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https://github.com/bradleykirton/foundery-case-study
Case study for FOUNDeRY
https://github.com/bradleykirton/foundery-case-study
data-science python
Last synced: 22 days ago
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Case study for FOUNDeRY
- Host: GitHub
- URL: https://github.com/bradleykirton/foundery-case-study
- Owner: BradleyKirton
- Created: 2018-06-23T08:00:02.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2022-12-08T02:14:11.000Z (about 2 years ago)
- Last Synced: 2023-03-08T22:31:49.603Z (almost 2 years ago)
- Topics: data-science, python
- Language: Jupyter Notebook
- Size: 6.03 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# FOUNDeRY Case Study
Using all or part of the [cover type](https://archive.ics.uci.edu/ml/datasets/Covertype) data set build a multi-class classification model for predicting types of forest cover from the input data available.
# Project Structure
This project is structured as follows:
- foundery-case-study/
- [data](./data/)
- [img](./img/)
- [case-study.ipynb](./case-study.ipynb)
- [Pipfile](./Pipfile)
- [Pipfile.lock](./Pipfile.lock)
- [README.md](./README.md)
This project makes use of pipenv to manage dependencies and virtual environments. All data is stored in the data subfolder however the raw data files are excluded from the repository. The data required in this project is downloaded in the [case-study notebook](./case-study.ipynb).
The accompanying presentation can be found [here](https://slides.com/bradleystuartkirton/deck-1#/).
# Getting Started
To get started just clone the repository and install the project dependencies with pipenv. If you don't already have pipenv installed check out the [official docs](https://docs.pipenv.org/) for details on installation.
```bash
$ git clone https://github.com/BradleyKirton/foundery-case-study
$ cd foundery-case-study
$ pipenv install
$ pipenv shell
$ jupyter lab
```
## Project Dependencies
Outside of the Python standard library this project requires the following libraries:
- [jupyterlab](https://jupyter.org/)
- [requests](http://docs.python-requests.org/en/master/)
- [pandas](https://pandas.pydata.org/)
- [scikit-learn](http://scikit-learn.org/stable/index.html)
- [scipy](https://www.scipy.org/)
- [altair](https://altair-viz.github.io/)
- [matplotlib](https://matplotlib.org/)
- [tqdm](https://tqdm.github.io/)
For a detailed dependency graph run the following command.
```bash
pipenv graph
```
# Case Study Overview
### Data Set Information
> Predicting forest cover type from cartographic variables only
(no remotely sensed data). The actual forest cover type for
a given observation (30 x 30 meter cell) was determined from
US Forest Service (USFS) Region 2 Resource Information System
(RIS) data. Independent variables were derived from data
originally obtained from US Geological Survey (USGS) and
USFS data. Data is in raw form (not scaled) and contains
binary (0 or 1) columns of data for qualitative independent
variables (wilderness areas and soil types).
>
> This study area includes four wilderness areas located in the
Roosevelt National Forest of northern Colorado. These areas
represent forests with minimal human-caused disturbances,
so that existing forest cover types are more a result of
ecological processes rather than forest management practices.
>
> Some background information for these four wilderness areas:
Neota (area 2) probably has the highest mean elevational value of
the 4 wilderness areas. Rawah (area 1) and Comanche Peak (area 3)
would have a lower mean elevational value, while Cache la Poudre
(area 4) would have the lowest mean elevational value.
>
> As for primary major tree species in these areas, Neota would have
spruce/fir (type 1), while Rawah and Comanche Peak would probably
have lodgepole pine (type 2) as their primary species, followed by
spruce/fir and aspen (type 5). Cache la Poudre would tend to have
Ponderosa pine (type 3), Douglas-fir (type 6), and
cottonwood/willow (type 4).
>
> The Rawah and Comanche Peak areas would tend to be more typical of
the overall dataset than either the Neota or Cache la Poudre, due
to their assortment of tree species and range of predictive
variable values (elevation, etc.) Cache la Poudre would probably
be more unique than the others, due to its relatively low
elevation range and species composition.
The data set contains the following features.
| Name | Data Type | Measurement | Description |
|------------------------------------|---------------|--------------------------------|------------------------------------------------|
| Elevation | quantitative | meters | Elevation in meters |
| Aspect | quantitative | azimuth | Aspect in degrees azimuth |
| Slope | quantitative | degrees | Slope in degrees |
| Horizontal_Distance_To_Hydrology | quantitative | meters | Horz Dist to nearest surface water features |
| Vertical_Distance_To_Hydrology | quantitative | meters | Vert Dist to nearest surface water features |
| Horizontal_Distance_To_Roadways | quantitative | meters | Horz Dist to nearest roadway |
| Hillshade_9am | quantitative | 0 to 255 index | Hillshade index at 9am, summer solstice |
| Hillshade_Noon | quantitative | 0 to 255 index | Hillshade index at noon, summer soltice |
| Hillshade_3pm | quantitative | 0 to 255 index | Hillshade index at 3pm, summer solstice |
| Horizontal_Distance_To_Fire_Points | quantitative | meters | Horz Dist to nearest wildfire ignition points |
| Wilderness_Area (4 binary columns) | qualitative | 0 (absence) or 1 (presence) | Wilderness area designation |
| Soil_Type (40 binary columns) | qualitative | 0 (absence) or 1 (presence) | Soil Type designation |
| Cover_Type (7 types) | integer | 1 to 7 | Forest Cover Type designation |