butter - Posts - OneCompiler

<html> <head> <title>Student Name</title> <script> function changeStyle(element) { if (element.value == “”) { element.style.color = “”; element.style.fontSize = “”; document.getElementById(“image”).style.display = “block”; document.getElementById(“image”).style.width = “100px”; document.getElementById(“image”).style.height = “100px”; } else { element.style.color = “red”; element.style.fontSize = “18px”; document.getElementById(“image”).style.display = “none”; } } </script> </head> <body> <input type=”text” name=”studentname” onfocus=”changeStyle(this)” onblur=”changeStyle(this)” /> <img src=”Saifanphoto.png” id=”image” style=”display:none;” /> </body> </html>

Importpandasaspd
Frommlxtend.preprocessingimportTransactionEncoder
Frommlxtend.frequent_patternsimportapriori,association_rules
#Creatingthedataset
Dataset=[[‘butter’,‘bread’,‘milk’],[‘butter’,‘flour’,‘milk’,‘sugar’],[‘butter’,‘eggs’,‘milk’,‘salt’],
[‘eggs’],[‘butter’,‘flour’,‘milk’,‘salt’]]
Df=pd.DataFrame(dataset)
#Convertingthecategoricalvaluesintonumericformat
Te=TransactionEncoder()
Te_ary=te.fi
t(dataset).
transform(dataset)
Df=pd.DataFrame(te_ary,col
umns=te.columns_)
#Generatingfrequenti
temsetsusingApriorial
Min_sup_values=[0.4,0.
3,0.2]
Formin_supinmin_sup_values:
Frequent_i
temsets=apriori
gorithmwithdifferentmi
n_supvalues
(df,min_support=mi
n_sup,use_colnames=True)
Print(“Fr
equentItemsetswithminimumsupportof”,min_sup)
Print(fr
equent_itemsets)
#Generatingassociati
onrules
Rules=association_rul
es(frequent_i
Print(“Associ
ationRuleswithmini
Print(rul
es)