California Housing Prices README
==============================

This projects predicts median house values in Californian districts. The median house prices are derived from the 1990 census.

See report and documentation [here](https://caheredia.github.io/california_housing_prices/build/html/index.html)


Data Publishers
------------
- http://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html


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
    │
    ├── .ipynb             <- Jupyter notebooks. 
    │                         
    │                         
    ├── 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.yml   <- The requirements file for reproducing the analysis environment
    │                         
    │
    ├── 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
        │
        ├── 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
        │   ├── cost_estimator.py
        │
        └── visualization  <- Scripts to create exploratory and results oriented visualizations
            └── visualize.py

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