Recep Arda Kaya - 435060

Apriori Association to Grocery Store Data Set

Objective

* Trying Apriori alogrithm with generating frequent itemsets and explain how it works.

Data

Due to more practical explanation, I am going to use Grocery Store Data Set

This dataset contains 11 items : JAM, MAGGI, SUGAR, COFFEE, CHEESE, TEA, BOURNVITA, CORNFLAKES, BREAD, BISCUIT and MILK.

Review of the Data

!pip install -U scikit-learn
!pip install mlxtend  

## Necessary libraries imported
import numpy as np
import pandas as pd
from mlxtend.preprocessing import TransactionEncoder
from mlxtend.frequent_patterns import apriori

import warnings
warnings.filterwarnings("ignore")

data = pd.read_csv(r'C:\Users\Arda\Downloads\GroceryStoreDataSet.csv',names=["products"],header=None)

data.head(10)

	products
0	MILK,BREAD,BISCUIT
1	BREAD,MILK,BISCUIT,CORNFLAKES
2	BREAD,TEA,BOURNVITA
3	JAM,MAGGI,BREAD,MILK
4	MAGGI,TEA,BISCUIT
5	BREAD,TEA,BOURNVITA
6	MAGGI,TEA,CORNFLAKES
7	MAGGI,BREAD,TEA,BISCUIT
8	JAM,MAGGI,BREAD,TEA
9	BREAD,MILK

data.values

array([['MILK,BREAD,BISCUIT'],
       ['BREAD,MILK,BISCUIT,CORNFLAKES'],
       ['BREAD,TEA,BOURNVITA'],
       ['JAM,MAGGI,BREAD,MILK'],
       ['MAGGI,TEA,BISCUIT'],
       ['BREAD,TEA,BOURNVITA'],
       ['MAGGI,TEA,CORNFLAKES'],
       ['MAGGI,BREAD,TEA,BISCUIT'],
       ['JAM,MAGGI,BREAD,TEA'],
       ['BREAD,MILK'],
       ['COFFEE,COCK,BISCUIT,CORNFLAKES'],
       ['COFFEE,COCK,BISCUIT,CORNFLAKES'],
       ['COFFEE,SUGER,BOURNVITA'],
       ['BREAD,COFFEE,COCK'],
       ['BREAD,SUGER,BISCUIT'],
       ['COFFEE,SUGER,CORNFLAKES'],
       ['BREAD,SUGER,BOURNVITA'],
       ['BREAD,COFFEE,SUGER'],
       ['BREAD,COFFEE,SUGER'],
       ['TEA,MILK,COFFEE,CORNFLAKES']], dtype=object)

Apriori Algorithm

Apriori is a popular algorithm for extracting frequent itemsets with applications in association rule learning. The apriori algorithm has been designed to operate on databases containing transactions, such as purchases by customers of a store. An itemset is considered as "frequent" if it meets a user-specified support threshold. For instance, if the support threshold is set to 0.5 (50%), a frequent itemset is defined as a set of items that occur together in at least 50% of all transactions in the database.

## We can transform it into the right format via the TransactionEncoder as follows:

transact=TransactionEncoder()
te_data=transact.fit(data).transform(data)
transact.columns_

df=pd.DataFrame(te_data,columns=transact.columns_)
df

	A	B	C	D	E	F	G	I	J	K	L	M	N	O	R	S	T	U	V
0	False	False	False	False	False	False	False	True	False	True	True	True	False	False	False	False	False	False	False
1	True	True	False	True	True	False	False	False	False	False	False	False	False	False	True	False	False	False	False
2	False	True	True	False	False	False	False	True	False	False	False	False	False	False	False	True	True	True	False
3	True	True	False	True	True	False	False	False	False	False	False	False	False	False	True	False	False	False	False
4	False	False	False	False	False	False	False	True	False	True	True	True	False	False	False	False	False	False	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
61	False	False	False	False	True	False	True	False	False	False	False	False	False	False	True	True	False	True	False
62	True	False	False	False	True	False	False	False	False	False	False	False	False	False	False	False	True	False	False
63	False	False	False	False	False	False	False	True	False	True	True	True	False	False	False	False	False	False	False
64	False	False	True	False	True	True	False	False	False	False	False	False	False	True	False	False	False	False	False
65	True	False	True	False	True	True	False	False	False	True	True	False	True	True	True	True	False	False	False

66 rows × 19 columns

Now, let us return the items and itemsets with at least 38% support:

a = apriori(df, min_support=0.38, use_colnames=True)
a.sort_values(ascending=False, axis=0,by='support')

	support	itemsets
1	0.606061	(E)
0	0.560606	(A)
2	0.439394	(R)
3	0.393939	(E, A)

Most frequent single items

COFFEE = 0.606061

BISCUIT = 0.560606

Most frequent set items

COFFEE AND BISCUIT = 0.393939

Conclusions

Support threshold is set to 0.38(38%), a frequent itemset is defined as a set of items that occur together in at least 38% of all transactions in the database. In our example we can say that biscuit and coffee associated together if we set our support to 38%. Also, their purchase frequency is much higher while we consider them as a single unit.

References

https://medium.com/edureka/apriori-algorithm-d7cc648d4f1e