A Comparative Analysis of Linear Regression and XGBoost Algorithms for
Predicting GPU Prices Using Technical Specifications
Dublin Core
Title
A Comparative Analysis of Linear Regression and XGBoost Algorithms for
Predicting GPU Prices Using Technical Specifications
Predicting GPU Prices Using Technical Specifications
Subject
GPU, XGBoost, Linear Regression, Price Prediction, Machine Learning, Technical Specifications
Description
This study investigates and compares the predictive performance of Linear Regression and XGBoost algorithms in estimating Graphics
Processing Unit (GPU) prices based on their technical specifications. GPU prices are known for their high volatility, influenced not only by
hardware characteristics—such as memory capacity, clock speed, and bandwidth—but also by external market factors including demand from
the gaming industry, machine learning applications, and cryptocurrency mining activities. The dataset used in this research comprises 475 GPU
units from three leading manufacturers—NVIDIA, AMD, and Intel Arc—featuring 15 technical attributes obtained from publicly accessible data
sources. Adopting an experimental quantitative approach, the dataset was divided into training and testing subsets using an 80:20 ratio. The data
preprocessing phase involved handling missing values, detecting outliers through the Interquartile Range (IQR) method, performing data
normalization, and encoding categorical features. The models were evaluated using four performance metrics: the Coefficient of Determination
(R²), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results demonstrate
that XGBoost outperforms Linear Regression, achieving an R² of 0.8129, MAE of 85.07 USD, RMSE of 122.03 USD, and MAPE of 35.23%. In
comparison, the Linear Regression model recorded an R² of 0.7629, MAE of 106.59 USD, RMSE of 137.38 USD, and MAPE of 56.04%. The
superior performance of XGBoost can be attributed to its ability to model non-linear relationships and capture complex feature interactions among
GPU specifications
Processing Unit (GPU) prices based on their technical specifications. GPU prices are known for their high volatility, influenced not only by
hardware characteristics—such as memory capacity, clock speed, and bandwidth—but also by external market factors including demand from
the gaming industry, machine learning applications, and cryptocurrency mining activities. The dataset used in this research comprises 475 GPU
units from three leading manufacturers—NVIDIA, AMD, and Intel Arc—featuring 15 technical attributes obtained from publicly accessible data
sources. Adopting an experimental quantitative approach, the dataset was divided into training and testing subsets using an 80:20 ratio. The data
preprocessing phase involved handling missing values, detecting outliers through the Interquartile Range (IQR) method, performing data
normalization, and encoding categorical features. The models were evaluated using four performance metrics: the Coefficient of Determination
(R²), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results demonstrate
that XGBoost outperforms Linear Regression, achieving an R² of 0.8129, MAE of 85.07 USD, RMSE of 122.03 USD, and MAPE of 35.23%. In
comparison, the Linear Regression model recorded an R² of 0.7629, MAE of 106.59 USD, RMSE of 137.38 USD, and MAPE of 56.04%. The
superior performance of XGBoost can be attributed to its ability to model non-linear relationships and capture complex feature interactions among
GPU specifications
Creator
Dendi Putra Prakoso1,*
, Muhammad Irfan2
, Quba Siddique3
, Muhammad Irfan2
, Quba Siddique3
Source
https://ijiis.org/index.php/IJIIS/article/view/228/152
Publisher
Universitas Amikom Purwokerto
Date
desember 2024
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Dendi Putra Prakoso1,*
, Muhammad Irfan2
, Quba Siddique3
, “A Comparative Analysis of Linear Regression and XGBoost Algorithms for
Predicting GPU Prices Using Technical Specifications,” Repository Horizon University Indonesia, accessed January 2, 2026, https://repository.horizon.ac.id/items/show/9720.
Predicting GPU Prices Using Technical Specifications,” Repository Horizon University Indonesia, accessed January 2, 2026, https://repository.horizon.ac.id/items/show/9720.