Insights for wellbeing: Predicting personal air quality index using regression approach

Amel Ksibi, Amina Salhi, Ala Alluhaidan, Sahar A. El-Rahman

Research output: Contribution to journalConference articlepeer-review


Providing air pollution information to individuals enables them to understand the air quality of their living environments. Thus, the association between people's wellbeing and the properties of the surrounding environment is an essential area of investigation. This paper proposes Air Quality Prediction through harvesting public/open data and leveraging them to get Personal Air Quality index. These are usually incomplete. To cope with the problem of missing data, we applied KNN imputation method. To predict Personal Air Quality Index, we apply a voting regression approach based on three base regressors which are Gradient Boosting regressor, Random Forest regressor and linear regressor. Evaluating the experimental results using the RMSE metric, we got an average score of 35.39 for Walker and 51.16 for Car.

Original languageEnglish
JournalCEUR Workshop Proceedings
StatePublished - 2020
EventMultimedia Evaluation Benchmark Workshop 2020, MediaEval 2020 - Virtual, Online
Duration: 14 Dec 202015 Dec 2020


Dive into the research topics of 'Insights for wellbeing: Predicting personal air quality index using regression approach'. Together they form a unique fingerprint.

Cite this