In many cases, a machine learning model must learn to correctly predict a few data points with particular values of interest in a broader range of data where many target values are zero. Zero-inflated data can be found in diverse scenarios, such as lumpy and intermittent demands, power consumption for home appliances being turned on and off, impurities measurement in distillation processes, and even airport shuttle demand prediction. The presence of zeroes affects the models’ learning and may result in poor performance. Furthermore, zeroes also distort the metrics used to compute the model’s prediction quality. This paper showcases two real-world use cases (home appliances classification and airport shuttle demand prediction) where a hierarchical model applied in the context of zero-inflated data leads to considerable performance improvements. In particular, for home appliances classification, the weighted average of Precision, Recall, F1, and Area Under the Receiver Operating Characteristic Curve (AUC ROC) was increased by 39%, 49%, 88%, and 48%, respectively. Furthermore, it is estimated that the proposed approach is also four times more energy efficient than the state-of-the-art (SOTA) approach against which it was compared to. Two-fold modeling approaches significantly outperform regular regression, especially when predicting the occurrence of demand events. SOTA results were achieved using Gradient Boosting trees to determine whether an event will occur and Visual Geometry Group (VGG) or Support Vector Regressor (SVR) models for the subsequent classification/regression. The code has been released at two separate repositories.