#Court Documents HJH Capstone UNH

Table of contents

• Project Organization
• Developer Note
• Creators
• Copyright and license

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The goal of the project is to design a searchable database containing both state and county court records. This database will enable users to perform analysis and find the right attorney.

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
│   ├── raw            <- The original, immutable data dump from scraping
│   ├── interim        <- Intermediate data that has been transformed to usable format.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── dataset        <- Storing data such as mongodb, json dataset or sql.
│
├── 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`
│
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data_retriever           <- Scripts to scrape 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
├── webapp
│   ├── restapi        <- JSON RESTful API
│   └── webapp         <- An app
│
└── tox.ini            <- tox file with settings for running tox; see tox.testrun.org

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Developer Note

• Using PyCharm
• • Highly recommended using PyCharm to create virtualenv and install packages from requirements.txt

---

• Anaconda-Spyder
• • Make sure you are using Spyder version >= 3.2.8 (There is a bug from Spyder which causes error to connect to Google API)
• Python3
• • This project is written on Python 3 or greater (prefer >= 3.5)
• nltk
• • nltk "popular" package is required

import nltk

nltk.download("popular")

• Google Cloud Language

Set up Google Cloud API Credentials

• 1. You need to request a JSON API key from Huy Le
• 2. Change that JSON file to: cloudPlatform_apiKey.json
• 3. Add this to your .bashrc or .zshrc

export GOOGLE_APPLICATION_CREDENTIALS="[PATH_TO_THIS_DIRECTORY]/src/features/cloudPlatform_apiKey.json"

• 4. Move the cloudPlatform_apiKey.json to hjh-capstone/src/features/
• DONE

---

For everything else

pip install -r requirements.txt

Creators

Huy Le (developer), Heli Amin

Copyright and license

This project is not allowed to use for commercial purposes without permission

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