This is a text classification task - sentiment classification. Every document (a line in the data file) is a movie review extracted from IMDB. Your goal is to classify the sentiment of each review into "good" or "bad".

The training data contains 40,000 reviews, already labeled with 1 (representing a "good" sentiment) or 0 (indicating a "bad" sentiment). The test data contains 10,000 reviews that are unlabeled. The submission should be a .csv (comma-separated free text) file with a header line "Id,Category". Each subsequent line should contain exactly two integers, separated by a comma. The first integer is the line ID of a test sentence (ranging from 0 to 9,999), and the second integer is the category your classifier predicts, either 0 or 1.

You can make 10 submissions per day. Once you submit your results, you will get an accuracy score computed based on 90% of the test data. This score will position you somewhere on the leaderboard. Once the competition ends, you will see the final accuracy computed based on 100% of the test data. The evaluation metric is accuracy - so aim for the highest score possible.