Predicting Emergency Repairs using Classification Method

Authors

Jacqueline Tan, University of Plymouth
Qing Zhang
Sia Wang Ying, University of Plymouth
Yutong Qin, University of Plymouth

Document Type

Engineering, Computing and Mathematics Article

Abstract

This paper discusses how each explanatory variable affects the possibility of having an emergency repair to people’s home with the help of machine learning. Here, the outcome variable is binary. The aim of this is to determine whether increasing the frequency of routine repairs would decrease the frequency of emergency repairs, and the predicted probability of having an emergency repair based on the variable statuses for each property. Data exploratory is first carried out to understand and simplify the dataset obtained from a Housing Association. Statistical models such as logistic regression, decision tree, random forest, linear discriminant analysis and k-nearest neighbours are then used to fit the model to the dataset. We also investigate ways to approach the missing values. The best fitted model is then determined by comparing the highest accuracy of the predicted probabilities between these models.

Publication Date

2021-12-24

Publication Title

The Plymouth Student Scientist

Volume

14

Issue

2

First Page

465

Last Page

496

ISSN

1754-2383

Deposit Date

December 2021

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Recommended Citation

Tan, Jacqueline; Zhang, Qing; Ying, Sia Wang; and Qin, Yutong (2021) "Predicting Emergency Repairs using Classification Method," The Plymouth Student Scientist: Vol. 14: Iss. 2, Article 9.
DOI: https://doi.org/10.24382/7d4y-f418
Available at: https://pearl.plymouth.ac.uk/tpss/vol14/iss2/9