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Market Basket Analysis is a data mining technique used to discover purchasing patterns by identifying frequent combinations of items in customer transactions. This implementation uses the MLxtend library's Apriori algorithm and Transaction Encoder to analyze co-occurring product categories.
A collection of items that appear together in transactions. For example: {home_and_kitchen, electronics}
A set of items purchased together in a single order, representing one customer's basket.
A pattern of the form X → Y, indicating that customers who buy X are likely to buy Y.
Using MLxtend's implementation, we analyze transaction patterns to identify strong associations between product categories, helping to understand customer purchasing behavior and make data-driven recommendations.
Network visualization of category relationships. Node size represents category frequency, and edge thickness represents co-occurrence strength.
Word cloud visualization showing the relative frequency of different product categories. Larger text indicates higher frequency in the dataset.
Algorithm Type: Content-Based Filtering
Key Features:
Algorithm Type: User-Based Collaborative Filtering
Key Components:
Data Cleaning:
Data Processing
Numerical Computing
Machine Learning
Database
Machine Learning
Graph Theory