AbstractThe rapid growth of the smartphone industry requires a deeper understanding of price segmentation based on device specifications. This study aims to develop a smartphone price classification model using Logistic Regression and to evaluate the impact of data preprocessing and hyperparameter tuning on model performance. The results show that the model achieves good classification accuracy, with RAM and screen resolution as the most influential features. Data normalization also improves the stability of the model’s predictions.
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