Missing data is a prevalent problem in data science for many fields such as natural, social, and health sciences. Since most regression methods can not handle missing data directly, imputation methods are used in data pre-processing. Finding the best imputation method is non-trivial, however. Moreover, our results show that an independent choice for a best imputation method does not always result in the best predictive performance in the end; the combination matters. Furthermore, search-based approaches for finding a best-fitting imputer/regressor-pair can be computationally intensive. In this paper, we propose the MetaLIRS (Meta Learning Imputation and Regression Selection) framework for developing resource-friendly ML-based recommendation models for method selection. With MetaLIRS, we constructed a proof-of-concept recommendation model based on 12 meta-features that achieves an accuracy of 63% for selecting the best-fitting imputer/regressor-pair. A data scientist can use this model for a quick resource-friendly recommendation on which imputation and regression method to use for their particular data set and task without the need for an expensive grid search among methods.