地球资源数据云——数据资源详情
Spotify 分析与可视化
该数据集《Spotify Analysis & Visualization》主要用于多分类任务，数据形态以时序/信号为主。 题目说明：Track - level Spotify data for visualization, modeling, and trend analysis. 任务类型：时序/信号多分类。 建议流程：先做去噪和窗口化，再比较树模型与 1D CNN/LSTM 等时序基线。 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_analysis_dataset.csv。 Introduction Music is more than sound—it's data, emotion, culture, and mathematics woven together. This dataset, Spotify Analysis & Visualization, unlocks a rich world of musical characteristics by bringing together structured track - level metadata and detailed audio features extracted from Spotify. It is designed specifically for data scientists, analysts, ML practitioners, music researchers, and students who want to understand the measurable attributes that shape modern music. From danceability to acousticness, tempo to valence, each feature offers a unique lens through which you can interpret the essence of a song. Whether your goal is to build a machine learning model, explore musical patterns, create compelling visualizations, or analyze artist trends, this dataset provides a powerful, clean foundation that accelerates your workflow.
摘要概览
该数据集《Spotify Analysis & Visualization》主要用于多分类任务，数据形态以时序/信号为主。 题目说明：Track - level Spotify data for visualization, modeling, and trend analysis.
任务类型：时序/信号多分类。
建议流程：先做去噪和窗口化，再比较树模型与 1D CNN/LSTM 等时序基线。
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_analysis_dataset.csv。
Introduction
Music is more than sound—it's data, emotion, culture, and mathematics woven together. This dataset, Spotify Analysis & Visualization, unlocks a rich world of musical characteristics by bringing together structured track - level metadata and detailed audio features extracted from Spotify.
It is designed specifically for data scientists, analysts, ML practitioners, music researchers, and students who want to understand the measurable attributes that shape modern music.
From danceability to acousticness, tempo to valence, each feature offers a unique lens through which you can interpret the essence of a song.
Whether your goal is to build a machine learning model, explore musical patterns, create compelling visualizations, or analyze artist trends, this dataset provides a powerful, clean foundation that accelerates your workflow.