Q. 1) Write a PHP script to read “Movie.xml” file and print all MovieTitle and ActorName of file using
DOMDocument Parser. “Movie.xml” file should contain following information with at least 5 records
with values. MovieInfoMovieNo, MovieTitle, ActorName ,ReleaseYear [Marks 15]

<?php // Load XML file using DOMDocument $doc = new DOMDocument(); $doc->load("Movie.xml"); // Get all 'MovieInfo' elements $movieInfos = $doc->getElementsByTagName("MovieInfo"); // Loop through each 'MovieInfo' element foreach ($movieInfos as $movieInfo) { // Get MovieTitle and ActorName elements $movieTitles = $movieInfo->getElementsByTagName("MovieTitle"); $actorNames = $movieInfo->getElementsByTagName("ActorName"); // Print MovieTitle and ActorName echo "MovieTitle: " . $movieTitles->item(0)->nodeValue . "<br>"; echo "ActorName: " . $actorNames->item(0)->nodeValue . "<br><br>"; } ?>

Q. 2)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.preprocessing import TransactionEncoder
from mlxtend.frequent_patterns import apriori, association_rules

Read the dataset

df = pd.read_csv("market_basket.csv")

Drop null values

df.dropna(inplace=True)

Convert categorical values into numeric format

te = TransactionEncoder()
te_ary = te.fit(df.values).transform(df.values)
df_encoded = pd.DataFrame(te_ary, columns=te.columns_)

Apply Apriori algorithm to generate frequent itemsets

frequent_itemsets = apriori(df_encoded, min_support=0.05, use_colnames=True)

Generate association rules

rules = association_rules(frequent_itemsets, metric="confidence", min_threshold=0.2)

Display frequent itemsets and association rules

print("Frequent Itemsets:")
print(frequent_itemsets)

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