NLP-reviews-classifier

This project aims to train a model which will classify hotel reviews as number of stars

Project setup

• Step 1: Install Git LFS

  • If you haven't already installed Git LFS, you can download and install it from the official website or use a package manager like Homebrew (for macOS) or Chocolatey (for Windows). Refer to the Git LFS documentation for detailed installation instructions.

• Step 2: Clone the Repository

  • To clone a repository with LFS files, use the git clone command followed by the repository URL:

    git clone https://github.com/Newtoneiro/NLP-review-classifier

• Step 3: Fetch LFS Files

  • Once the repository is cloned, navigate into the repository directory and run the following command to fetch the LFS files:

    git lfs pull

  • This command downloads the LFS-tracked files associated with the repository.

• Step 4: Pull Updates

  • If you've previously cloned the repository and new LFS files have been added or modified, you can pull the updates using the standard git pull command:

    git pull

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.
│
├── 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`
│
├── 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
│   │   └── make_dataset.py < - This script was used to convert 50M record dataset into arround 1M records.
|   |   └── data_preprocess.ipynb <- This script preprocesses and balances the 1M records dataset (data.zip)
│   │
│   ├── 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
│
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

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