Spotify is a music streaming service with over 365 million active listeners worldwide and generates lots of data on user behavior and music trends. Analyzing data can help provide more insight the factors that contribute to the popularity of certain music genres on Spotify can provide valuable insights for music industry professionals, artists, and users alike. By analyzing data on the characteristics of popular songs across different genres, we can identify trends and patterns that may inform marketing strategies, music production, and playlist curation.
By analyzing this data, we aim to gain insights into user behavior patterns, popular music genres, and popular artists. The goal of this project is to identify and use a relationship between spotify's factors and the type of music genre.
Analyzing Spotify data can provide valuable insights into user behavior patterns, such as music preferences and listening habits. By analyzing the data, we can identify patterns and trends that contribute to the popularity of certain genres, which can inform marketing and production strategies and potentially lead to increased revenue for the music industry.
We will use a Spotify - Popularity Classification Models to observe the following features for each song:
| title | artist | top genre | year | # Beats Per Minute | Energy | Danceability | Loudness (dB) | Liveness | |
|---|---|---|---|---|---|---|---|---|---|
| Sunrise | Norah Jones | adult standards | 2004 | 157 | 30 | 53 | -14 | 11 | |
| Black Night | Deep Purple | album rock | 2000 | 135 | 79 | 50 | -11 | 17 | |
| Clint Eastwood | Gorillaz | alternative hip hop | 2001 | 168 | 69 | 66 | -9 | 7 | |
| The Pretender | Foo Fighters | alternative metal | 2007 | 173 | 96 | 43 | -4 | 3 |
Analyzing the Spotify dataset may encounter several challenges, such as incomplete or missing data, errors or inconsistencies in data or biased sampling impact the data.
It can also be difficult to determine which factors to choose that contribute to the popularity of a particular music genre, as there are many variables to consider, such as the time of year, # beat per minute, energy, danceability, loudness, and liveness.
We will use machine learning and statistical analysis techniques to identify the key factors that contribute to the popularity of certain music genres, such as tempo, mood, and instrument usage. We will then create visualizations to demonstrate our findings and provide insights to inform the music industry's decision-making processes.
We plan to use machine learning algorithms such as clustering and classification to analyze Spotify data and gain insights into user behavior and music trends.
We also plan to use visualization tools to help us gain insights into the data. For example, we may use scatter plots to visualize the relationship between different song features such as loudness and liveness and other factors.