  The rapid expansion in the usage of social media networking sites leads to a
huge amount of unprocessed user generated data which can be used for text
mining. Author profiling is the problem of automatically determining profiling
aspects like the author's gender and age group through a text is gaining much
popularity in computational linguistics. Most of the past research in author
profiling is concentrated on English texts \cite{1,2}. However many users often
change the language while posting on social media which is called code-mixing,
and it develops some challenges in the field of text classification and author
profiling like variations in spelling, non-grammatical structure and
transliteration \cite{3}. There are very few English-Hindi code-mixed annotated
datasets of social media content present online \cite{4}. In this paper, we
analyze the task of author's gender prediction in code-mixed content and
present a corpus of English-Hindi texts collected from Twitter which is
annotated with author's gender. We also explore language identification of
every word in this corpus. We present a supervised classification baseline
system which uses various machine learning algorithms to identify the gender of
an author using a text, based on character and word level features.
