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The Artist Success Trajectory Predictor is a data science project that forecasts artist success using historical chart performance and Spotify popularity. It generates visual insights to show trends and predict future success in the music industry. Key visualizations highlight the impact of different weighting strategies on prediction accuracy.

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Run Instructions for Artist Success Trajectory Predictor

Prerequisites

  1. Ensure you have Python installed on your system.
  2. Install the required packages using the following command:
    pip install -r requirements.txt
    
    (Make sure requirements.txt includes all necessary libraries like matplotlib, seaborn, pandas, etc.)

Running the Model

  1. Navigate to the project directory in your terminal.
  2. Activate your virtual environment if you are using one:
    source .venv/bin/activate  # On macOS/Linux
    .venv\Scripts\activate     # On Windows
    
  3. Run the visualization script:
    python code_files/generate_visualizations.py
    

Output

  • The visualizations will be generated in the output directory.
  • Two key visualizations will be created:
    • artist_success_prediction.png: Original weights.
    • artist_success_prediction_adjusted.png: Adjusted weights with higher Spotify popularity.

About

The Artist Success Trajectory Predictor is a data science project that forecasts artist success using historical chart performance and Spotify popularity. It generates visual insights to show trends and predict future success in the music industry. Key visualizations highlight the impact of different weighting strategies on prediction accuracy.

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  • Python 95.5%
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