markrofail / airbnb-new-user-bookings-kaggle-

My solution for Kaggle Competition:

[KAGGLE] Airbnb New User Bookings

predict in which country a new user will make his or her first booking.

Results

• Random Forests
  • 85.2% Accuracy Kaggle Tests

• Neural Network
  • 40% Accuracy Kaggle Tests
  • 60% Accuracy Local Tests

How to use

• install requirements pip install -r requirements.txt

• generate data python -m src.data.data_clean && python -m src.data.data_transform

• use NN model python -m src.models.train_model

• use Random Forests model python -m src.models.decision_tree

Project Organization

├── data
│   ├── processed      <- The final, canonical data sets for modeling.
│   ├── interim        <- Intermediate data that has been transformed.
│   └── raw            <- The original, immutable data dump.
│
├── 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`.
│
├── 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.
    │
    ├── data           <- Scripts to download or generate data
    │   └── make_dataset.py
    │
    └── models         <- Scripts to train models and then use trained models to make
        │                 predictions
        ├── decision_tree.py RANDOM FORESTS MODEL
        └── train_model.py   NEURAL NETWORK MODEL

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