Spotify Audio Features vs Popularity — Data Analysis

📌 Project Overview

This project provides a comprehensive analysis of Spotify tracks to identify the key audio features (such as danceability, energy, and acousticness) that drive track popularity. Leveraging the Python data science stack (Pandas, NumPy, Matplotlib, Seaborn), the project includes a robust ETL pipeline, exploratory data analysis (EDA), and a baseline linear regression model to quantify feature importance.

📂 Repository Structure

spotify-audio-popularity-analysis/
├── data/               # Dataset directory (user-provided CSV)
├── notebooks/          # Jupyter Notebooks for analysis
│   └── 01_spotify_eda.ipynb
├── src/                # Helper modules
│   └── utils.py        # Data cleaning and plotting utilities
├── outputs/            # Generated visualizations (PNG)
├── requirements.txt    # Project dependencies
└── README.md           # Project documentation

🚀 Setup & Usage

1. Prerequisites

Ensure you have Python 3.8+ installed.

2. Installation

Clone the repository and install the required dependencies:

pip install -r requirements.txt

3. Data Configuration

Download a Spotify Tracks dataset (e.g., from Kaggle's "Spotify Tracks Dataset"). The dataset should be a CSV file containing columns like popularity, danceability, energy, tempo, etc.

Action: Place the CSV file at ./data/spotify_tracks.csv.

4. Running the Analysis

Launch the Jupyter Notebook to execute the analysis pipeline:

jupyter notebook notebooks/01_spotify_eda.ipynb

Run all cells to generate the insights and populate the outputs/ directory.

📊 Output Visualizations

The analysis automatically generates the following visualizations in the outputs/ directory:

Filename	Description
popularity_distribution.png	Histogram and KDE of the target variable popularity.
audio_features_distribution.png	Combined distribution plots for key audio features (danceability, energy, valence, etc.).
correlation_heatmap.png	Pearson correlation matrix showing relationships between numerical features.
bivariate_scatter_plots.png	Scatter plots with linear trendlines for top correlated features vs. popularity.
tempo_outliers_comparison.png	Boxplots demonstrating the impact of IQR outlier removal on Tempo.
explicit_vs_popularity.png	Boxplot comparison of popularity for Explicit vs. Non-Explicit tracks.
mode_vs_popularity.png	Popularity distribution across Major vs. Minor modes.
hit_vs_nonhit_profile.png	Bar chart comparing mean audio feature values for "Hits" (Pop ≥ 80) vs. others.
popularity_trend_by_year.png	Time-series analysis of average popularity over years.

🔑 Key Analytical Insights

• Correlation Analysis: Identifies which features have the strongest positive/negative correlation with popularity.
• Hit Profile: "Hit" songs often exhibit distinct characteristics in terms of loudness, danceability, and energy compared to the general population.
• Explicit Content: Analysis reveals quantifiable differences in popularity distribution between explicit and clean tracks.

🛠 Tech Stack

• Core: pandas, numpy
• Visualization: matplotlib, seaborn
• Modeling: scikit-learn (Linear Regression baseline)

👨‍💻 Author

Batuhan Küçükaydın
Software Engineer | Computer Engineer | iOS Developer
📫 LinkedIn • GitHub • Medium

⭐️ Support

If you like this project, please consider giving it a star 🌟
It really helps me keep building and improving!