Abstract: Technologies are provided for evaluation of electronic books as candidates for publication as audiobooks. An electronic book can be evaluated using a machine-learning model trained using target electronic books as a training set and various attributes of the target electronic books as feature inputs. A target electronic book is an electronic book that published prior to the publication of the electronic book as a successful audiobook. Success can be established using performance metrics, such as number of sales and/or review ratings, and a defined success rule. The various attributes include static attributes (author and genre, for example) and dynamic attributes representing respective performance metrics. By applying the trained machine-learning model to values of static and dynamic attributes of a particular electronic book, a candidacy score can be determined. The candidacy score represents a likelihood that the particular electronic book is a candidate for publication as an audiobook.