Spotify Song Attributes

### Context

A dataset of 2017 songs with attributes from Spotify's API. Each song is labeled "1" meaning I like it and "0" for songs I don't like. I used this to data to see if I could build a classifier that could predict whether or not I would like a song.

I wrote an article about the project I used this data for. It includes code on how to grab this data from the Spotipy API wrapper and the methods behind my modeling.
https://opendatascience.com/blog/a-machine-learning-deep-dive-into-my-spotify-data/

### Content

Each row represents a song.

There are 16 columns. 13 of which are song attributes, one column for song name, one for artist, and a column called "target" which is the label for the song.

Here are the 13 track attributes: acousticness, danceability, duration_ms, energy, instrumentalness, key, liveness, loudness, mode, speechiness, tempo, time_signature, valence.

Information on what those traits mean can be found here: https://developer.spotify.com/web-api/get-audio-features/

### Acknowledgements
I would like to thank Spotify for providing this readily accessible data.

### Inspiration

I'm a music lover who's curious about why I love the music that I love.

### 背景描述
本数据集收录了2017年的歌曲，并包含了从Spotify API获取的属性信息。每首歌曲被标记为“1”，表示喜爱，而“0”则代表不喜爱。我利用这些数据来探究是否能够构建一个分类器，用以预测个人对某首歌曲的喜好程度。
我撰写了一篇关于该项目的研究文章，其中包含了如何从Spotipy API包装器中获取这些数据以及建模方法的详细介绍。
[文章链接](https://opendatascience.com/blog/a-machine-learning-deep-dive-into-my-spotify-data/)

### 数据内容
每行数据代表一首歌曲。
数据集包含16列，其中13列为歌曲属性，包括歌曲名称、艺术家以及一个名为“target”的列，用于表示歌曲的标签。
以下是13个歌曲属性：音域、舞蹈性、时长（毫秒）、能量、乐器性、调性、现场感、音量、模式、语音度、节拍、时间签名和情感值。
有关这些属性含义的详细信息，请参阅以下链接：[Spotify开发者文档](https://developer.spotify.com/web-api/get-audio-features/)

### 致谢
我衷心感谢Spotify提供的易于获取的数据。

### 灵感来源
作为一名音乐爱好者，我对个人喜爱音乐的原因充满好奇。