Welcome to the Visualization Dashboard for Mental Health and Music Features Analysis!
This project is an innovative tool designed to bridge the gap between mental health metrics and music features, offering users a comprehensive platform to explore, analyze, and visualize data interactively. By leveraging advanced data processing techniques and machine learning models, this dashboard empowers users to uncover meaningful insights and patterns.
Watch the project in action: Mood in the Music - Visualization Dashboard Demo
The dashboard provides an intuitive interface for exploring the intricate relationships between mental health indicators and musical attributes.
Whether you're a researcher, data enthusiast, or mental health advocate, this tool equips you with the resources to delve deep into the data and generate actionable insights.
- Dynamic Visualizations: Interactive charts and graphs that bring your data to life.
- Advanced Filtering Options: Apply filters for specific regions, clusters, or metrics.
- Predictive Analytics: Use machine learning models to predict mental health trends based on music features.
- Exportable Insights: Save your visualizations and summaries for offline use.
- Clustering and Dimensionality Reduction: Explore data clusters and reduce complexity using PCA and K-Means.
- Geospatial Analysis: Visualize mental health metrics on choropleth maps with GeoJSON integration.
This dashboard is user-friendly, making it accessible to both technical and non-technical audiences.
Mental health and music are deeply interconnected, with music often serving as a therapeutic medium.
By analyzing these relationships, this project aims to:
- Provide insights into how musical features influence mental well-being.
- Enable data-driven decision-making for mental health interventions.
- Foster a better understanding of global mental health trends through music.
The dashboard integrates a robust data processing pipeline with state-of-the-art visualization techniques.
Users can interact with the data through an intuitive interface, apply filters, and generate insights in real time.
The underlying machine learning models enhance the analytical capabilities, offering predictions and clustering for deeper exploration.
- Data Filtering: Remove unnecessary features for focused exploration.
- Decoupling Multi-Value Features: Split multi-value columns for granular analysis.
- Linguistic Feature Analysis: Use NLP to standardize and categorize genres.
- Normalization: Scale numeric values to a common range.
- Data Merging: Combine all datasets into a unified file.
- Handling Missing Values: Fill gaps using linear regression.
- Duplicate Removal: Eliminate redundant records.
- Data Sampling and Clustering: Apply PCA and K-Means to reduce dataset size while preserving key patterns.
This ensures clean, consistent, and analysis-ready data.
- Python 3.8 or higher
- pip (Python package manager)
git clone https://github.com/amirhnajafiz/visualization-dashboard.git
cd visualization-dashboard
python3 -m pip install -r requirements.txt
cd dashboard
python3 app.py --reload=True --debug=False --port=5000