rubix / divorce
Use the K Nearest Neighbors algorithm to predict the probability of a divorce.
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Type:project
Requires
- php: >=7.4
- rubix/ml: ^2.0
README
Use the K Nearest Neighbors 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:
$ composer create-project rubix/divorce
Requirements
- PHP 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 and the tutorial is licensed CC BY-NC 4.0.