  Legal predictive models are of enormous interest and value to legal
community. The stakeholders, specially, the judges and attorneys can take the
best advantages of these models to predict the case outcomes to further augment
their future course of actions, for example speeding up the decision making,
support the arguments, strengthening the defense, etc. However, accurately
predicting the legal decisions and case outcomes is an arduous process, which
involves several complex steps -- finding suitable bulk case documents, data
extracting, cleansing and engineering, etc. Additionally, the legal complexity
further adds to its intricacies. In this paper, we introduce our newly
developed ML-enabled legal prediction model and its operational prototype,
eLegPredict; which successfully predicts the Indian supreme court decisions.
The eLegPredict is trained and tested over 3072 supreme court cases and has
achieved 76% accuracy (F1-score). The eLegPredict is equipped with a mechanism
to aid end users, where as soon as a document with new case description is
dropped into a designated directory, the system quickly reads through its
content and generates prediction. To our best understanding, eLegPredict is the
first legal prediction model to predict Indian supreme court decisions.
