1. Alex Larson ECE 539 Fall 2013

2. Reasons to Predict & Goal Movies are a large part of today’s culture Movies are expensive to make Goal: To predict a movie potential box office success based on its characteristics

3. Data Collection Movie statistics available online on various websites Random samples of top movies from recent years Sample data from the-numbers.com

4. Features and Classes Release Month Distributor Genre MPAA Rating (G, PG, etc…) Is Sequel? (Y/N) 3 Classes based on Gross in release Year Gross < $49,000,000 $49,000,000 < Gross < $91,000,000 $91,000,00 < Gross

5. Results So far Initial results: poor low classification rate Reevaluated data Removed various outliers Limit to top Distributors/Studios Simplified genre classification

6. Results So Far KNN Classifier Testing Data200820092010201120122013Average C Rate(%)48645256323648 Confusion Matrix 31127 241412 15827 Maximum Likelihood Classifier Testing Data200820092010201120122013Average C Rate(%)48565256482447.3 Confusion Matrix 34106 251015 111227 Confusion Matrix 231413 151817 13829 MLP back propagation Testing Data200820092010201120122013Average C Rate(%)5248 52404447.3

7. Results So far Correctly Predicted for 2013: Iron Man 3 Hunger Games Oblivion Incorrectly Predicted for 2013: Gravity After Earth The Internship

8. Possible Improvements Refine features more, add more features like budge, director, lead actors Try with different combination of features