<![CDATA[The rapid advancement of mobile technology tools day by day benefits thousands of smartphone retailers by offering various innovations. This study aims to predict smartphone prices based on their technical features using the linear regression method. The dataset used includes various technical attributes from different smartphone models. The research process involves a data preprocessing stage to clean missing or invalid values and feature transformation to prepare the data for the linear regression process. Subsequently, a linear regression model is developed and tested using cross-validation techniques to evaluate its performance. The metric used to measure the model's prediction accuracy or error is RMSE. The experimental results show an RMSE value of 170.692. The target variable, which is the smartphone price, ranges from the lowest price of 614 to the highest price of 4,361. The RMSE value obtained in this study can be considered fairly good, as it is less than 10% of the actual value or average price. Variables such as RAM, storage size, camera, and processor type significantly influence smartphone prices. However, other factors such as brand and design may also have an impact, albeit to a lesser extent. This study confirms that linear regression can be effectively used to predict smartphone prices based on technical specifications. The findings of this research can assist companies in developing pricing strategies based on smartphone specifications. Additionally, it can help determine which products are suitable for market introduction.]]>
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