TrackTelligence: Spotify Popularity Predictor

This repository contains a data science project designed to analyze and predict the popularity of Spotify songs based on their audio features. The project follows a structured workflow, including exploratory data analysis (EDA), clustering and predictive modeling. The implementation adheres to a specific requirement of using statsmodels for model training and scikit-learn for preprocessing, providing a balance of statistical interpretability and modern analysis techniques.

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Key Features

• Data Source: Spotify dataset containing audio features and metadata for songs, sourced from Spotify Songs Dataset from TidyTuesday.
• Framework: Python-based analysis leveraging:
  • pandas, matplotlib, seaborn and more for data manipulation and visualization.
  • scikit-learn for preprocessing and evaluation.
  • statsmodels for statistical modeling.
• Objective: To develop a data analysis pipeline that accurately predicts Spotify song popularity.
• Development Environment: The analysis and modeling were developed in Jupyter Notebooks and are presented as HTML files for seamless exploration and visualization.

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Repository Contents

• notebooks/EDA_Supporting_Notebook: Performs exploratory data analysis (EDA) to uncover patterns and relationships in song features.
• notebooks/Clustering_Supporting_Notebook: Applies clustering techniques to group songs based on feature similarities.
• notebooks/Modeling_Supporting_Notebook: Builds and evaluates predictive models using statsmodels to determine a song's popularity score.
• notebooks/Main_Notebook: Integrates all components (EDA, clustering and modeling) into a streamlined workflow, providing a cohesive summary of the analysis.

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Getting Started

Follow the steps below to clone the repository and access the notebooks locally:

1. Clone the Repository

To get started, clone the repository to your local machine:

git clone! https://github.com/IsraelAzoulay/TrackTelligence-Spotify-Popularity-Predictor.git

2. Navigate to the Project Directory

Change into the project directory:

cd TrackTelligence-Spotify-Popularity-Predictor

3. Access the Notebooks

The notebooks are too large to preview directly on GitHub. You can download and view them locally by following these steps:

1. Navigate to the notebooks folder in the repository.
2. Click on the notebook you want to view (e.g., notebooks/EDA_Supporting_Notebook.html).
3. When the raw data opens in a new browser tab:
   • Windows/Linux: Press Ctrl + S to save the file.
   • Mac: Press Cmd + S to save the file.
4. Open the saved file in your web browser to explore the notebook.

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Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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License

This project is licensed under the MIT License.