This project focuses on analyzing Spotify track data using data analytics techniques and Python libraries such as Matplotlib, Seaborn, Pandas, and NumPy. The goal is to derive meaningful insights and answer various questions about the tracks based on the available dataset.
The dataset used in this project consists of Spotify track information, including track ID, artists, album name, track name, popularity, duration, explicit lyrics, danceability, energy, key, loudness, mode, speechiness, acousticness, instrumentalness, liveness, valence, tempo, time signature, and track genre.
data/: Directory containing the dataset file(s) used in the analysis.
notebooks/: Directory containing Jupyter notebooks used for data exploration, analysis, and visualization.
README.md: This file, providing an overview of the project and instructions for running the code.
Python 3.x Pandas Matplotlib Seaborn NumPy
Provide a brief summary of the key findings and insights obtained from the analysis. Include visualizations, charts, or tables that highlight important trends or patterns discovered in the dataset. Discuss any conclusions or implications of the analysis.
Contributions to this project are welcome! If you find any issues or have ideas for improvements, feel free to submit a pull request.
