What makes some songs become popular? The dataset describes popular songs based on auditory features such as loudness and tempo.
Construct a model using a dataset of popular songs to predict ratings based on auditory features of the songs included in scoringData.csv. (You may use linear regression, logistic regression, feature selection e.g. Lasso, decision tree or advance tree)
Submissions will be evaluated based on RMSE (root mean squared error). Lower the RMSE, better the model.
The submission file should be in text format (.csv) with only two columns, id and rating. The rating column must contain predicted rating. The number of decimal places to use is up to you. The file should contain a header and have the following format:
An example submission file (example_submission.csv) is shared with the set of files under Data.
Here is an illustration in R of how you can create a model, apply it to scoringData.csv to prepare a submission file.
# ensure analysisData.csv and scoringData.csv are in your working directory
# following code will read data and construct a simple model
songs = read.csv(‘analysisData.csv’)
model = lm(rating~ tempo+time_signature,songs)
# read in scoring data and apply model to generate predictions
scoringData = read.csv(‘scoringData.csv’)
pred = predict(model,newdata=scoringData)
# construct submission from predictions
submissionFile = data.frame(id = scoringData$id, rating = pred)
write.csv(submissionFile, ‘sample_submission.csv’,row.names = F)
* Disclaimer: This data is to be used solely for the purpose of the Project for this course. It is not recommended for any use outside of this competition.
By the end of the competition, you must have at least 3 submissions. At least one must use a forest ranger model.
Attached File Descriptions:
analysisData.csv: Data for building a model
scoringData.csv: Use for applying predictions or scoring
example_submission.csv: Sample submission file in the desired format
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