Analysis of shopping behavior of clients to understand the baskets of products that are purchased together for an international supermarket.
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Purchasing behavior changes depending on age, gender, and purchasing power along with other characteristics such as weather, time of the day, month or year. The client wanted to study the consumer behavior, as it helps them position their products better and develop effective marketing strategies.
The dataset of customers consisted of approximate 5 million transactions, 600,000 receipts and 10,000 customer cardholders. Data mining techniques were used to build advanced mathematical models for predicting customer behaviors based on Class Association Rules developed in-house using SCALA/ MongoDB and R.