https://github.com/rubixml/divorce

Use the K Nearest Neighbors algorithm to predict the probability of a divorce with high accuracy.
https://github.com/rubixml/divorce

classification cross-validation data-science divorce divorce-prediction example-project k-nearest-neighbors knn machine-learning machine-learning-tutorial nearest-neighbors php php-machine-learning php-ml prediction rubix-ml

Last synced: 7 months ago
JSON representation

Use the K Nearest Neighbors algorithm to predict the probability of a divorce with high accuracy.

• Host: GitHub
• URL: https://github.com/rubixml/divorce
• Owner: RubixML
• License: mit
• Created: 2019-12-04T08:41:32.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2023-07-19T09:42:08.000Z (almost 2 years ago)
• Last Synced: 2024-11-24T20:47:06.476Z (7 months ago)
• Topics: classification, cross-validation, data-science, divorce, divorce-prediction, example-project, k-nearest-neighbors, knn, machine-learning, machine-learning-tutorial, nearest-neighbors, php, php-machine-learning, php-ml, prediction, rubix-ml
• Language: PHP
• Homepage: https://rubixml.com
• Size: 87.9 KB
• Stars: 14
• Watchers: 4
• Forks: 11
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Rubix ML - Divorce Predictor

Use the [K Nearest Neighbors](https://docs.rubixml.com/latest/classifiers/k-nearest-neighbors.html) algorithm to predict who of your friends will stay married or get a divorce based on their answers to a 54 question survey about their partner. Included in this project is a 171 sample human-annotated dataset that we'll use to train the learner.

- **Difficulty**: Easy

- **Training time**: Less than a minute

## Installation

Clone the project locally using [Composer](https://getcomposer.org/):

```sh

$ composer create-project rubix/divorce

```

## Requirements

- [PHP](https://php.net) 7.4 or above

## Tutorial

On the map ...

## Original Dataset

- Dr. Mustafa Kemal Yöntem, Nevşehir Hacı Bektaş Veli University, Faculty of Education, Department of Educational Sciences, muskemtem '@' hotmail.com

- Dr. Kemal ADEM, Aksaray University, Faculty of Economics and Administrative Sciences, Department of Management Information Systems, kemaladem '@' gmail.com

- Prof. Dr. Tahsin İlhan, Tokat GAZİOSMANPAŞA University, Faculty of Education, Department of Educational Sciences, tahsinilhan73 '@' gmail.com

- Lecturer Serhat Kılıçarslan, Tokat GAZİOSMANPAŞA University, Rectorate, Department of Informatics, serhatklc '@' gmail.com

### References

>- M. K. Yöntem et al. (2019). Divorce Prediction Using Correlation Based Feature Selection and Artificial Neural Networks.

>- Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

>- T. A. DeWees et al. (2020). Investigation Into the Effects of Using Normal Distribution Theory Methodology for Likert Scale Patient-Reported Outcome Data From Varying Underlying Distributions Including Floor/Ceiling Effect.

## License

The code is licensed [MIT](LICENSE) and the tutorial is licensed [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).