  Predicting the outcome of liquid droplet collisions is an extensively studied
phenomenon but the current physics based models for predicting the outcomes are
poor (accuracy $\approx 43\%$). The key weakness of these models is their
limited complexity. They only account for 3 features while there are many more
relevant features that go unaccounted for. This limitation of traditional
models can be easily overcome through machine learning modeling of the problem.
In an ML setting this problem directly translates to a classification problem
with 4 classes. Here we compile a large labelled dataset and tune different ML
classifiers over this dataset. We evaluate the accuracy and robustness of the
classifiers. ML classifiers, with accuracies over 90\%, significantly
outperform the physics based models. Another key question we try to answer in
this paper is whether existing knowledge of the physics based models can be
exploited to boost the accuracy of the ML classifiers. We find that while this
knowledge improves the accuracy marginally for small datasets, it does not
improve accuracy with if larger datasets are used for training the models.
