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Innovative Information Systems services have the potential to promote more sustainable behavior. For these so-called Green Information Systems (Green IS) to work well, they should be tailored to individual behavior and attitudes. Although various theoretical models already exist, there is currently no technological solution that automatically estimates individual’s current sustainability levels related to their consumption behaviors in various consumption domains (e.g., mobility and housing). The paper aims at addressing this gap and presents the design of GREENPREDICT, a framework that enables to predict these levels based on multiple features, such as demographic, socio-economic, psychological, and factual knowledge about energy information. To do so, the paper presents and evaluates six different classifiers to predict acts of consumption on the Swiss Household Energy Demand Survey (SHEDS) dataset containing survey answers of 2000 representative individuals living in Switzerland. The results highlight that the ensemble prediction models (i.e., random forests and gradient boosting trees) and the multinomial logistic regression model are the most accurate for the mobility and housing prediction tasks.