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
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artist: The name of the artist who performed the song.
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song: The title of the song.
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link: A link, presumably to the song lyrics or additional information about the song. This column is dropped during preprocessing in the notebook.
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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.
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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.
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Preprocessing Steps:
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Sampling to 5,000 rows, indicating a subset is used for analysis or modeling to reduce computational load.
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Dropping the
linkcolumn, 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.
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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.