The Effect of Music on Mental Health¶

Project Proposal

data set: https://www.kaggle.com/datasets/catherinerasgaitis/mxmh-survey-results/code?resource=download

Part 1: Describes and motivates a real-world problem where data science may provide helpful insights. Your description should be easily understood by a casual reader and include citations to motivating sources or relevant information (e.g. news articles, further reading links … Wikipedia makes for a poor reference but the links it cites are usually promising).¶

Mental health is a prevalent issue and has had slow growth in innovation. One of the major issues is understanding drivers of mental health and observing trends.

Measuring mental health is difficult: https://www.dmagazine.com/healthcare-business/2021/05/can-we-objectively-measure-mental-health/

  • Lots of things even in someone's day-to-day activities can have an effect on their mental health: quality of sleep, exercise, stress,

Understanding mental health is extremely important --- can lead to serious issues if not treated.

Mental health statistics: https://www.nami.org/mhstats

  • 1 in 5 U.S. adults experience mental illness each year
  • 1 in 20 U.S. adults experience serious mental illness each year
  • 50% of all lifetime mental illness begins by age 14, and 75% by age 24

Understanding the causes/ drivers of mental health is imperative.¶

Part 2: Explicitly load and show your dataset. Provide a data dictionary which explains the meaning of each feature present. Demonstrate that this data is sufficient to make progress on your real-world problem described above.¶

In [5]:
import pandas as pd 
df_health = pd.read_csv('mentalhealth.csv')
df_health.head()
Out[5]:
Timestamp Age Primary streaming service Hours per day While working Instrumentalist Composer Fav genre Exploratory Foreign languages ... Frequency [R&B] Frequency [Rap] Frequency [Rock] Frequency [Video game music] Anxiety Depression Insomnia OCD Music effects Permissions
0 8/27/2022 19:29:02 18.0 Spotify 3.0 Yes Yes Yes Latin Yes Yes ... Sometimes Very frequently Never Sometimes 3.0 0.0 1.0 0.0 NaN I understand.
1 8/27/2022 19:57:31 63.0 Pandora 1.5 Yes No No Rock Yes No ... Sometimes Rarely Very frequently Rarely 7.0 2.0 2.0 1.0 NaN I understand.
2 8/27/2022 21:28:18 18.0 Spotify 4.0 No No No Video game music No Yes ... Never Rarely Rarely Very frequently 7.0 7.0 10.0 2.0 No effect I understand.
3 8/27/2022 21:40:40 61.0 YouTube Music 2.5 Yes No Yes Jazz Yes Yes ... Sometimes Never Never Never 9.0 7.0 3.0 3.0 Improve I understand.
4 8/27/2022 21:54:47 18.0 Spotify 4.0 Yes No No R&B Yes No ... Very frequently Very frequently Never Rarely 7.0 2.0 5.0 9.0 Improve I understand.

5 rows × 33 columns

In [8]:
dict_description = {'timestamp': 'Date and time when form was submitted', 'age': 'Respondents age', 
                   'primary streaming service': 'Respondents primary streaming service', 'hours per day': 
                   'Number of hours the respondent listens to music per day', 'while working': 'Does the respondent\
                   listen to music while studying/working?', 'instrumentalist': 'Does the respondent play an instrument regularly?',
                   'composer': 'Does the respondent compose music?', 'fav genre': 'users favorite genre', 'foreign languages':
                   'Does the respondent regularly listen to music with lyrics in a language they are not fluent in?', 'frequency \
                   (r&b, rap, rock, video game music)': 'frequency of listening to a specific genre', 'anxiety': 'user rating of anxiety',
                   'depression': 'user rating of depression', 'insomnia': 'user rating of insomnia', 'OCD': 'User rating of OCD',
                   'music effects': 'how does music help user mental health'}
dict_description
Out[8]:
{'timestamp': 'Date and time when form was submitted',
 'age': 'Respondents age',
 'primary streaming service': 'Respondents primary streaming service',
 'hours per day': 'Number of hours the respondent listens to music per day',
 'while working': 'Does the respondent                   listen to music while studying/working?',
 'instrumentalist': 'Does the respondent play an instrument regularly?',
 'composer': 'Does the respondent compose music?',
 'fav genre': 'users favorite genre',
 'foreign languages': 'Does the respondent regularly listen to music with lyrics in a language they are not fluent in?',
 'frequency                    (r&b, rap, rock, video game music)': 'frequency of listening to a specific genre',
 'anxiety': 'user rating of anxiety',
 'depression': 'user rating of depression',
 'insomnia': 'user rating of insomnia',
 'OCD': 'User rating of OCD',
 'music effects': 'how does music help user mental health'}
In [9]:
len(df_health)
Out[9]:
736

This data set came from a google form and has 736 responses. This allows for a deep analysis into how music effects mental health.

Part 3: Write one or two sentences about how the data will be used to solve the problem.¶

There are many variables in this data set. The data can be used to determine if listeners of certain genres of music are more likely to report mental health issues and if there is a correlation between certain genres and mental health disorders.

Another way the data can be used is to determine if age is correlated with self-reported positive effects when listening to music.

other examples:

  • correlation between hours listening to music / mental health
  • streaming device and mental health