<![CDATA[Laptop prices fluctuate greatly because of varying hardware specifications, making it tough for both buyers and sellers to estimate costs. Utilizing machine learning methods offers a useful way to forecast laptop prices based on their characteristics. This research seeks to examine and compare how well the Linear Regression and Random Forest Regressor algorithms can predict laptop prices. The dataset in use includes multiple laptop specifications such as RAM, CPU, GPU, storage size, display dimensions, and operating system. The research process consisted of data cleaning, analyzing feature correlations, encoding categorical data, dividing the data into training and testing sets, training the models, and assessing their performance. The metrics employed for evaluation were Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-Squared (R²). Findings reveal that the Random Forest Regressor surpassed Linear Regression in forecasting laptop prices. Random Forest recorded an MAE of 174. 50, an RMSE of 251. 49, and an R² score of 83. 72%, while Linear Regression achieved an MAE of 277. 16, an RMSE of 354. 12, and an R² score of 67. 72%. Additionally, the Actual vs Predicted analysis showed that the predictions made by Random Forest were more aligned with real laptop prices. Consequently, the Random Forest Regressor is regarded as the more efficient model for predicting laptop prices due to its superior accuracy and enhanced ability to understand complex relationships among the features.]]>
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