Spotify 播放量建模复盘

项目问题

这次分析围绕 Spotify 2023 数据集展开，目标是用歌曲音频特征和分类变量解释 streams。播放量本身跨度很大，所以建模时使用 log(streams) 作为响应变量，让模型更接近线性回归可以处理的尺度。

数据准备

原始数据里有一些数字字段带逗号，直接读入会变成字符。项目里先写了一个 to_num() 工具函数，把逗号移除，再转换为数值。进入模型前保留完整观测：

model_df <- raw_model_df[complete.cases(raw_model_df), ]
model_df$log_streams <- log(model_df$streams)

主要变量包括 bpm、key、mode、danceability_%、valence_%、energy_%、acousticness_%、instrumentalness_% 和 speechiness_%。

模型设计

初始模型使用多元线性回归，并额外加入 mode:valence_percent 交互项。直觉是：歌曲调式可能会改变 valence 对播放量的解释方式。

prelim_model <- lm(
  log_streams ~ bpm + key + mode + danceability_percent + valence_percent +
    energy_percent + acousticness_percent + instrumentalness_percent +
    speechiness_percent + mode:valence_percent,
  data = model_df
)

诊断流程

项目重点不只是拟合模型，而是检查模型是否可靠。诊断包括四类图：

Residuals vs Fitted：查看残差是否出现明显结构。
Normal Q-Q：检查误差正态性假设是否大致合理。
Scale-Location：观察残差方差是否稳定。
Residuals vs Leverage：结合 Cook's Distance 找高影响点。

这些图帮助判断模型有没有系统性偏差、异方差或异常影响点。

共线性检查

另一个重要问题是 predictor 之间可能高度相关。项目先对多个 predictor 做辅助回归，再用 VIF 检查模型共线性。特别需要关注 energy_percent、valence_percent、acousticness_percent 之间的关系，因为这些变量在音乐情绪和声音特征上可能并不独立。

模型评估

模型表现通过 summary()、ANOVA、置信区间、AIC/BIC 以及 10-fold cross validation 进行评估。交叉验证的意义是避免只看训练集拟合效果，让模型表现更接近“遇到新歌曲时是否还能解释得通”。

我学到的东西

这次项目最有价值的部分是把统计建模流程串起来：数据清洗、变量转换、模型设定、诊断、共线性检查和验证。相比只追求更高的 R-squared，一个更完整的分析应该能解释为什么这样建模、假设哪里可能出问题、以及下一版如何改进。

Project Question

This analysis uses the Spotify 2023 dataset to explain song streams with audio features and categorical predictors. Since stream counts vary across a very large range, the model uses log(streams) as the response so the problem fits a linear regression scale more naturally.

Data Preparation

Some numeric columns in the raw dataset contain commas and are imported as character values. The project first defines a to_num() helper, removes commas, converts values to numeric, and keeps complete observations before fitting:

model_df <- raw_model_df[complete.cases(raw_model_df), ]
model_df$log_streams <- log(model_df$streams)

The main predictors include bpm, key, mode, danceability_%, valence_%, energy_%, acousticness_%, instrumentalness_%, and speechiness_%.

Model Design

The preliminary model is a multiple linear regression with an additional mode:valence_percent interaction. The idea is that musical mode may change how valence relates to streaming performance.

prelim_model <- lm(
  log_streams ~ bpm + key + mode + danceability_percent + valence_percent +
    energy_percent + acousticness_percent + instrumentalness_percent +
    speechiness_percent + mode:valence_percent,
  data = model_df
)

Diagnostics

The project is not only about fitting a model; it is about checking whether the model is trustworthy. The diagnostic workflow includes:

Residuals vs Fitted: checks whether residuals show visible structure.
Normal Q-Q: checks whether the normal error assumption is roughly reasonable.
Scale-Location: looks for non-constant variance.
Residuals vs Leverage: combines leverage with Cook's Distance to identify influential points.

These plots help detect systematic bias, heteroskedasticity, and unusually influential observations.

Collinearity

Another important issue is that predictors may be strongly related to each other. The project uses auxiliary regressions and VIF checks to diagnose collinearity. energy_percent, valence_percent, and acousticness_percent deserve special attention because they may overlap in how they describe mood and sound texture.

Model Evaluation

The model is evaluated with summary(), ANOVA, confidence intervals, AIC/BIC, and 10-fold cross validation. Cross validation matters because it moves the analysis beyond training-set fit and asks whether the model can still explain unseen songs.

Takeaway

The most valuable part of this project is the full modeling workflow: cleaning data, transforming variables, specifying the model, diagnosing assumptions, checking collinearity, and validating performance. A stronger analysis is not only about higher R-squared; it should explain why the model is built this way, where assumptions may fail, and how the next version could improve.