Laptop Price Prediction Using Extreme Gradient Boosting Algorithm
DOI:
https://doi.org/10.61098/jarcis.v2i1.173Keywords:
Laptop Prices, Extreme Gradient Boosting, Cross Validation, OutlierAbstract
The laptop is a support for many people in doing all activities. The number of laptop outputs with various models can affect the price of laptops. The presence of various online and offline stores causes different laptop prices and it becomes difficult to compare prices that are close to the low price range. Based on these problems, a system is needed that can predict laptop prices based on laptop specifications that are useful for people in finding a cheap price range. Data collection in this study came from bhinneka.com with 560 data and pemmz.com with 319 data collected by scrapping method. This research uses the Extreme Gradient Boosting method with evaluation techniques in the form of cross-validation resulting in an R2 score at the Bhinneka store of 0.98 and RMSE of 1250363.29 with the best cross-validation of 8. At Pemmz store produces an R2 score of 0.98 and RMSE of 1073090.92 with the best cross-validation of 6. Both results use data with outliers.
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