This project involves a detailed analysis of Spotify songs streamed from 2018 to 2023 using Power BI. The analysis provides insights into trends, popular tracks, artists, genres, and more, based on the streaming data collected over the years.
The data used for this analysis was sourced from Spotify's API, which provides information about songs, artists, albums, and user interactions. The dataset encompasses a wide range of attributes for each song, including but not limited to track name, artist name, release date, popularity, duration, and genre.
- Power BI: Used for data visualization and analysis, creating interactive reports and dashboards.
- Spotify API: Accessed to retrieve streaming data for analysis.
- Python: Potentially used for data preprocessing and cleaning before importing into Power BI.
- Microsoft Excel: Utilized for data preprocessing, cleaning, and potentially for some additional analysis or calculations before importing into Power BI.
- Trend analysis of streaming patterns over the years.
- Identification of most streamed songs, artists, and genres.
- Geographic analysis to understand regional preferences.
- Time series analysis to uncover seasonal trends and changes in listening habits.
- User engagement analysis based on factors like duration of listening sessions, repeat listens, etc.
- Clone the repository to your local machine.
- Import the provided dataset into Power BI.
- Open the Power BI file (
spotify2023Analysis.pbix) to explore the interactive visualizations and analysis. - Customize and extend the analysis as needed for your own insights.
Include screenshots or a brief demo video showcasing the interactive visualizations and key findings from the analysis.
This project is licensed under the MIT License.