This project showcase the tools/techniques for extracting information from the PDF documents
├── LICENSE ├── 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-ng-Text-Preparation-for-LDA-Topic-Modeling`. │ ├── 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 │ | │ ├── features <- Scripts to turn raw data into features for modeling │ │ └── build_features.py │ │ │ └── visualization <- Scripts to create exploratory and results oriented visualizations │ └── visualize.py │ Project based on the cookiecutter data science project template. #cookiecutterdatascience