kevork
Uploaded by
10 SLIDES
202 VUES
100LIKES

Predicting Album Sales with Artificial Neural Networks

DESCRIPTION

Explore predicting album sales using MLP network to determine key factors influencing sales, utilizing critical acclaim, hype level, and previous sales data for classification. Results indicate 60% classification rate. Future work includes more detailed analysis of feature vectors and potential reduction to two classes.

1 / 10

Télécharger la présentation

Predicting Album Sales with Artificial Neural Networks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Sophomore Slumpware Predicting Album Sales with Artificial Neural Networks Matthew Wirtala ECE 539

  2. Overview • Record sales have decreased ~30% over the past 4 years • No consensus on why this is • File-sharing? • Inferior albums being released?

  3. Overview • Perhaps album sales can be predicted with an MLP network • May show what factors determine how well an album will sell • Indicate which albums deserve a better marketing push

  4. Feature data • Critical acclaim • Review scores gathered from 4 sources • www.pitchforkmedia.com • www.allmusic.com • www.metacritic.com • Rolling Stone

  5. Feature data • Hype level • Amount of press coverage will lead to higher public awareness and possibly higher album sales • Previous album sales • Serve as barometer of how established an artist may be.

  6. Data labelling • Too difficult to predict exact album sales • Data labelled as one of three classes • Albums that sell fewer than 500,000 copies • Gold albums (500,000 – 1,000,000 copies) • Platinum albums ( > 1,000,000 copies sold)

  7. Data preprocessing • Data gathered for 60 albums • 20 from each class • Some from same artist falling into separate classes • Data randomized and split into three partitions • Feature vectors normalized to -5 - +5

  8. The Neural Network • Utilized Professor Hu’s standard bp.m algorithm • Trialed many different configurations • Optimal configuration • 2 hidden layers • 7 neurons in first layer, 8 in second • Learning rate = 0.267, momentum = 0.007 • Tested with 3-way cross validation

  9. Results • Highest classification rate 60% • Correctly classified class 1 and 2 albums with 80-90% accuracy • Could not separate class 2 albums • Class 2 featured albums with vectors similar to those of classes 1 and 3 • Sample confusion matrix: 4 0 2 2 0 5 1 0 6

  10. Future Improvements • Further analysis of feature vectors to determine possible differences in class 2 albums • Possible reduction of labelling to two classes (combine Gold and Platinum) • Classification does show that predictions can be made based on the features considered in this study

More Related