Today we would like to present
Relationship between the Type of Music and Economic Conditions.
This is our agenda for the presentation.
Firstly, we will introduce our data set
and then see the meaning of variables as dictionary.
Third, we will talk about our hypothesis for our study.
Fourth, we will go through our background
and then the data processing part
such as changing categorical variables to continuous variables
and handling our missing values and outliers and then split the data set.
Next, we will discuss the best model in our dataset.
Lastly, we will give business values and recommendations.
In this study, we will analyze the song's popularity on Spotify in US
from 2006 to 2019 as well as economic indicators
such as GDP, inflation r ate, unemployment rate, and happiness variable.
In this dataset, we have 1,389 rows and 20 columns.
We have taken this dataset from Kaggle.
Furthermore, other datasets are Morris for economic indicator.
We can see more detailed information on the next page.
This is our dictionary of dataset.
The target variable is popularity
which means the higher the value, the more popular the song is.
It would be the continuous variable.
In the predictor variable, we have musical attributes
such as explicit, danceability, and loudness.
Also, we have four variables for economic indicators
which are GDP, happiness index, unemployment rate, and inflation.
Our hypothesis is that during times of economic downturn,
we suppose there is an increase in the popularity of upbeat
and energetic genres such as electronic dance music,
while during economic growth periods,
more soulful, introspective, thoughtful music
tend to dominate the charts.
We expect that it provides valuable insights into the culture
and economic factors that influence the music industry
and can assist music producers and marketers
in understanding consumer behavior and making informed decisions.
We are using 2006 to 2019 year in the US.
It shows that 2006 to 2007 is pre-recession time,
so economy was still stable.
2008 to 2009 went through a global financial crisis.
2010 to 2012 is post- recession recovery time.
In this time, the economy began recovering.
In 2013 to 2015 is steady recovery time.
In 2016 to 2017, the economy continued to grow.
Finally, 2018 to 2019 is late- stage growth time.
In this time, it was approaching a more mature and stable state.
We found that the worse the economy,
the more positive the type of music people listen to.
Based on this timeline, we can get a correlation with other variables.
As you can see in this graph, we can see that unemployment rate
is getting higher during 2008 to 2012 which is a global financial crisis.
Based on this hypothesis,
positive music will be popular for this time.
This is about the tempos and the years.
The lower value means the lower tempos and higher value means the higher tempos
which we thinks that lower tempos
may appear in the sad song or softer songs
and the higher tempos may appear in the inspiring songs or happier songs.
We look into 2006 through 2007 when the economy was growing and stable,
you could see that they accounted… The lower tempo have more…
Lower- tempo songs appeared more parts in this period.
If we look into 2013 through 2015 and 2016 through 2017
when the economy was growing, we could see that they also
have more lower- tempo songs compared with the other years.
If we look into 2008 to 2009 and 2010 through 2012
when the economy was really bad,
2008 through 2009 was the financial crisis
and 2010 through 2012, the unemployment rate was the highest.
You can see that higher- tempo songs have more parts in those years.
We got a conclusion that people might turn to listen to higher- tempo songs
when the economy was really bad
and they re turned to listen to the lower songs
when the economy was good.
Next page.
We look into valence and the years.
The lower valence means that it will be a negative songs
or a lot of softer songs,
and higher values means that it will be happier songs,
positive songs.
We look into 2008 through 2009 and 2010 through 2012,
economic bad period.
We could see that the positive songs appeared more parts in those periods.
In 2013 through 2015 and 2016 through 2017,
the economy was growing,
they accounted more part in the negative songs
which match our hypothesis, seems that people might listen
to more negative songs when the economy was good
and people want to listen to more inspiring songs or positive songs
when the economy was really bad.
Next Page.
Look into the total number of hit songs of each years.
We can see that through 2013 to 2017 the hip hop songs was increasing
and 2018 to 2019,
there have more genre songs appear in this period.
The hip hop songs and the pop song and the dance songs decreased,
but the Latin song and the R &B songs increased.
Next page.
It will be clear if we look into this graph,
we could see that the other genre of songs accounted more parts in 2018 through 2019.
If we look through the time period,
we could see that while the economy was growing, the other genres of songs
accounted more parts than the economic was really bad.
Next page.
When we compare with the valence and the GDP,
we may find that the valence and the GDP are negatively correlated to each other.
It seems that when the GDP was high
we will say that the economy was good and the valence will be lower.
It means that people may turn to listen to the negative songs
when the economy was really good,
but they will listen to the positive songs when the economy was really bad.
Next page.
Before we do our model,
we look into our dataset and we found t hat the genre
was the only categorical variable.
We transform it into seven columns of binary variables.
It will be blues, classical, country dance, easy listening, folk, and hip hop.
Next page.
We also deal with the missing value.
We found [inaudible 00:10:38] r ows. We excluded all of them.
The next, we looked at the outliers and their distribution.
It turns out the only column, instrumentalness, had 86 outliers.
We decided to transform the column
using SHASH transform to normalize the column
and on the right side, you can see that it's normalized.
Next page, please.
After cleaning the dataset, now it is time to split the dataset.
We divided 0.6, 0.2, and 0.2 to training, validation, and test dataset.
Next slide, please.
After applying to several different models such as decision tree, regression,
[inaudible 00:11:46] , KNN, neural network, we found that the decision tree
has the highest R Square
and it proves that it failed to reject our null hypothesis.
Next slide, please.
When you look close to the column contributions,
other than the music factors such as duration, acousticness, key,
we also found that unemployment rate,
GDP, and inflation rate also has positive correlation
with our target variable, popularity.
But among the economic factors, the unemployment rate
has the highest impact on the target variable.
But other than that,
the music factors such as duration, acousticness, key, loudness, and valence
are the top five contribution to our target variable.
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Based on our model's result, we can say that unemployment rate
has the highest correlation among economic factors with popularity,
but also other factors have positive correlation
with the target variable.
As unemployment rate and [inaudible 00:13:15] rate gets higher,
the songs that has high duration, acousticness, key, loudness,
valence, speechiness, energy, and danceability are popular.
Basically, the song that is longer, more brighter, and more danceability
has the higher popularity during those times.
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According to our study, we found that the economic impact
of the popularity of different types of songs
so we recommend that during the economic turn down,
we recommend that the music producers to make high- duration acousticness, key,
loudness, valence, speechiness, energy, and danceability music and vice versa.
Meanwhile, when the economy is booming,
we recommend the music producers to make low duration,
acousticness, key, loudness, valence, speechiness,
energy, and danceability music.
Next slide, please.
This is our reference.
This is pretty much everything we prepare for this presentation.
Thank you so much for listening.