Download PDFOpen PDF in browser

DEMATEL-Based Feature Selection for Improved Laptop Price Prediction

12 pagesPublished: August 6, 2024

Abstract

Laptops have become an indispensable part of everyone’s lives. They are beneficial for quickening the pace when it comes to any task. They serve the purpose of communication through online interactions and streamline other operations. The different kinds of laptops on the market have varying features designed precisely for your day to day needs. We have proposed a laptop price predictor that aims to predict the price of laptops. The results of the current study aid in understanding and identifying the factors that customers consider vital when purchasing a laptop. Further, the relationships amongst the important criteria were also identified. DEMATEL(Decision making trial and evaluation laboratory) method is employed to analyse the relationship between the variables and identify the most important factors. Mutual information regression (MIR) method was employed for feature selection and finally the selected features were input to the various Machine Learning and Deep learning algorithms for price prediction. Our study revealed that LightGBM Regressor and XGB Regressor performed the best, with the highest r2 scores of 0.81 after hyperparameter tuning.

Keyphrases: deep learning, dematel, machine learning, mutual information regression, price prediction

In: Rajakumar G (editor). Proceedings of 6th International Conference on Smart Systems and Inventive Technology, vol 19, pages 61-72.

BibTeX entry
@inproceedings{ICSSIT2024:DEMATEL_Based_Feature_Selection,
  author    = {Amritha Gopakumar and Aathira Shine and Amina Ajim and Anjali T},
  title     = {DEMATEL-Based Feature Selection for Improved Laptop Price Prediction},
  booktitle = {Proceedings of 6th International Conference on Smart Systems and Inventive Technology},
  editor    = {Rajakumar G},
  series    = {Kalpa Publications in Computing},
  volume    = {19},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1762},
  url       = {/publications/paper/qk7h},
  doi       = {10.29007/h72z},
  pages     = {61-72},
  year      = {2024}}
Download PDFOpen PDF in browser