Journal of Computer Technology & Applications

Journal of Computer Technology & Applications Review Article

SpecForesight: A Predictive Analytics Pipeline for Laptop Price Forecasting

  1. Bhargav Chebrolu MS in Supply Chain Management (Naveen Jindal School of Management), The University of Texas, Dallas, Richardson, TX 75080

Abstract

This paper frames laptop pricing as a supervised predictive analytics problem, transforming product specifications into feature-rich signals to forecast price with calibrated regression models and operational guardrails against drift. A structured pipeline ingests tabular listings, performs data cleaning, and engineers domain-informed features (e.g., central processing unit (CPU) family and clocks, graphics processing unit (GPU) tiering, memory/storage density, display, and touch capabilities), followed by encoding and normalization to optimize model learnability. Multiple learners are benchmarked under cross-validated protocols—spanning linear baselines and non-linear ensembles—with model selection guided by out-of-sample R² and mean absolute error (MAE), residual diagnostics, and segment-wise error profiling across device categories. The chosen model is calibrated and threshold-governed to stabilize predictions under changing spec distributions, then exposed via a lightweight web interface to support counterfactual “what-if” pricing scenarios for unseen configurations. Contributions include an end-to-end, production-oriented specification-to-price pipeline, a comparative evaluation suite emphasizing generalization and interpretability, and a deployment pattern enabling real-time inference and scenario simulation. By centering forecasting rigor, error governance, and decision readiness, the work advances a practical blueprint for price prediction in consumer electronics grounded in predictive analytics principles.

Keywords

  • Machine Learning
  • Price prediction
  • consumer electronics
  • feature engineering
  • random forest
  • regression analysis

References (11)

  1. Stolley KS. Statistics on Adoption in the United States. The Future of Children. 1993;3(1):26. doi:10.2307/1602400
  2. Karakuş Y, Bilgin TT. Laptop price range prediction with machine learning methods. Int J Multidiscip Stud Innov Technol. 2024;8(1):40–45.
  3. Shaik MA, Varshith M, SriVyshnavi S, Sanjana N, Sujith R. Laptop Price Prediction using Machine Learning Algorithms. 2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS). 2022:226-231. doi:10.1109/icetems56252.2022.10093357
  4. Hasnain M, Sajid A, Ayeb A, Awan A. Predicting the price of used electronic devices using machine learning techniques. Int J Comput Relat Technol. 2023;4(1):13–19.
  5. Sorower MS. A literature survey on algorithms for multi-label learning [technical report]. Corvallis (OR): Oregon State University; 2010. p. 1–25.
  6. Pandey M, Kumar Sharma V. A Decision Tree Algorithm Pertaining to the Student Performance Analysis and Prediction. International Journal of Computer Applications. 2013;61(13):1-5. doi:10.5120/9985-4822
  7. Priyam A, Abhijeeta GR, Rathee A, Srivastava S. Comparative analysis of decision tree classification algorithms. Int J Curr Eng Technol. 2013;3(2):334–337.
  8. Varlı M. (2020). Laptop Price: Laptop Company Price List for Regression. [online] Kaggle. Available from: https://www.kaggle.com/datasets/muhammetvarl/laptop-price
  9. Molnar C. Interpretable Machine Learning. Morrisville (NC): Lulu Press; 2020.
  10. Gama J, Žliobaitė I, Bifet A, Pechenizkiy M, Bouchachia A. A survey on concept drift adaptation. ACM Computing Surveys. 2014;46(4):1-37. doi:10.1145/2523813
  11. Yao Q, Wang M, Escalante HJ, Guyon I, Hu YQ, Li YF, et al. Taking human out of learning applications: a survey on automated machine learning. [Preprint]. 2018. arXiv:1810.13306. doi:10.48550/arXiv.1810.13306.