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Spotify-Music

Team Members

Part 1

  • Arnav Gupta
  • Freddy Chen
  • Jatin Suri
  • Lakshya Agarwal
  • Om Sangwan
  • Yiyi Yang

Part 2

  • Om Sangwan
  • Yiyi Yang
  • David Gao
  • Emily Wu
  • Yifan Lu

Data Source

The dataset we are using is the Spotify - 30000 songs.

Project Description

We aim to uncover patterns and relationships that drive song popularity to empower artists and producers with the knowledge to make smarter, evidence-based decisions. We will be using the Spotify dataset to analyze the features of songs and their popularity. We aim to build two models:

  • Predictive Model for Song Popularity
  • Causal Model for Song's Duration Effect on its Popularity

Model 1: Predictive Model for Song Popularity

Stacked Classifier

Model 2: Causal Model for Song's Duration Effect on Song's Popularity

Causal Results

The results show a statistically significant negative causal relationship between the duration of a song and its popularity that increases with time.

Project Architecture

Project Architecture

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using 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.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── 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
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src 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
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

Getting Started

To get started with the project, you can set up a virtual environment.

Create a virtual environment in your project directory using the following command:

python -m venv env

Activate the virtual environment using the following command:

source env/bin/activate

use the following command to install the required packages:

poetry install

If you don't have poetry installed, you can install it using the following command:

pip install poetry

To run mlflow, use the following command to start a local mlflow server:

mlflow server --host 127.0.0.1 --port 5000

Project based on the cookiecutter data science project template. #cookiecutterdatascience