地球资源数据云——数据资源详情
Spotify 全球音乐数据集（2009–2025）
该数据集《Spotify Global Music Dataset (2009–2025)》主要用于监督学习任务，数据形态以文本为主，应用场景偏向安全检测。 题目说明：Analysis of artist popularity, track trends, and music evolution over time. 任务类型：文本监督学习。 建议流程：先做文本清洗与分词，再比较 TF - IDF+线性模型 与 预训练语言模型。 评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。 可用文件：spotify_data clean.csv, track_data_final.csv。 Context One of the most well - known music streaming services in the world, Spotify, offers comprehensive information about songs and artists. It offers insights on how music trends, artist popularity, and genres have changed throughout time and features both contemporary songs from 2025 and timeless hits from 2009 to 2023. The information was extracted for analysis and education using Spotify's public API. It includes information at the track, artist, and album levels that can be used to investigate different facets of the music business. Content spotify_data clean.csv:Modern tracks and recent artists (2025)
摘要概览
该数据集《Spotify Global Music Dataset (2009–2025)》主要用于监督学习任务，数据形态以文本为主，应用场景偏向安全检测。 题目说明：Analysis of artist popularity, track trends, and music evolution over time.
任务类型：文本监督学习。
建议流程：先做文本清洗与分词，再比较 TF - IDF+线性模型 与 预训练语言模型。
评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。
可用文件：spotify_data clean.csv, track_data_final.csv。
Context
One of the most well - known music streaming services in the world, Spotify, offers comprehensive information about songs and artists. It offers insights on how music trends, artist popularity, and genres have changed throughout time and features both contemporary songs from 2025 and timeless hits from 2009 to 2023.
The information was extracted for analysis and education using Spotify's public API. It includes information at the track, artist, and album levels that can be used to investigate different facets of the music business.
Content
spotify_data clean.csv:Modern tracks and recent artists (2025)