How Does the Challenge Work?

• In this challenge, you'll build a natural language processing (NLP) model that can automatically analyze the sentiment of movie reviews
• Your model will take a movie review (text) as input and predict whether the sentiment is positive or negative. This is a classic classification problem and a perfect introduction to NLP, text preprocessing, and modeling.

Practice Skills

• Python
• Text classification techniques, feature engineering

Evaluation

Goal

• It is your job to predict whether the movie review is positive or negative

Metric

• Accuracy = Correct Predictions / Total Predictions

Submission File Format

Submission guide

Your final submission file must be a CSV file with the following structure:

review_index	sentiment
35000	negative
35001	positive
35002	negative
35003	positive
35004	positive
35005	negative
35006	positive

• Include the header row.
• Make sure the review_index matches the review_index values from the test dataset.

The challenge includes two types of submissions:

Public Submission

• Use the public testing dataset (provided in the Data section) to predict your submission file.

Private Submission

• You must submit your model.
• It will be evaluated on a public testing dataset.
• Important: Your code must recursively scan the entire data directory (Code section for details).