Apriori market

Download the Market basket dataset.
#Write a python program to read the dataset and display its information.
#Preprocess the data (drop null values etc.)
#Convert the categorical values into numeric format.
#Apply the apriori algorithm on the above dataset to generate the frequent itemsets and
#association rules

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

data = pd.read_csv(r"Market_Basket_Optimisation.csv",header=None)
transactions = []
for i in range(0,len(data)):
transactions.append([str(data.values[i, j]) for j in range(0,len(data.columns))])

te=TransactionEncoder()
te_array=te.fit(transactions).transform(transactions)
df=pd.DataFrame(te_array,columns=te.columns_)
df

freq_patterns=apriori(df,min_support=0.05,use_colnames=True)
print(freq_patterns)

rules=association_rules(freq_patterns,metric='support',min_threshold=0.05)
print(rules)