p13as.py

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

# Load data
data = pd.read_excel('data.xlsx', header=None)

# Fill blank cells with "N/A"
data.fillna("N/A", inplace=True)

# Convert data to a list of transactions
transactions = data.values.tolist()

# Encoding data with one-hot encoding
te = TransactionEncoder()
te_ary = te.fit(transactions).transform(transactions)
df = pd.DataFrame(te_ary, columns=te.columns_)

# Building the model
frequent_itemsets = apriori(df, min_support=0.3, use_colnames=True)
rules = association_rules(frequent_itemsets, metric="confidence", min_threshold=0.6)

# Displaying the results
print("Frequent Itemsets:")
print(frequent_itemsets)

print("\nAssociation Rules:")
print(rules)