This paper presents a software package developed in Python that allows the application of the technique known as Kernel Logistic Regression (KLR), a Machine Learning (ML) tool, to the problem of transport demand prediction. More concretely, it permits the specification of a series of models using KLR and their estimation by means of a Penalised Maximum Likelihood Estimation (PMLE) procedure providing a set of goodness-of-fit indicators and the application of model validation techniques. Another functionality is that it allows to extract from the model several indicators such as the Willingness to Pay (WTP) or the Value of Time (VOT).