School District Clustering

Description

This program consists of three parts:

• [Data Pipeline]: A start-to-finish data pipeline that processes U.S. school district data into an ML-friendly format (src/data)

• [School District Clustering]: A clustering script that labels similar school districts (src/features and src/models)

• [Choropleth]: An interactive map that can be used to visualize the data (src/visualization)

Installation

Using a Linux system with Python 3.x installed, type make all into the terminal.

$ make all

Warning: This process requires a significant amount of memory (~16 GB). If you see "segmentation error" in the output, you may need to close some applications on your system and try again.

Execution

Using a Linux system with Python 3.x and Git LFS installed, type make visual into the terminal.

$ make visual

Then, using a web browser of your choice, navigate to http://localhost:8000/interactive.html

Video Demo

A video walkthrough of this process can be found here.

Other Useful Information

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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.
│
├── 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.
│
├── 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
    │
    ├── features       <- Scripts to turn raw data into features for modeling
    │
    ├── models         <- Scripts to train models and then use trained models to make
    │                     predictions
    │
    └── visualization  <- Scripts to create exploratory and results oriented visualizations

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Data Documentation

https://educationdata.urban.org/documentation/school-districts.html#overview

Cookiecutter Data Science Template

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