<![CDATA[The development of laptop technology has driven the need for accurate price predictions to assist consumers in making purchasing decisions appropriately and efficiently. This study implements a Linear Regression algorithm to predict laptop prices based on 4 main features including Brand, Processor, RAM, and GPU. The dataset used consists of 11,768 data obtained from the Kaggle platform which is processed through preprocessing, feature transformation, and model evaluation stages with various performance metrics. The analysis results show that the RAM feature has the most significant influence on laptop prices, followed by Processor, Brand, and GPU. The developed Linear Regression model successfully achieved an R-squared value of 0.6453, which indicates that the model is able to explain 64.53% of the variation in laptop prices based on the analyzed features. This study contributes to the development of an accurate laptop price prediction system and provides a practical tool to support data-based purchasing decisions effectively and efficiently.]]>
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