https://github.com/mahtabranjbar/geospatial-clustering
This project performs geospatial clustering on a dataset of latitude and longitude coordinates to identify clusters of points.
https://github.com/mahtabranjbar/geospatial-clustering
clustering machine-learning unsupervised-learning
Last synced: 2 months ago
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This project performs geospatial clustering on a dataset of latitude and longitude coordinates to identify clusters of points.
- Host: GitHub
- URL: https://github.com/mahtabranjbar/geospatial-clustering
- Owner: MahtabRanjbar
- License: mit
- Created: 2023-08-04T16:35:45.000Z (almost 2 years ago)
- Default Branch: master
- Last Pushed: 2023-09-15T09:36:38.000Z (over 1 year ago)
- Last Synced: 2025-01-24T11:44:42.609Z (4 months ago)
- Topics: clustering, machine-learning, unsupervised-learning
- Language: HTML
- Homepage:
- Size: 3.38 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Geospatial Clustering
This project performs geospatial clustering on a dataset of latitude and longitude coordinates to identify clusters of points.
## Clustering Report
I have performed clustering analysis on the dataset and the results can be found in **reports** folder.
you can also see Clustering report [here](reports/README.md)
## Installation
To use this project, you will need to have Python 3 and `conda` installed on your computer. You can download Python 3 from the [official website](https://www.python.org/downloads/) and `conda` from the [official website](https://docs.conda.io/en/latest/miniconda.html).
Once you have installed Python 3 and `conda`, you can create an environment for this project by running the following command in the root directory of the project:
```sh
conda create --name geo_cluster python=3.9
```
This will create a new conda environment named geo_cluster with Python 3.9 installed. You can activate this environment by running the following command:
```sh
conda activate geo_cluster
```
Once you have activated the cancer_clf environment, you can install the necessary libraries for this project by running the following command in the root directory of the project:
```sh
pip install -r requirements.txt
```
This will install the libraries listed in the requirements.txt file using conda. Once you have installed these libraries, you can use this project as described in the Usage section.
## Usage
To use this project, you will need to set the `PYTHONPATH` environment variable to the root directory of the project. You can do this by running the following command in the root directory of the project:
```sh
export PYTHONPATH=${PWD}
```
Once you have set the `PYTHONPATH` environment variable, you can run the main script of the project by running the following command in the `src` directory of the project:
```sh
python src/main.py
```
This will load and preprocess the data, train the clustering models, evaluate their performance, visualize the clusters, and generate a report.
The report will be saved as a Markdown file in the `reports` directory. You can view the report using a Markdown viewer or convert it to another format such as HTML or PDF using a Markdown converter.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more information.