Comparison: Machine Learning Models

Binary Classification--Success or Fail
Random Forest, Logistic Regression, Support Vector Machine (SVM), and K Nearest Neighbor (KNN) models were used to classify the movie data as successful or failed movies.
Random Forest had the highest testing data score of 0.896, so for this dataset, we would recommend using this machine learning model.
However, KNN had the lowest score of 0.840, so given the small range of difference between all four models, any of these options would be acceptable to use.

Multi-label Classification--Rating Integer of 2-9
KNN and a Neural Network were used to classify the movies into specific rating integers ranging from 2 to 9. These models were much less successful and only predicted the correct score about half of the time.
The most successful model was the Neural Network with one hidden layer which had a score of 0.546. The least successful model was KNN which only had a score of 0.456.