import pandas as pd
from mlxtend.preprocessing import TransactionEncoder
from mlxtend.frequent_patterns import apriori , association_rules

transcations = [['TATA','Nexon','2017'],
['MG','Astor','2021'],
['Kia' ,'Seltos' ,'2019'],
['Hyundai','Creta','2015']]

te = TransactionEncoder()
te_data = te.fit(transcations).transform(transcations)

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

frequent_items = apriori(df, min_support=0.2,use_colnames=True)

print(frequent_items)

rules = association_rules(frequent_items,metric='support',min_threshold=0.05)

print(rules)