Lecture 68:- ML Apriori Code

Certainly! Here's a step-by-step example of how to implement the Apriori algorithm for association rule mining using Python and the mlxtend library. This example uses a sample retail transaction dataset:

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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, 1, 2, 3, 3, 3, 4, 4, 5, 6], 'Items': ['milk', 'bread', 'milk', 'bread', 'milk', 'diapers', 'milk', 'bread', 'milk', 'bread']} 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) # Generate association rules with minimum confidence of 0.6 rules = association_rules(frequent_itemsets, metric='confidence', min_threshold=0.6) # Display frequent itemsets and association rules print("Frequent Itemsets:") print(frequent_itemsets) print("\nAssociation Rules:") print(rules)

In this example:

1. We define a sample transaction dataset with 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.
4. We use the association_rules function to generate association rules from the frequent itemsets. We set the metric to 'confidence' and specify a minimum confidence threshold of 0.6.
5. The output will display the frequent itemsets and the generated association rules along with various metrics like support, confidence, and lift.

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

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pip install mlxtend

Remember that this is a simplified example. In practice, you would work with larger and more complex datasets, and you might want to fine-tune the parameters and explore different metrics to obtain meaningful results for your specific use case.