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Predicting pregnant shoppers based on their transaction history and purchasing trends is a challenging problem because of data sparsity and imbalance. Not only are the instances, i.e., the ratio of pregnant versus nonpregnant shoppers, skewed but also is the proportion of the products that reveal pregnancy status. The problem of class imbalance has been solved by taking an equal number of positive and negative examples for training and testing while deployment has been done on the entire dataset yielding results that were congruent with the real-world. In this paper, we use a novel approach that uses deep Convolutional Neural Networks (CNNs) to handle one dimensional data. The proposed solution overcomes the above mentioned challenges and proves that two dimensional CNNs outperform a baseline LightGBM (gradient boosting framework that uses tree based learning algorithms) model on two different datasets the dataset based on twenty one hot products and the dataset based on all products by subcategory. The CNN model reached an F1 score of 0.69 on the test set.

Predicting Pregnant Shoppers Based on Purchase History Using Deep Convolutional Neural Networks