Alsalmi, Khaled Nasser2023-05-042023-05-04https://hdl.handle.net/2097/43290Kuwaiti political analysts and pundits rely solely on their personal experience, intuition, and subjective perspectives to analyze election results and make predictions. However, this approach neglects the potential benefits and insights that data analysis can provide in uncovering influential factors behind various outcomes. In this report, a data science analysis is carried out on the Kuwaiti parliamentary elections that took place in September 2022, considering voters' age, gender, tribe, religion, occupation, and district. The analysis model was developed by comparing various classification algorithms depending on their suitability to the nature of the data. Most algorithms exhibited a similar trend; However, the RandomForest algorithm consistently achieved the highest accuracy. The interaction between all features excluding tribe and all features excluding religion exhibited a statistically significant result indicating their suitability as predictors for the election outcome of the 2022 elections, making it a potential predictor of Kuwaiti election results.en-USMachine learningPredictionElectionKuwaitPoliticsReport