#Court Documents HJH Capstone UNH

Table of contents

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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

Developer Note

nltk.download("popular")

pip install -r requirements.txt

Creators

Huy Le (developer), Heli Amin

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

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