Hands-On Data Science for Marketing

Data preparation

Before we dive into building a product recommender engine using a collaborative filtering algorithm, we need to do the following couple of things:

• Handle NaN values in the dataset
• Build a customer-to-item matrix

First, we need to handle NaN values in our dataset, especially those NaNs in the CustomerID field. Without correct values in the CustomerID field, we cannot build a proper recommendation system, since the collaborative filtering algorithm depends on the historical item purchase data for individual customers.

Second, we need to build customer-to-item matrix before we move onto implementing the collaborative filtering algorithm for product recommendation. The customer-item matrix is simply tabular data, where each ...