https://github.com/dcabezas98/confidence-knn-stratigraphic-visualization

Measuring the Confidence of the k-Nearest Neighbors Algorithm developed in Python for Visualizing the 3D Stratigraphic Architecture of the Llobregat River Delta in NE Spain
https://github.com/dcabezas98/confidence-knn-stratigraphic-visualization

confidence-score geology jupyter-notebook knn machine-learning python visualization

Last synced: 20 days ago
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Measuring the Confidence of the k-Nearest Neighbors Algorithm developed in Python for Visualizing the 3D Stratigraphic Architecture of the Llobregat River Delta in NE Spain

• Host: GitHub
• URL: https://github.com/dcabezas98/confidence-knn-stratigraphic-visualization
• Owner: dcabezas98
• License: cc0-1.0
• Created: 2022-09-05T14:26:14.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2022-12-05T14:21:59.000Z (almost 2 years ago)
• Last Synced: 2024-01-27T15:04:11.619Z (10 months ago)
• Topics: confidence-score, geology, jupyter-notebook, knn, machine-learning, python, visualization
• Language: Jupyter Notebook
• Homepage:
• Size: 14.6 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# confidence-knn-stratigraphic-visualization

## Measuring the Confidence of the k-Nearest Neighbors Algorithm developed in Python for Visualizing the 3D Stratigraphic Architecture of the Llobregat River Delta in NE Spain

Confidence maps for the KNN predictions in https://www.mdpi.com/2077-1312/10/7/986 / https://github.com/dcabezas98/knn-stratigraphic-visualization.

## How to use

### Download the code

The code can be found in the repository, it can be downloaded as ZIP by clicking in the geen Code button. The only necessary file is the notebook `confidence.ipynb`.

### Download the data

The data can be found in the `data` folder. Only two files are necessary: `deltacontourn.csv`, that contains the points of the contour of the Delta; and `hsd new basements.xls`, that contains the data from the wells.

### How to run the notebook

You can run the notebook in [Jupyter-Notebook](https://jupyter.org/) or [Visual Studio Code](https://code.visualstudio.com/), you also need a Python kernel installed in your computer. We recommend installing [Anaconda](https://www.anaconda.com/) and launching Jupyter by typing `jupyter-notebook` in the Anaconda Prompt.

To successfully run the notebook, you need to locate it in the same folder as the `data` directory. In order to do this, you may just extract the ZIP file with the whole repository. Then, launch Jupyter Notebook and select the notebook `confidence.ipynb`. To run a cell, you can just click in the run button (next to the cell number) or click on it and press Ctrl+Enter. You're now ready to go!

#### Authors:

Manuel Bullejos, David Cabezas, Manuel Martín-Martín and Francisco Javier Alcalá