Taylor Swift Spotify Analysis 📊

An exploratory data analysis (EDA) of Taylor Swift's Spotify presence. This project examines the relationship between song popularity and technical track attributes using a Kaggle Spotify dataset.

🎯 Project Goal

The objective was to determine if song duration influences popularity and to analyze the distribution of tracks across her discography.

🛠️ Tech Stack

• Language: Python
• Libraries: Pandas, Matplotlib, NumPy
• Dataset: Spotify top artist tracks (Kaggle)

📈 Key Findings & Correlations

• Correlation Result: Based on the analysis, there is a weak correlation between track duration and popularity. This suggests that Taylor Swift's audience engagement is driven by factors other than song length.
• Artist Dominance: The data highlights a high density of tracks with popularity scores above 75, showcasing consistent listener retention.
• Data Cleaning: Performed preprocessing to handle missing values and formatted the duration metrics for accurate statistical plotting.

🚀 How to Use

1. Clone the repository.
2. Ensure you have pandas and matplotlib installed.
3. Run spotify_analysis.ipynb to view the visualizations.

📁 Repository Structure

• spotify_analysis.ipynb: The main analysis and visualization notebook.
• spotify_data_clean.csv: Preprocessed dataset used for the analysis.