Ajay! Glad you liked the video. In a nutshell: Support - The percentage of transactions in the data with a particular itemset. For example, using the Titanic data, the percentage of passengers that were both female and traveling in 1st class. Confidence - The probability that a transaction has the items on the left-hand side of the rule and also contains the item on the right-hand side of the rule. Take the following rule using the Titanic data: {female, 1st Class} => Survived The confidence is the probability of seeing females traveling in first class that survived.
Hi David, I am wondering if I can use this technique to segment the market for my thesis. In my survey, I have a lot of categorical variables such as the living area, income (in classes), education, etc. The purpose of my thesis is to research if there is a link between these categorical variables and the interest in the product that my thesis company is selling. Do you recommend using the market basket analysis for this or are there any other data analysis methods that will be suitable for this topic? Ps: Thanks for your very informative videos, I genuinely think your work will help me loads with my thesis.
Gosh Dave! You are just the best!! I wish you could be my mentor. Thanks so much for this. It would be really nice to learn how to do this with R. #TeamR
Thank you for the compliment and glad you liked the video. If you're interested in market basket analysis with R, check out this blog post on my website: 💻 www.daveondata.com/blog/titanic-market-basket-analysis-with-r/
This video is great but does anyone know how to organize raw data into a workable table? What do you do to organize the data if you have columns with variables in random order. Using the titanic example, what if your passenger data looks like this for thousands of rows: Passenger 1| single | survived | third class | adult Passenger 2| survived | child | with group | second class
Hi David, great tutorial. Q: how can you use the same concept for associations that are in decimals like 0.8, 0.4, 0, 1? and how can you highlight that significance is above 0.7 for instance? is that doable in excel?
Great question! Did you have a specific correlation technique in mind? I'm thinking that cosine similarity might be better between the vectors (e.g., moving from binary to "product" counts), but then there's the problem of handling all the vector combinations (e.g., a rule using 4 "products" on the LHS and 1 "product" on the RHS.
I Finally mangaed to duplicate this in Power BI. Nearly broke me but I feel like I've got superpowers now :) The formula for 3 way lift however is different from the traditional formula seen elsewhere (Number of A,B, C Occurences / (Total transactions * Fraction of A occurrences * Fraction of B occurrences * Fraction of C occurrences)). This Formula used here seems to be (Number of A,B, C Occurences / (Fraction of A Occurances * Fraction of B&C Occurances). Is there a reason for this? I know this is probably TLDR :D
Hi David, I'm looking at my transactional data where each row represents an order and the columns are the products (107 total columns). For each order (row), I have either a 1 (purchased) or 0 (did not purchase) in every cell. I went through your tutorial and it was great. But what I do not understand is that I do not have a RH column. If your example, you had RH as survived 1 or 0. But I do not have such a column as every row is a purchase so yes, every one purchased something. What am I to do?
why you didn't explain the formulas in this sheet and the concept behind them, for example the 2 way method formulas and table is not like the 3 way method
Great question! Unfortunately, that was an editorial call because I wanted to keep the video from getting too long. That and most folks on RUclips aren't interested in the math - they just want to get things done. I might follow up with a more detailed video on market basket analysis math later. If you're interested in the math right now, check out the following blog post: towardsdatascience.com/a-gentle-introduction-on-market-basket-analysis-association-rules-fa4b986a40ce
@@DaveOnData thanks and please make a video about market basket analysis math. you are one of the few content creators in data on RUclips that can explain quite well with easy example, I started following your posts on LinkedIn then I jumped on your channel here.
The Solver is an Add-in that is not enabled by default. Check out the following article from Microsoft: support.microsoft.com/en-us/office/load-the-solver-add-in-in-excel-612926fc-d53b-46b4-872c-e24772f078ca
![Market Basket Analysis [Association Analysis]](http://i.ytimg.com/vi/icGS26TS1fE/mqdefault.jpg)
💥 Learning R programming is easy for Excel users! 💥
📺 ruclips.net/video/MNpsyjSuR20/видео.html
👩🔬👨🔬 Learn to use your basic Excel skills to analyze the business like a Facebook data scientist:
📺 ruclips.net/video/xIXymabyFIM/видео.html
I’m a data analyst who just started working for a grocery store, so you can’t imagine how excited I am to watch this. Thank you thank you!!
I'm interviewing with one very soon. And i love Excel. This is very interesting stuff and gives me so much insight, confidence, and further learning focus to develop my skills and career!
Can i talk with you in private...I need your help with sth
@@nnajiobikachilo6807 no
Perfect timing to find this gem of a channel. Intro to Data mining & Intro to Business Analytics and Big Data finals are right around the corner.
This is incredible and I don't know why we didn't learn this in school (or even in grad school) but all the better to learn from you Dave! Thank you for sharing your knowledge!
Many Thanks for a great content!
I love how you solve this type of analysis with Excel cause you can see everything step-by-step. I read and followed an article to do it with R but I didn't know what was happening behind the algorithm and was pretty difficult to understand till now. Thanks Dave!
This is absolute gold. So clearly explained.
Thank you for the feedback! Glad you enjoyed the video.
Fantastic content Dave. Very simple, straight forward yet powerful demonstration.
Thank you for the explanation! I’m planning on using python for my mba, but seeing you explain it using excel really helped me understand the concept in high level way
You are welcome and so glad you found the content useful!
Really amazing explanation David beautiful work.
Wow! Thank you so much for the kind words. Glad to hear you enjoyed the video.
Really Great Example! Thx!!
Very useful for me as a Business Intelligence Analyst. Awesome teaching, thanks David
So glad you found the content useful! Be sure to check out some of my other tutorials (e.g., K-Means clustering). 😁
David, your content is awesome. Please put up more videos on machine learning.
great video dave! congrats on the quality work.
Thank you for the feedback, much appreciated! 🙏🙇♂️
Great video and explanation of how Market basket analysis works in excel. thank you for posting
My pleasure! Glad you found the video useful.
From Argentina... Awesome, absolutely awesome explanation, congratulations
Argentina!? Awesome! So glad you found the content useful.
TBH I was a little skeptical to start a 20 min video on this but this was a very efficient way of explaining each and everything. I am really thankful to RUclips algorithm that I it showed on the top of my list. LIKED, SUBSCRIBED (for all notifications). also hyped to learn R with @david.
Thanks Man! Happy new year!
Awesome stuff David. I’ve used countifs in excel to associate two different criterias. One main criteria with a table of others. Then I create another column of total frequencies with a final hit percentage. I used a data link to change the main criteria. Using solver I could dig even deeper. Maybe I’ll look at R to get a full count down from best to second best, etc.
Great explanation. Subscribed and going to watch all your excel videos Dave. Thanks so much for your efforts in making such great videos.
This is so awesome, best video on this tupic
Thank you for the kind words, very much appreciated! Glad you liked the video.
very awesome video great job bro!
Thank you! I'm glad you enjoyed the video, and I appreciate the kind words.
Hey, very nice video. I would love to see more videos focused on specific analysis techniques, rather than tools
Thank you for the feedback, much appreciated! Could you provide some more context on this?
Are you referring to video context focused on the techniques/algorithms in the abstract?
For example, in this video I don't cover the apriori algorithm that is a common market basket analysis implementation.
@@DaveOnData sure! I was trying to say that the videos that I enjoy the most are the ones that you provide a bit of context on a particular technique/algorithm e do a quick showcase in it`s application. Another example was the "Classification Tree Intuition" video.
I really liked this Market Basket Analysis video in particular, because your formalized for me a type of analysis that I've done before, but wasn't even aware that it had a name. So it was particularly useful for me, to have a more formal grasp of the technique and be able to google further into it!
Of course, it is just my opinion. I'll keep a look out for your future content either way! Keep it up! 🖤
Awesome, thanks for the clarification!
I'm thinking you might like my latest video on K-means cluster analysis with Excel: ruclips.net/video/YuxwkchSAW4/видео.html
Great video David
Thank you! Glad you liked the video.
Thank you very much for this content
Good content, liked it
Thank you for the feedback! Most appreciated. 🙏🙇♂️
Hey David, Great video! I wanted to point out that you don't mention that the formula for the occurrences needs to be entered as an array and it doesn't show that in the video either. Took me a long time to figure this out and get the formulas to work correctly :/
David, excellent video, so logical and clear, as with your other videos. I see that you base it on binary values of 0 or 1 but what if the characterstics have multiple values such as a likert based scale or for example such as a person's age?
Thank you very much for your Video. It is Awesome. I wonder if you may do a simple video about random forest. Thank you!
Thanks for a great video! I actually tried your model to analyze some data from my own company, and set it up with 4-ways lift and 5-ways lift. When trying the 5-ways lift I encountered a problem with the Solver, it actually picked the same decision variable twice. In the Titanic Dataset this could for example be "Female", "Firstclass", "Istriplet", "Female" and "Survived" - that is "female" is choocen twice. Do you happen to know how we can make a constraint that ensure unique decision variables? Thanks in advance for any help.
Outstanding tutorial! You present very well and your method of teaching is excellent. I have used Python for amateur data analysis but I have not used R. Do you see a big difference between the two? Keep in mind I'm not a mathematician or computer science guy. 👍👍
David, Great Content!, best explanation. By the way, what are support and confidence?
Thanks, Ajay! Glad you liked the video.
In a nutshell:
Support - The percentage of transactions in the data with a particular itemset. For example, using the Titanic data, the percentage of passengers that were both female and traveling in 1st class.
Confidence - The probability that a transaction has the items on the left-hand side of the rule and also contains the item on the right-hand side of the rule. Take the following rule using the Titanic data:
{female, 1st Class} => Survived
The confidence is the probability of seeing females traveling in first class that survived.
WOW your AWESOME!!
Hi David, I am wondering if I can use this technique to segment the market for my thesis. In my survey, I have a lot of categorical variables such as the living area, income (in classes), education, etc. The purpose of my thesis is to research if there is a link between these categorical variables and the interest in the product that my thesis company is selling.
Do you recommend using the market basket analysis for this or are there any other data analysis methods that will be suitable for this topic?
Ps: Thanks for your very informative videos, I genuinely think your work will help me loads with my thesis.
Awesome, David! Do you have something like this excel for conjoint analysis simulation of scenarios?
Glad you like the content! At this time I do not have any content on conjoint analysis.
If we only are looking for associations with those who've survived, could we clean up this data set to include only records where survived = 1?
Gosh Dave! You are just the best!! I wish you could be my mentor. Thanks so much for this. It would be really nice to learn how to do this with R.
#TeamR
Thank you for the compliment and glad you liked the video.
If you're interested in market basket analysis with R, check out this blog post on my website:
💻 www.daveondata.com/blog/titanic-market-basket-analysis-with-r/
I ran the solver and it came back with Female and HasElderly as the best combo, with a lift of 2.53. Did I do something wrong?
This video is great but does anyone know how to organize raw data into a workable table? What do you do to organize the data if you have columns with variables in random order. Using the titanic example, what if your passenger data looks like this for thousands of rows:
Passenger 1| single | survived | third class | adult
Passenger 2| survived | child | with group | second class
Hi David, great tutorial. Q: how can you use the same concept for associations that are in decimals like 0.8, 0.4, 0, 1? and how can you highlight that significance is above 0.7 for instance? is that doable in excel?
viele danke
Hey dave, Amazing Content. Do you have the R code for this analysis as well?
Check out the following blog post on my website:
💻 www.daveondata.com/blog/titanic-market-basket-analysis-with-r/
Great job, but isn't it easy if we use the correlation feature of data analysis?
Great question!
Did you have a specific correlation technique in mind?
I'm thinking that cosine similarity might be better between the vectors (e.g., moving from binary to "product" counts), but then there's the problem of handling all the vector combinations (e.g., a rule using 4 "products" on the LHS and 1 "product" on the RHS.
@@DaveOnData
Yes, you're right
Thank you David for this clarification
My pleasure!
I Finally mangaed to duplicate this in Power BI. Nearly broke me but I feel like I've got superpowers now :)
The formula for 3 way lift however is different from the traditional formula seen elsewhere (Number of A,B, C Occurences / (Total transactions * Fraction of A occurrences * Fraction of B occurrences * Fraction of C occurrences)).
This Formula used here seems to be (Number of A,B, C Occurences / (Fraction of A Occurances * Fraction of B&C Occurances).
Is there a reason for this? I know this is probably TLDR :D
In articles on the same topic I see that Lift is calculated using Confidence, how come Confidence is not being applied here?
Hi David, I'm looking at my transactional data where each row represents an order and the columns are the products (107 total columns). For each order (row), I have either a 1 (purchased) or 0 (did not purchase) in every cell. I went through your tutorial and it was great. But what I do not understand is that I do not have a RH column. If your example, you had RH as survived 1 or 0. But I do not have such a column as every row is a purchase so yes, every one purchased something. What am I to do?
Does the table data needs to be in binary form only to conduct MBA? like in thr form of 0 and 1 only?
why you didn't explain the formulas in this sheet and the concept behind them, for example the 2 way method formulas and table is not like the 3 way method
Great question!
Unfortunately, that was an editorial call because I wanted to keep the video from getting too long.
That and most folks on RUclips aren't interested in the math - they just want to get things done.
I might follow up with a more detailed video on market basket analysis math later.
If you're interested in the math right now, check out the following blog post:
towardsdatascience.com/a-gentle-introduction-on-market-basket-analysis-association-rules-fa4b986a40ce
@@DaveOnData
thanks and please make a video about market basket analysis math.
you are one of the few content creators in data on RUclips that can explain quite well with easy example, I started following your posts on LinkedIn then I jumped on your channel here.
Copy that, I'll add it to my video backlog! And thank you for the compliment, very much appreciated. 🙏🙇♂️
Cool
I must confess that learning how to use the software is what gets procastinating to start making soft. It scares lol
I dont have solver
The Solver is an Add-in that is not enabled by default. Check out the following article from Microsoft:
support.microsoft.com/en-us/office/load-the-solver-add-in-in-excel-612926fc-d53b-46b4-872c-e24772f078ca
how to transform like titanic dataset but i have dataset like this
id_transaction products
1 egg,bread
2 bread,milk,peanuts
3 egg,milk, peanuts
Hello Smart people of the internet. Can anyone please explain to me why his frequency data set doesn't add up to 100% ??
Because multiple features can occur at the same time. Ex: You can be Female, FirstClass and HasChild.
You encoded your data between 0-1 why ?
Not enough in detail
To those wondering where Solver Parameters is: support.microsoft.com/en-us/office/load-the-solver-add-in-in-excel-612926fc-d53b-46b4-872c-e24772f078ca