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

# creating simple dataset 
transactions = [
    ['Milk', 'Bread', 'Butter'],
    ['Milk', 'Bread'],
    ['Milk', 'Butter'],
    ['Bread', 'Butter'],
    ['Milk', 'Bread', 'Butter'],
    ['Bread'],
]
# encoding
te = TransactionEncoder()
te_array = te.fit(transactions).transform(transactions)

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

# finding frequest itemset
frequent_itemsets = apriori(
    df,
    min_support=0.4,
    use_colnames=True
)

frequent_itemsets

# generate rules
rules = association_rules(
    frequent_itemsets,
    metric="confidence",
    min_threshold=0.6
)

rules[['antecedents', 'consequents', 'support', 'confidence', 'lift']]

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