Our Run Models CoLab provides Logistic Regression, Support Vector Machines (SVM), MLP, XGBoost and two Random Forests.
Paths for features and targets are joined based on location IDs (state or county FIPS, etc.) or brain regions for eye blink predictions.
Our main input is currently industry features by county ID (FIPS) for exploring environmental impact targets like bee data and tree canopy.
We are also preping data for International Trade Flow.
Run-Models-bkup.ipynb is a backup of the Run Models CoLab that we run locally. We append "-bkup" to indicate it is not the primary source.
Learn about our cuML GPU speed enhancements - and SMOTE balancing of our classes We're using SHAP to explain our model predictions
Bee YAML Updated - Changed bee data target to bees-targets-top-20-percent.csv in parameters yaml. This new "colony density" target uses the top 20% of counties with the highest bee population density (rather than top colony growth between years, as was used in bees-targets.csv).
In the CoLab, a select menu allows you to choose default parameter yaml paths set in parameter-paths.csv.
parameters-simple.yaml - Bee density for 2020 for Maine and New York by top industries (2-digits)
parameters.yaml - Predicts bee density by industry (6-digit NAICS) for New England states 2017 to 2021
parameters-gdc.yaml - Google Data Commons - Predicting states using demographics
parameters-zip.yaml - Needs zip code target. Uses bee population density.
parameters-blinks.yaml - Uses only features dataset (which contains the target column).
TO DO: Top ten counties in each state likely to have declining tree canopy
TO DO: Within Run Models, add python pull from Google Data Commons API for population, education levels, income/poverty levels to use as both features and targets. - IN PROGRESS
TO DO: Use Tensorflow.org for Neural Network predictions with our training data.