predict in which country a new user will make his or her first booking.
- Random Forests
- 85.2% Accuracy Kaggle Tests
- Neural Network
- 40% Accuracy Kaggle Tests
- 60% Accuracy Local Tests
-
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
├── 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
Project based on the cookiecutter data science project template. #cookiecutterdatascience