Majority of the past research on application of machine learning (ML) in earthquake engineering focused on contrasting the predictive performance of different ML algorithms. In contrast, the emphasis of this paper is on the use of data to boost the predictive performance of surrogates. To that end, a novel data engineering methodology for seismic collapse risk assessment is proposed. This method, termed the automated collapse data constructor (ACDC), stems from combined understanding of the ground motion characteristics and the collapse process. In addition, the data-driven collapse classifier (D2C2) methodology is proposed which enables conversion of the collapse data from a regression format to a classification format. The D2C2 methodology can be used with any classification tool, and it allows estimation of seismic collapse capacities in a way analogous to the incremental dynamic analysis. The proposed methodologies are tested in a case study using decision trees (XGBoost) and neural network classifiers with an extensive dataset of collapse responses of a 4-story and an 8-story steel moment resisting frames. The results suggest that the ACDC methodology allows for dramatic improvement of the predictive performance of data-driven tools while at the same time significantly reducing data requirements. Specifically, the proposed method can reduce the number of ground motions required for collapse risk assessment from at least forty, as traditionally used, to less than twenty motions. Moreover, interpretation of feature importance conforms with the engineering understanding while revealing a novel, period-dependent measure of ground motion duration. All data and code developed in this research are made openly available.