Comparative Analysis of Machine Learning Models for Laptop Price Prediction An Evaluation of Linear Regression, Histogram Gradient Boosting, and XGBoost Approaches

come indispensable for both personal and professional applications, with a vast array of models presenting varied specifications and features. The intricate interplay of hardware configurations and pricing frameworks underscores the necessity for robust predictive models that empower consumers and manufacturers to make well-informed choices. This study delves into the critical challenge of accurately forecasting laptop prices by evaluating three machine learning methodologies: Linear Regression (LR), Histogram Gradient Boosting Regression (HGBR), and XGBoost Regression (XGBR). The research's importance is rooted in its capacity to refine pricing strategies, bolster market efficiency, and provide consumers with deeper insights into the value dynamics associated with different laptop specifications. acterized by 11 pivotal attributes encompassing processor type, RAM, storage capacity, screen dimensions, and graphical performance. Analytical techniques encompassed correlation assessment, feature significance determination, and comparative evaluation of model efficacy, employing key performance indicators such as the R² coefficient, Mean Squared Error (MSE), Root Mean Squared Error (