Predicting Music Popularity: A Machine Learning Approach Using

Spotify Data

Shuo Jiang

Computer Science, Huazhong University of Science and Technology, Wuhan, China

Keywords: Machine Learning, Computer Science, Artificial Intelligence, Deep Learning.

Abstract: In today's world, with the continuous advancement and application of streaming technologies, music has

become ubiquitous and is increasingly integrated into the daily lives of people. This paper examines the

application of machine learning algorithms for predicting music popularity through an extensive dataset

sourced from Spotify, comprising 114,000 songs recorded over two decades. Traditional methods of

predicting song success have often been subjective and inaccurate; however, advancements in artificial

intelligence (AI) offer new avenues for improvement. This paper employed three machine learning models—

Random Forest Regressor, Simple Linear Regression, and Gradient Boosting Machines—to analyze various

audio features and their influence on song popularity. The Random Forest Regressor surfaced as the most

effective model, capturing complex relationships within the data and achieving a respectable R² score. The

findings highlight key predictors of popularity, including danceability, energy, and loudness, while also

revealing challenges in accurately forecasting songs at both ends of the popularity spectrum. This research

highlights the significance of incorporating various elements, including marketing tactics and social media

engagement, in addition to audio characteristics, to improve predictive accuracy. Ultimately, the study

showcases the capability of machine learning methods in grasping the intricacies of music popularity

dynamics.

1 INTRODUCTION

Music genres, much like trends, continually evolve in

response to changing times and public tastes. The

popularity of songs can fluctuate not only year by

year but also month by month, making the prediction

of music popularity a compelling area of study.

Traditional predictive methods have often been

subjective and data-deficient, leading to inaccuracies.

However, advancements in Artificial Intelligence

(AI) have introduced high-performance algorithms

that enhance prediction accuracy and adaptability,

making AI increasingly relevant in this field.

Recent progress in machine deep learning,

especially with Convolutional Neural Networks

(CNNs) and Recurrent Neural Networks (RNNs), has

significantly improved AI capabilities, particularly

with large-scale data processing. Reinforcement

Learning (RL) has also shown remarkable potential in

autonomous decision-making. The success of

AlphaGo exemplifies RL's impact, contributing to the

https://orcid.org/0009-0006-7267-2908

broader adoption of AI across diverse domains,

including music.

For instance, omar et al. presents a categorization

of AI techniques employed in algorithmic music

composition., which focuses on the automatic

generation of music by computer systems. The

application of AI in this field involves utilizing

various techniques as primary tools for creating

compositions (Lopez-Rincon, 2018); In a

heterogeneous network framework, Huan et al.

organized the digraph set of track characteristics into

multiple clusters, maximizing the diversity within

each cluster, ensuring that the digraphs within each

cluster are maximally isomorphic. By focusing

similarity searches within the most relevant cluster to

the target user, this approach enhances the efficiency

of music recommendations by providing a sufficient

selection of applicable tracks (Wang, 2022); Artistic

style transfer is a fascinating application of generative

AI that merges the content of one image with the

artistic style of another, creating distinctive and

imaginative visual artworks. Jonayet et al. present a

324

Jiang, S.

Predicting Music Popularity: A Machine Learning Approach Using Spotify Data.

DOI: 10.5220/0013330000004558

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Modern Logistics and Supply Chain Management (MLSCM 2024), pages 324-328

ISBN: 978-989-758-738-2

new approach to style transfer that employs CNNs

(Miah, 2023); Gauri et al. summarized the research on

using artificial intelligence technologies to filter,

diagnose, monitor, and disseminate information

about COVID-19 through human audio signals.

This overview will help develop automated

systems to support COVID-19 related efforts to

utilize non-invasive and user-friendly biosignals in

human non-verbal and verbal audio (Deshpande,

2022); One of the most important directions for this

is the prediction of music popularity, and here are

some examples: HuaFeng et al. developed a model for

predicting song popularity that combines multimodal

feature fusion with LightGBM. The model consists of

a LightGBM component, a multimodal feature

extraction framework and a logistic regression

component (Zeng, 2022); Notably, the research by

Seon et al. empirically examined how acoustic

features enhance the likelihood of songs reaching the

top 10 on the Billboard Hot 100, analyzing data from

6,209 unique songs that appeared on the chart

between 1998 and 2016, with a particular emphasis

on acoustic features supplied by Spotify (Kim, 2021);

In the research by Bang Dang et al., the paper focuses

on predicting the rankings of popular songs for the

next six months. The dataset, used for the Hit Song

Prediction problem in the Zalo AI Challenge 2019,

includes not only songs but also details like

composers, artist names, release dates, and more. The

paper advocates for treating hit song prediction as a

ranking problem using Gradient Boosting techniques,

rather than the typical regression or classification

methods employed in previous studies. The optimal

model demonstrated strong performance in predicting

whether a song would become a top Ten dance hit

versus lower-ranked positions (Pham, 2020).

Thanks to the robust development in this field,

this paper also aims to employ AI algorithms for

popularity prediction. To achieve this objective, the

study utilizes extensive streaming data, including

official metrics such as Spotify's track play counts

and datasets from Kaggle relevant to the model.

Experimental results validate the effectiveness of the

proposed methods.

2 METHODS

2.1 Dataset Preparation

The Dataset which this paper picked was a Spotify

Songs dataset that recorded 114,000 songs with their

popularity, artists, genre, duration, etc.

These features can be used to predict a song's

popularity and also to explore how these features

influence that popularity. Additionally, this study

conducted an online search for streaming play counts

and popularity data for singles from 2004 to 2024. To

account for regional differences, data was collected

primarily from Spotify, YouTube Music, and QQ

Music. These datasets were used as another critical

source of information. Utilizing these datasets, the

study conducts a regression task to examine the

relationship between play counts and a song's

popularity.

After cleaning the data, this paper selected

features that were not popularity to become the

independent variables. Then, were selected only the

popularity as out dependent variable since its the

target that this study aims to predict. In terms of data

preprocessing, this paper conducted normalization

training. To properly evaluate the model's

performance, it's important to split the dataset into

training and testing sets. This paper makes use of the

"train-test-split" function from the

“sklearn.model_selection” module, allocating 80% of

the data to the training set and 20% to the testing set.

2.2 Machine Learning Models-Based

Prediction

About the models this study chosen, this paper

selected three different models. They are Random

Forest Regressor(RF),Gradient Boosting Machines

(GBM) and Simple Linear Regression.

2.2.1 Random Forest

Firstly, RF shown in Figure 1 is an ensemble method

that constructs multiple decision trees and merges

their results. It leverages bootstrapping and feature

randomness to enhance model performance and

reduce overfitting. It's Methodology including

Ensemble Construction which generates multiple

decision trees using bootstrap samples from the

training data. Besides, each tree is trained on a unique

subset of the data, which aids in minimizing variance

and preventing overfitting. The reasons of why this

study chose it are as follows: 1. powerful ensemble

learning method; 2. It is capable of effectively

handling both linear and non-linear relationships; 3. it

offers robustness against overfitting, especially in

datasets with many features.

Predicting Music Popularity: A Machine Learning Approach Using Spotify Data

325

Figure 1: The structure of the RF (Muchisha, 2021).

2.2.2 Linear Regression

Simple Linear regression is important in modeling,

encompassing model specification, model estimation,

statistical inference, model diagnostics and prediction

(Su, 2012). It is a commonly employed method in

statistical modeling and machine learning for

forecasting a continuous response variable using one

or more input variables. Its objective is to uncover the

linear relationship between the variables by

minimizing the discrepancy between observed and

predicted values. The model could be showed as:

𝑌𝛽0 𝛽1𝑋1 𝛽2𝑋2 ⋯𝛽𝑝𝑋𝑝𝜖. (1)

𝛽0 is the intercept, β

are the coefficients, 𝑥



are independent variables, Yis the dependent variable

and ϵ is the error term. Its target is to estimate βthat

best fits the observed data. A widely used approach

for estimating coefficients is Ordinary Least Squares

(OLS). Mathematically, the objective is to minimize:

∑

Y

 Y



i1

(2)

The reasons this study chose Simple Linear

Regression was because of its simplicity and

interpretability.

2.2.3 Gradient Boosting Machines

Lastly, this paper selected Gradient Boosting

Machines (GBM). GBM encompass a group of

effective machine-learning methods that have

achieved significant success across various real-

world applications. They can be tailored to meet

specific application needs, including the ability to be

trained with different loss functions (Natekin, 2013).

It is a robust ensemble learning technique that

constructs predictive models by sequentially

incorporating weak learners and enhancing their

performance using gradient descent. The GBM

algorithm follows a boosting framework where the

model is constructed in a sequential manner. In every

iteration, a new weak learner is fitted to the residuals

from the current ensemble's predictions. The final

model aggregates all weak learners, with each

weighted according to its effectiveness.

Mathematically, the model can be showed as:







∑

m1

x (3)





is the final model prediction,

ℎ

x

represents the m  th weak learner, and α

is the

weight associated with the m  th learner.GBM

employs gradient descent to minimize a specified loss

function. In each boosting iteration, it evaluates the

gradient of the loss function relative to the current

model's predictions, subsequently training a new

weak learner to approximate this gradient, thereby

effectively diminishing residual errors.

It's update rule for the model can be showed as:

m1

xF

x 𝜂∙F

m1

(x) (4)

𝜂 is the rate of learning, which regulates the

contribution of each new learner. Of course, to

prevent overfitting and enhance generalization, GBM

incorporates regularization technique such as pruning

the decision trees and incorporating constraints on

tree depth or leaf nodes.

GBM offers several advantages: it builds models

sequentially and its high predictive accuracy. Also,

GBM offers insights into feature importance, which

helps in understanding and interpreting the model.

3 RESULTS AND DISCUSSION

Regarding model performance, the final Random

Forest Regressor was assessed on the test set, yielding

promising results.

3.1 Random Forest Performance of

Regression

The key performance metrics are as follows:

Figure 2: The prediction performance of the RF

(Photo/Picture credit: Original).

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326

From the Figure 2, the model achieved an R²

score of 0.6137437848169063. In addition, an MSE

of 190.61005900977344 was obtained.

High Popularity Predictions: The model exhibited

a tendency to underpredict several popular songs.

This discrepancy may arise from the unique traits or

marketing strategies associated with these tracks,

which were not fully captured by the audio features

used in the model. As a result, the model may have

overlooked important factors influencing a song's

popularity, such as cultural context or promotional

efforts.

Low Popularity Predictions: On the other hand,

the model overpredicted the popularity of certain low-

scoring tracks. This issue could be attributed to data

noise or the misalignment of niche genres with

mainstream metrics. Tracks from less popular genres

may not align well with the features that typically

drive popularity in more mainstream contexts,

leading to inaccurate predictions. This highlights the

need for a more nuanced approach to feature selection

and model tuning, especially when dealing with

diverse musical styles.

3.2 Learning Curve

Learning Curve is shown in Figure 3 follows:

Figure 3: The learning curve (Photo/Picture credit:

Original).

The training error is close to zero, but the validation

error stays high. The validation error does not change

much as the size of the training set increases.

3.3 Random Forest Performance of

Classification

This study initially evaluated the model's

performance with varying numbers of classes.

Figure 4 is the confusion matrix when the whole

popularity is classified into 4 parts.

Figure 4: The confusion matrix of 4 parts (Photo/Picture

credit: Original).

Figure 5 is the accuracy when the whole

popularity classified into 3 parts.

Figure 5: The confusion matrix of 3 parts (Photo/Picture

credit: Original).

Figure 6 the confusion matrix when the whole

popularity is classified into 2 parts.

Figure 6: The confusion matrix of 2 parts (Photo/Picture

credit: Original).

For feature importance and performance analysis,

the results can be found in Table 1 and Figure 7.

Predicting Music Popularity: A Machine Learning Approach Using Spotify Data

327

Table 1: The performance of the model.

Precision Recall F1-

score

Support

1 0.76 0.84 0.80 11164

2 0.77 0.74 0.76 10181

3 0.76 0.33 0.46 1455

accurac

0.76 22800

Macro av

0.76 0.64 0.67 22800

Weighted

avg

0.76 0.76 0.76 22800

Figure 7: The feature importance (Photo/Picture credit:

Original).

To sum up, while the model performed well overall,

the error analysis revealed some challenges in

accurately predicting songs at the extremes of the

popularity spectrum. This indicates that while audio

features are important, other factors like marketing

efforts, artist reputation, lyrics, and social media

presence may also impact popularity.

Additionally, this paper explored the use of a

Random Forest Classifier for this task, which

provides approximate popularity levels for each song

and achieved acceptable accuracy. The analysis

emphasized important features like energy, loudness,

danceability and valence as crucial indicators of a

song's success.

4 CONCLUSIONS

This study effectively built machine learning models

to predict song popularity using data sourced from

Spotify. After thoroughly evaluating several

candidate models, this study ultimately preferred the

Random Forest Regressor for its outstanding ability

to capture the complex relationships between audio

features and song popularity effectively. Its

performance in modeling these complexities stood

out, making it the ideal choice for the analysis. This

model demonstrated strong performance in accuracy

and achieved a notable R² score, reflecting its ability

to account for a considerable amount of the variance

in song popularity.

The analysis revealed that the model effectively

identified patterns within the data, allowing for

meaningful predictions. However, it also highlighted

the challenges of accurately forecasting popularity for

songs at both ends of the popularity spectrum. While

audio features play a crucial role, the model suggests

that other factors—such as marketing strategies，artist

reputation and social media presence — may also

significantly influence a song's success.

Overall, the research findings provide valuable

insights into the dynamics of music popularity and

underscore the potential of machine learning

techniques in this field.

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