Spotify-Project

análisis y el aprendizaje automático aplicados a un conjunto de datos que contiene el "top" de canciones desde el año 2000 hasta 2019. El objetivo principal del proyecto es extraer información relevante y obtener conocimientos sobre las tendencias y características de las canciones populares durante ese periodo.

Content

• artist: Name of the Artist.

• song: Name of the Track.

• duration_ms: Duration of the track in milliseconds.

• explicit: The lyrics or content of a song or a music video contain one or more of the criteria which could be considered offensive or unsuitable for children.

• year: Release Year of the track.

• popularity: The higher the value the more popular the song is.

• danceability: Danceability describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable.

• energy: Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity.

• key: The key the track is in. Integers map to pitches using standard Pitch Class notation. E.g. 0 = C, 1 = C♯/D♭, 2 = D, and so on. If no key was detected, the value is -1.

• loudness: The overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typically range between -60 and 0 db.

• mode: Mode indicates the modality (major or minor) of a track, the type of scale from which its melodic content is derived. Major is represented by 1 and minor is 0.

• speechiness: Speechiness detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks.

• acousticness: A confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic.

• instrumentalness: Predicts whether a track contains no vocals. "Ooh" and "aah" sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly "vocal". The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0.

• liveness: Detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live.

• valence: A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry).

• tempo: The overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration.

• genre: Genre of the track

Installation guide

Please read install.md for details on how to set up this project.

Project Organization

├── LICENSE
├── tasks.py           <- Invoke with commands like `notebook`.
├── README.md          <- The top-level README for developers using this project.
├── install.md         <- Detailed instructions to set up this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── models             <- Trained and serialized models, model predictions, or model summaries.
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures         <- Generated graphics and figures to be used in reporting.
│
├── environment.yml    <- The requirements file for reproducing the analysis environment.
│
├── .here              <- File that will stop the search if none of the other criteria
│                         apply when searching head of project.
│
├── setup.py           <- Makes project pip installable (pip install -e .)
│                         so spotify_project can be imported.
│
└── spotify_project               <- Source code for use in this project.
    ├── __init__.py    <- Makes spotify_project a Python module.
    │
    ├── data           <- Scripts to download or generate data.
    │   └── make_dataset.py
    │
    ├── features       <- Scripts to turn raw data into features for modeling.
    │   └── build_features.py
    │
    ├── models         <- Scripts to train models and then use trained models to make
    │   │                 predictions.
    │   ├── predict_model.py
    │   └── train_model.py
    │
    ├── utils          <- Scripts to help with common tasks.
        └── paths.py   <- Helper functions to relative file referencing across project.
    │
    └── visualization  <- Scripts to create exploratory and results oriented visualizations.
        └── visualize.py

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Project based on the cookiecutter conda data science project template.