Data Analysis Project: Relationship between Music and Mental Health

JB Music Therapy

Project Background and Dataset Overview:

A survey was conducted to access the impact of music on people’s mental health and mood in general. The survey recorded responses from over 700 participants and includes details like musical preferences, listening habits, and overall state of mind.

Respondents were given 16 genres of music for which they ranked the frequency of listening to each. The responses ranged from never, to very frequently. The respondents also ranked the intensity of their feelings of anxiety, depression, insomnia, and OCD on a scale of 1 to 10. 0 being the lowest in intensity to 10 being the highest.

The dataset was provided by Qureous from JB Music Therapy

Project Objectives

1. Data Cleaning and Processing: Clean and prepare the data for analysis.
2. Data Visualization: Visualize the data to make it easy to understand.
3. Data Reporting: Use the insights and correlations within the data to make recommendations.

About the Company

JB Music Therapy (largest employer of Music Therapists in Canada) is a team of Certified Music Therapists who collaborate, design, and personalize therapy with their clients that will foster desired change: boost mood, decrease stress, strengthen relationships, increase focus, develop learning, reduce anxiety, rehab after an injury, and recover lost memories.

The core services they offer are;

1. Individual therapy
2. Group programs
3. Public Education

Link to their website is here: JB MUSIC

A- Data Cleaning and Processing

After downloading the Excel file, I started by reviewing the dataset with pivot data, sort and filter functions to understand the entities and range in each column. Then, I proceed to distinguish the column headers and fix them to right formats, and carried out these:

1. Removed duplicates and outliers (like Beat Per Minute of 900 and listening hours of 24).
2. Trimmed white spaces and change to Proper text format with TRIM(PROPER()) Function for text format columns
3. Used the nested (IF(AND()) Function to categorize age and intensity of feelings’ into groups.

Examples:

For Age; 0–19 as Children and Adolescents, 20–29 as Young Adults

For Feelings; 1–4 as Mild, 4–6 as Moderate, and 7–10 as Severe

B- Data Visualization

I used EXCEL to create a 5 dashboards for: Dataset Overview, Anxiety, OCD, Insomnia, and Depression. Shown as follows:

Dashboards

C-Insights and Recommendations

Insights

1. Majority of respondents use Spotify streaming service.
2. The dataset significantly represents Children, Young Adults, and Adults which can be associated to the popularity of music genre in vogue presently.
3. Rock, Pop, Metal, Classical, EDM, and Video Game Music are the top most common music genres and their effectiveness on enhancing mental health is also in that order.
4. Listening hours has a positive correlation with mental health improvement and listening hours increases steadily from Elderly to Children and Adolescents.
5. Improvements were highest at severe level of disorder except for OCD where more improvements were from the mild level.
6. Disorder level gets more intense from Elderly to Children and Adolescents.

Recommendations

1. JB Music Therapy can create the recommended playlists on Spotify and run their ads on the platform.
2. Strategic plans can be created to include more age group in their customer base.
3. They can advice their clients to listen more the musics recommended for more effective actions.

I appreciate #Querous for the opportunity to work on this project.

Qureous is a great platform where you can greatly develop your data analysis skills through working on amazing projects, mentorships and job searching.
I am going to share more info about Qureous soon!