Apriori Algorithm

Association Rules

S.Reza.Eftekhary 1400/11/12

1 - Importing the required libraries

import numpy as np import pandas as pd from mlxtend.frequent_patterns import apriori, association_rules, fpgrowth

2 - Loading and exploring the data

Import Data UCI

http://archive.ics.uci.edu/ml/datasets/Online+Retail

data = pd.read_csv('OnlineRetail.csv') data.head()

3 - Exploring the columns of the data

data.columns print (data.columns)

4 - Exploring the different regions of transactions

data.Country.unique()

5 - Cleaning the Data

data['Description'] = data['Description'].str.strip() # Dropping the rows without any invoice number data.dropna(axis = 0, subset =['InvoiceNo'], inplace = True) data['InvoiceNo'] = data['InvoiceNo'].astype('str')

6 - Splitting the data according to the region of transaction

# Dropping all transactions which were done on credit data = data[~data['InvoiceNo'].str.contains('C')] # Transactions done in France basket_France = (data[data['Country'] =="France"] .groupby(['InvoiceNo', 'Description'])['Quantity'] .sum().unstack().reset_index().fillna(0) .set_index('InvoiceNo')) # Transactions done in the United Kingdom basket_UK = (data[data['Country'] =="United Kingdom"] .groupby(['InvoiceNo', 'Description'])['Quantity'] .sum().unstack().reset_index().fillna(0) .set_index('InvoiceNo')) # Transactions done in Portugal basket_Por = (data[data['Country'] =="Portugal"] .groupby(['InvoiceNo', 'Description'])['Quantity'] .sum().unstack().reset_index().fillna(0) .set_index('InvoiceNo')) basket_Sweden = (data[data['Country'] =="Sweden"] .groupby(['InvoiceNo', 'Description'])['Quantity'] .sum().unstack().reset_index().fillna(0) .set_index('InvoiceNo'))

7 - Hot encoding the Data

# Defining the hot encoding function to make the data suitable # for the concerned libraries def hot_encode(x): if(x<= 0): return 0 if(x>= 1): return 1 # Encoding the datasets basket_encoded = basket_France.applymap(hot_encode) basket_France = basket_encoded basket_encoded = basket_UK.applymap(hot_encode) basket_UK = basket_encoded basket_encoded = basket_Por.applymap(hot_encode) basket_Por = basket_encoded basket_encoded = basket_Sweden.applymap(hot_encode) basket_Sweden = basket_encoded

8 - Building the models and analyzing the results

a) France:

# Building the model frq_items = apriori(basket_France, min_support = 0.05, use_colnames = True) # Collecting the inferred rules in a dataframe rules = association_rules(frq_items, metric ="lift", min_threshold = 1) rules = rules.sort_values(['confidence', 'lift'], ascending =[False, False]) print(rules.head())

From the above output, it can be seen that paper cups and paper and plates are bought together in France. This is because the French have a culture of having a get-together with their friends and family atleast once a week. Also, since the French government has banned the use of plastic in the country, the people have to purchase the paper-based alternatives. 

b) United Kingdom:

#frq_items = apriori(basket_UK, min_support = 0.01, use_colnames = True) #rules = association_rules(frq_items, metric ="lift", min_threshold = 1) #rules = rules.sort_values(['confidence', 'lift'], ascending =[False, False]) #print(rules.head())

If the rules for British transactions are analyzed a little deeper, it is seen that the British people buy different colored tea-plates together. A reason behind this may be because typically the British enjoy tea very much and often collect different colored tea-plates for different occasions.

c) Portugal:

frq_items = apriori(basket_Por, min_support = 0.05, use_colnames = True) rules = association_rules(frq_items, metric ="lift", min_threshold = 1) rules = rules.sort_values(['confidence', 'lift'], ascending =[False, False]) print(rules.head())

On analyzing the association rules for Portuguese transactions, it is observed that Tiffin sets (Knick Knack Tins) and color pencils. These two products typically belong to a primary school going kid. These two products are required by children in school to carry their lunch and for creative work respectively and hence are logically make sense to be paired together.

d) Sweden:

frq_items = apriori(basket_Sweden, min_support = 0.05, use_colnames = True) rules = association_rules(frq_items, metric ="lift", min_threshold = 1) rules = rules.sort_values(['confidence', 'lift'], ascending =[False, False]) print(rules.head())

On analyzing the above rules, it is found that boys’ and girls’ cutlery are paired together. This makes practical sense because when a parent goes shopping for cutlery for his/her children, he/she would want the product to be a little customized according to the kid’s wishes.

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