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Spotify Millsongdata

22 Apr 04:59
@xn-coder xn-coder
spotify_millsongdata
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The spotify_millsongdata.csv file appears to be a dataset related to Spotify music tracks, specifically focusing on songs and possibly their lyrics or metadata. Based on the context provided from the Model Training.ipynb notebook, here's a description of the dataset:

spotify_millsongdata.csv Description

  1. artist: The name of the artist who performed the song.

  2. song: The title of the song.

  3. link: A link, presumably to the song lyrics or additional information about the song. This column is dropped during preprocessing in the notebook.

  4. text: The lyrics of the song or possibly a description related to the song. This text undergoes several preprocessing steps, including lowercasing, removal of newline characters, and tokenization/stemming for analysis.

  • Usage: The dataset is used for text analysis, as indicated by the preprocessing steps (lowercasing, tokenization, stemming) and the application of TF-IDF vectorization followed by cosine similarity calculations. This suggests the dataset might be used for tasks such as song recommendation based on lyrics similarity, lyrics analysis, or artist style analysis.

  • Preprocessing Steps:

    • Sampling to 5,000 rows, indicating a subset is used for analysis or modeling to reduce computational load.

    • Dropping the link column, focusing analysis on artist, song title, and lyrics/text.

    • Text cleaning includes converting to lowercase, replacing certain characters and newline characters with spaces, and stemming.

  • Analysis Tools: Utilizes Python libraries such as Pandas for data manipulation, NLTK for natural language processing (tokenization and stemming), and Scikit-learn for TF-IDF vectorization and cosine similarity calculation.

This dataset is a rich source for exploring music lyrics and metadata, suitable for natural language processing tasks, recommendation systems, or exploratory data analysis to uncover insights about musical trends, artist vocabulary, and more.

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