The project involved conducting a data analysis on my Spotify data. The analysis was performed using various data analysis techniques and tools to gain insights into my listening habits and preferences on the platform.
In this project, The data was first collected by extracting my listening history from my Spotify account, which included details such as song names, artist names, and time stamps. This data was then cleaned and processed to remove any duplicates or irrelevant information. The analysis included exploratory data analysis techniques, such as creating visualizations to understand patterns and trends in the data. This involved using tools like Python and Tableau to generate charts and graphs, highlighting my most played artists, genres, and songs. Further analysis was conducted using statistical methods to identify patterns and correlations in the data. For example, regression analysis was used to investigate the relationship between my listening habits and external factors like time of day or day of the week. In the End, a recommender system was created using machine learning techniques, which used my listening history to recommend new songs based on my preferences.
- Pandas
- plotly
- Seaborn
- matplotlib
- requests
- sklearn
Amirhossein Zarabadipour
- 0.1
- Initial Release