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We present in this paper an automated method to assess the quality of Jupyter notebooks. The quality of notebooks is assessed in terms of reproducibility and executability. Specifically, we automatically extract a number of expert-defined features for each notebook, perform a feature selection step, and then trained supervised binary classifiers to predict whether a notebook is reproducible and executable, respectively. We also experimented with semantic code embeddings to capture thedoi:10.32473/flairs.v34i1.128560 fatcat:quqr6bnferg7vpli7nasaoo6ne