Our work is inspired by the so called Hit Song Science, whose pioneer is the music entrepreneur Mike McCready. The Hit Song Science aims at predicting whether a song will be a hit before its distribution, by analyzing its audio features through machine learning algorithms.
Research question: "Can we accurately predict whether a song will be a hit knowing some of its audio features?"
Statistical Learning and Machine Learning techniques for classifications:
- Logistic Regression, Lasso Logistic Regression
- Support Vector Machines
- Decision trees
- Random Forests
https://www.kaggle.com/theoverman/the-spotify-hit-predictor-dataset
The Code is written in R 4.0.3.
- tree 1.0.40
- ISLR 1.2
- ggpubr 0.4.0
- ggplot2 3.3.3
- glmnet 4.1
- MASS 7.3.53
- randomForest 4.6.14
- e1071 1.7.4
- gbm 2.1.8
- caret 6.0.86
- dplyr 1.0.3
- reshape2 1.4.4
- scales 1.1.1
- pheatmap 1.0.12
- Marta Fattorel (marta.fattorel@studenti.unitn.it)
- Fabio Taddei Dalla Torre (f.taddeidallatorre@studenti.unitn.it)
This work is available under the Creative Commons Attribution-ShareAlike License. Read more about this license from https://creativecommons.org/licenses/by-sa/3.0/.