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    <name>Conference Paper</name>
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              <text>Predicting Song Popularity Using Data Analysis</text>
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              <text>LightGBM Regressor; Mean Absolute Error; Mean Squared Error; Popularity prediction; R-squared; Random Forest Regressor; Root Mean Squared Error; Spotify; XGBoost Regressor</text>
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              <text>In today's music landscape, predicting a song's success is crucial for musicians, record labels, and streaming platforms. This paper introduces a methodology for estimating popularity using Spotify data, termed the 'Proxy Popularity Score.' Three models - Random Forest, LightGBM Regressor, and XGBoost Regressor - are utilized for prediction. Performance metrics including mean absolute error, mean squared error, root mean squared error, and R-squared error are employed to evaluate model accuracy. Correlation values of 99.85%, 99.87%, and 99.84% are achieved for XGBoost, LightGBM, and Random Forest respectively. The study concludes with a ranking of songs based on predicted popularity scores.   2024 IEEE.</text>
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          <name>Creator</name>
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              <text>Medows K.J.; Leisha R.; Thiruthuvanathan M.M.</text>
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              <text>Proceedings of InC4 2024 - 2024 IEEE International Conference on Contemporary Computing and Communications</text>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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          <name>Date</name>
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              <text>2024-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1109/InC460750.2024.10649267" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/InC460750.2024.10649267&lt;/a&gt;
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              <text>Restricted Access</text>
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              <text>ISBN: 979-835038365-2</text>
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              <text>Online</text>
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              <text>English</text>
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              <text>Medows K.J., CHRIST (Deemed To Be University), Computer Science and Engineering, Bangalore, India; Leisha R., CHRIST (Deemed To Be University), Computer Science and Engineering, Bangalore, India; Thiruthuvanathan M.M., CHRIST (Deemed To Be University), Computer Science and Engineering, Bangalore, India</text>
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