Function and pricing of mobile phones have always been a noticeable question to customers. Countless studies have been done for phone brands to set their standard on pricing new released phones. This paper analyzes the mobile price classification dataset on Kaggle dataset, by using three models: logistic regression, k-nearest neighbors, and support vector machine. Feature selection by considering the correlations between features are used as comparison to the models without feature selection. The performance of models is shown by accuracy and macro F1 score. The performance of models on training dataset and testing dataset are then compared. The conclusion by this paper is that two models of logistic regression have the best performance, followed by two models of k-nearest neighbors which has almost the same performance. Support vector machine with feature selection has a good performance, while it performs poorly without feature selection. It is concluded that feature selection improves the performance of the model significantly. The performance of models on training dataset and testing dataset are consistent.
