• A Shiny application that has widget input, ui input in server.R, reactive output using server calculations, and supporting documentation.

• A Presentation that contains five slides in R Presentation.

• All related souce codes are uploaded to Github.

https://github.com/Yanyul/datasciencecoursera/tree/master/Data%20Sicence%20Product/Sportify%20Interactive%20Analysis%20(Shiny%20%26%20Presentation)/Sportify

• The application was designed to interactively predict whether certain attributes of a song will be liked or not.

• Randomn Forest Classifier was applied to predict the result based on trained model.

• Music composers might find it extremely useful for their future composition.

• Attributes include : acousticness, danceability, duration_ms, energy, instrumentalness, key, liveness, loudness, mode, speechines, tempo, time_signature, valence, target

• Diamond data is from Kaggle Website, some useless predictors are removed in advance

• Specify the predictor that were factor and change the datatype to integer (in ui.R input)

Model: randomForest(target~.,data=sportify,mtry=10,proximity=TRUE)

Now you are able to identify what combination of attributes could yield a popular song!!!!