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Motion Picture Revenue Prediction
An Artificial Neural Network Method for Predicting Opening Weekend
Box-Office Revenue
ECE 539 – Fall 2001
Final Project
Chad M. Steighner
ID: 253-699-5562
Concept
Use opening weekend revenues from 1989 through 2000 to train a MLP with back-propagation for classification into 5 classes.
Input features include: Genre, MPAA rating, Date, # Screens, Critical Rating,
Distributor, Run-time and Weekend Length
The MLP is then tested with motion picture data from 2001.
Implementation
Found 473 films with opening weekend data (www.boxofficeguru.com)
Used www.imdb.com to obtain add’l fields
Created Parsedata.java to construct TrainingData and TestingData for MLP
• 432 Training (1989 – 2000)
• 41 Testing (2001)
Through testing and 3-way cross validation found the best set-up to be:
3 Layer MLP (1 hidden) 6 hidden neurons Learning rate = 0.1 Momentum = 0.9 1000 Epochs
Results
45.6% avg. class. of 2001 films. (5.87 St.dev) $10-$12M, $12-$14M, $14-$17M, $17-$28M, $28M+
No Exact Replica Baseline Study: Nat’l Research Group (LA)
- telephone surveys to within 5% of opening weekend revenue. Moviefone claims to be even closer (movie info website) Prof. Arthur De Vany (UC-Irvine)
Bose-Einstein distribution of particles falling into urns. Equally likely particles (audience) will fall into a few urns
(movies) as it is for them to be distributed in any other way.