price_prediction_model

End to end lightGBM regression model to predict house prices in Beijing

Project Organization

│
├── data/               <- The original, immutable data dump. 
│
├── figures/            <- Figures saved by scripts or notebooks.
│
├── notebooks/          <- Jupyter notebooks. Naming convention is a short `-` delimited 
│                         description, a number (for ordering), and the creator's initials,
│                        e.g. `initial-data-exploration-01-hg`.
│
├── output/             <- Manipulated data, logs, etc.
│
├── tests/              <- Unit tests.
│
├── price_prediction_model/      <- Python module with source code of this project.
│
├── environment.yml     <- conda virtual environment definition file.
│
├── LICENSE
│
├── Makefile            <- Makefile with commands like `make environment`
│
├── README.md           <- The top-level README for developers using this project.
│
└── tox.ini             <- tox file with settings for running tox; see tox.testrun.org

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Project based on the cookiecutter data science project template.

Set up

Install the virtual environment with conda and activate it:

$ conda env create -f environment.yml
$ conda activate example-project 

Install price_prediction_model in the virtual environment:

$ pip install --editable .