  In this paper, we have discussed initial findings and results of our
experiment to predict sexual and reproductive health vulnerabilities of
migrants in a data-constrained environment. Notwithstanding the limited
research and data about migrants and migration cities, we propose a solution
that simultaneously focuses on data gathering from migrants, augmenting
awareness of the migrants to reduce mishaps, and setting up a mechanism to
present insights to the key stakeholders in migration to act upon. We have
designed a webapp for the stakeholders involved in migration: migrants, who
would participate in data gathering process and can also use the app for
getting to know safety and awareness tips based on analysis of the data
received; public health workers, who would have an access to the database of
migrants on the app; policy makers, who would have a greater understanding of
the ground reality, and of the patterns of migration through machine-learned
analysis. Finally, we have experimented with different machine learning models
on an artificially curated dataset. We have shown, through experiments, how
machine learning can assist in predicting the migrants at risk and can also
help in identifying the critical factors that make migration dangerous for
migrants. The results for identifying vulnerable migrants through machine
learning algorithms are statistically significant at an alpha of 0.05.
