churnr

A churn prediction project for a music streaming service

Requirements

• Anaconda Python 2.7
• Google Cloud SDK

Installing

$ make env
$ source activate churnr
$ make reqs

Create Data

$ make data

Dispatch Training Job

$ make submit

Download Model Predictions

$ make download

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
│   ├── README.md      <- Data description and source paths
│
├── models             <- Model predictions
│
├── notebooks          <- Jupyter notebooks. 
│
├── 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
│
├── churnr             <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── app.py         <- Application entry point for the experiment dispatcher
│   │
│   ├── submitter.py   <- Script for submitting training jobs to CloudML
│   │
│   ├── sample.pys     <- Script for sampling user ids for the experimentss
│   │
│   ├── scala/parse.sh <- Dispatches a play context parser job on Dataflow on the sampled data in 'sample.py'
│   │
│   ├── extract.py     <- Engineer features and aggregate into timesteps the data parse at 'parse.sh'
│   │
│   └── process.py     <- Normalize data from 'extract.py' and export to files in GCS
└── tox.ini            <- tox file with settings for running tox; see tox.testrun.org