This project applies an SVM based species distribution to model if solar installers have particular 'habits' (or preferred markets) which they work. We use household attributes from appraisal district data for the analysis.
database.py
query databasemake_bunches.py
- preprocess the data for futureinstaller_habitat_svm.py
contains data and script for analysisfilemanage.py
load and save files in proper format
Scripts were written in Python 2.7. You'll need the following modules:
matplotlib >= 1.5.1
numpy >= 1.10.1
pandas >= 0.17.1
scipy >= 0.16.0
seaborn >= 0.6.0
sklearn >= 0.17
To install modules, run:
$ pip install <module>
Thanks to:
- Species distribution modelling http://scikit-learn.org/stable/auto_examples/applications/plot_species_distribution_modeling.html
- this was helpful for laying out http://www.i-programmer.info/programming/python/3942-arrays-in-python.html?start=1
- and this for saving the grids http://geospatialpython.com/2013/12/python-and-elevation-data-ascii-grid.html