ASSOCIATION RULE MINING
DATA PREPARATION
For Association Rule Mining, we will use a transaction dataset. Let's assume we have a transaction dataset where each row represents a transaction and each column represents an item.
We'll create a sample transaction dataset for demonstration purposes:
ASSOCIATION RULE MINING
FEATURE ENGINEERING
Association Rule Mining (ARM) typically doesn't involve traditional feature engineering steps like scaling, transformation, or creation of new features, as it deals with binary data representing item occurrences in transactions.
In ARM, the focus is primarily on analyzing the presence or absence of items in transactions and discovering association rules between them.
These association rules are based on itemsets, which are sets of items that frequently occur together in transactions.
ASSOCIATION RULE MINING
MODELING
We will use the mlxtend library to perform Association Rule Mining. We'll first transform the dataset into the required format and then mine the association rules.
ASSOCIATION RULE MINING
MODEL EVALUATION
Association rules are evaluated based on metrics like support, confidence, and lift. These metrics help determine the strength and significance of the rules.
REFLECTION
Through this exercise, I gained practical experience in implementing machine learning models in Python. I learned how to preprocess data, engineer features, and build and evaluate models. The clustering task enhanced my understanding of unsupervised learning, while the Association Rule Mining task introduced me to a different aspect of data mining and pattern discovery. These projects reinforced concepts covered in the course, such as data preprocessing and model evaluation techniques. Overall, this project helped me broaden my understanding of machine learning and its applications in real-world scenarios.