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Classification of movies as 'Fresh', 'Rotten', 'Certified-Fresh' using categorical predictors as well as review sentiment. Performed feature encoding and used Decision Tree, Random Forest Classifiers. Tackled class imbalance issues by assigning weights to classes. Used tokenization to generate word vectors for reviews to predict movie status.

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.gitignore		.gitignore
README.md		README.md
rotten_tomatoes.ipynb		rotten_tomatoes.ipynb
rotten_tomatoes_critic_reviews_50k.csv		rotten_tomatoes_critic_reviews_50k.csv
rotten_tomatoes_movies.csv		rotten_tomatoes_movies.csv

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rotten-tomatoes-movie-classification

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Classification of movies as 'Fresh', 'Rotten', 'Certified-Fresh' using categorical predictors as well as review sentiment. Performed feature encoding and used Decision Tree, Random Forest Classifiers. Tackled class imbalance issues by assigning weights to classes. Used tokenization to generate word vectors for reviews to predict movie status.

nlp random-forest decision-trees tokenization feature-encoding class-imbalance-problem

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