Spotify Top 50 Track Analysis

This project compares Spotify's top 50 track artists' songs on various factors using data visualization techniques.

Analysis Overview

• Spotify's top 50 track artist songs are compared on various factors using data visualization.
• The dataset is sourced from Kaggle and visualized both in Python and Tableau.

The analysis is conducted using Python with numpy, pandas, and Seaborn libraries.

Analysis with Python

The analysis involves exploring the top 50 tracks' data, including factors like popularity, tempo, danceability, and more. Python libraries such as numpy, pandas, and Seaborn are utilized to visualize and analyze the data effectively.

Tableau Visualization

Additionally, Tableau is used for data visualization to provide insights into various parameters and trends discovered during the analysis.

Parameters Covered

• Popularity
• Tempo
• Danceability
• Energy
• Acousticness
• Instrumentalness
• Valence
• Loudness
• Duration
• and more...

Installation

To run the analysis, make sure you have Python installed along with the following libraries:

• numpy
• pandas
• Seaborn

You can install these libraries using pip:

pip install numpy pandas seaborn

Usage

1. Clone the repository: Clone the repository to your local machine using the following command:

   git clone https://github.com/advikbhatt/Spotify-Track--Analysis

2. Install the required Python libraries as mentioned above.
3. Run the Python script for data analysis.
4. Use Tableau for additional visualization and insights.

Contributing

Contributions are welcome! If you'd like to contribute to this project, feel free to fork the repository and submit a pull request with your changes.