discussion on how we can use statistical methods to measure and improve the efficacy of http://freeCodeCamp.com
hi can i calculate transaction of single column dataset
T ID Items
1 {Bread,Milk}
2 {Bread, Diapers, Beer, Eggs}
3 {Milk, Diapers, Beer, Cola}
4 {Bread, Milk, Diapers, Beer}
5 {Bread, Milk, Diapers, Cola}
1 {Bread,Milk}
2 {Bread, Diapers, Beer, Eggs}
3 {Milk, Diapers, Beer, Cola}
4 {Bread, Milk, Diapers, Beer}
5 {Bread, Milk, Diapers, Cola}
this is my dataset
please help me
@KiranGkiran11_twitter welcome! In the future, it would help if you gave more context about your problem. Luckily, I was able to figure out that "transactions" in this case was in the context of association analysis. The beginning of this article describes your example.
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https://medium.freecodecamp.org/how-to-predict-likes-and-shares-based-on-your-articles-title-using-machine-learning-47f98f0612ea
Hello all, Can you guys mention few resources for Machine Learning, Please help me learn from the basics. I also want to learn the Mathematics involved.
thanks @erictleung
Error in asMethod(object) :
column(s) 1 not logical or a factor. Discretize the columns first.
column(s) 1 not logical or a factor. Discretize the columns first.
when i apply appriori function error show like this
Association rules only takes discrete / categorically variables. Transform your data. @KiranGkiran11_twitter
but using this code i get result, but when i give dataset locally error occur
library(arules)
library(arulesViz)
library(datasets)
Load the data set
data(Groceries)
print(Groceries)
rules <- apriori(Groceries, parameter = list(supp = 0.001, conf = 0.8))
Show the top 5 rules, but only 2 digits
options(digits=2)
inspect(rules[1:5])
lhs rhs support confidence lift count
[1] {liquor,red/blush wine} => {bottled beer} 0.001931876 0.9047619 11.235269 19
[2] {curd,cereals} => {whole milk} 0.001016777 0.9090909 3.557863 10
[3] {yogurt,cereals} => {whole milk} 0.001728521 0.8095238 3.168192 17
[4] {butter,jam} => {whole milk} 0.001016777 0.8333333 3.261374 10
[5] {soups,bottled beer} => {whole milk} 0.001118454 0.9166667 3.587512 11
[1] {liquor,red/blush wine} => {bottled beer} 0.001931876 0.9047619 11.235269 19
[2] {curd,cereals} => {whole milk} 0.001016777 0.9090909 3.557863 10
[3] {yogurt,cereals} => {whole milk} 0.001728521 0.8095238 3.168192 17
[4] {butter,jam} => {whole milk} 0.001016777 0.8333333 3.261374 10
[5] {soups,bottled beer} => {whole milk} 0.001118454 0.9166667 3.587512 11
@GoldbergData
Check the types of your columns. Use glimpse(data). Then reread your error message
Observations: 5
Variables: 2
$ T.ID <int> 1, 2, 3, 4, 5
$ Items <fct> {Bread,Milk}, {Bread, Diapers, Beer, Eggs}, {Milk, Diapers, Beer, Cola}, {Bread, Milk, Diapers, Beer}, {Bread, Milk, Diapers, Cola}
Variables: 2
$ T.ID <int> 1, 2, 3, 4, 5
$ Items <fct> {Bread,Milk}, {Bread, Diapers, Beer, Eggs}, {Milk, Diapers, Beer, Cola}, {Bread, Milk, Diapers, Beer}, {Bread, Milk, Diapers, Cola}
@GoldbergData
may i pm u @GoldbergData
The talks and slides for the Emotion AI Summit from earlier this month have finally been made accessible, for anyone who is curious.