Lecture 67:- ML Apriori Code 1

Certainly! Here's a basic example of how to implement the Apriori algorithm for association rule mining using Python and the mlxtend library:

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from mlxtend.frequent_patterns import apriori from mlxtend.frequent_patterns import association_rules import pandas as pd # Sample transaction data data = {'TransactionID': [1, 2, 3, 4, 5], 'Items': ['milk bread eggs', 'bread eggs', 'milk eggs', 'milk bread', 'milk']} df = pd.DataFrame(data) # Preprocess data: split items and create a binary matrix item_matrix = df['Items'].str.get_dummies(' ') # Find frequent itemsets with minimum support of 0.4 frequent_itemsets = apriori(item_matrix, min_support=0.4, use_colnames=True) # Display the resulting frequent itemsets print("Frequent Itemsets:") print(frequent_itemsets)

In this example:

1. We use a simplified transaction dataset containing transaction IDs and items purchased in each transaction.
2. We preprocess the data by creating a binary matrix where each row represents a transaction and each column represents an item. A value of 1 indicates that the item was purchased in the transaction, and a value of 0 indicates that it was not.
3. We use the apriori function from the mlxtend.frequent_patterns module to find frequent itemsets with a minimum support of 0.4. The use_colnames=True parameter ensures that item names are used in the results.

The output will display the frequent itemsets along with their corresponding support values.

Keep in mind that this example is a simplified illustration. In practice, you would typically work with larger and more complex datasets. Additionally, you can explore various parameters of the apriori function, such as setting minimum and maximum itemset lengths, using different metrics, and applying additional filtering based on other criteria.

Make sure you have the mlxtend library installed. You can install it using the following command:

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pip install mlxtend