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

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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