Authors :
Neha Joshi; Jyoti Sabrad
Volume/Issue :
Volume 11 - 2026, Issue 3 - March
Google Scholar :
https://tinyurl.com/2ntf7etu
Scribd :
https://tinyurl.com/4stpr4dn
DOI :
https://doi.org/10.38124/ijisrt/26mar1691
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
This research work presents a granular analysis of the contemporary laptop market, focusing on the intersection
of hardware configurations, pricing strategies, and consumer satisfaction. By examining a curated dataset of 48 distinct
laptop models, the study investigates how technical specifications including processor architecture, RAM capacity, storage
media, and display technology influence market positioning and user perception. The scope of this analysis covers a broad
economic spectrum, with unit prices ranging from ₹23,990 to ₹81,990, providing a comprehensive view of both entry-level
and mid-to-high-tier segments. Utilizing a combination of data visualization and statistical inference, the paper quantifies
the relationship between hardware utility and customer ratings (ranging from 3.5 to 4.7).
Keywords :
Data Visualization, Processor Architecture, RAM Capacity, Storage Media, Display Technology, Economic Spectrum, Statistical Inference.
References :
- "Data Visualization Fundamentals - Web Scraping Laptop Dataset," in Laptop Market Analysis: Comprehensive Statistical Insights, E-Commerce Research Laboratory, 2024. [Online]. Available: https://pandas.pydata.org/docs/user_guide/index.html. [Accessed: Feb. 21], [2024].
- W. McKinney, "Pandas: A foundational Python library for data analysis and manipulation," J. Open Source Softw., vol. 5, no. 52, p. 1815, Aug. 2020. [Online]. Available: https://pandas.pydata.org/. [Accessed: Feb. 21], [2024].
- T. Oliphant, "NumPy: A fundamental package for numerical computing in Python," Comput. Sci. Eng., vol. 9, no. 3, pp. 10–20, May 2007.
- M. Waskom, "Seaborn: Statistical data visualization," J. Open Source Softw., vol. 6, no. 60, p. 3021, 2021. [Online]. Available: https://seaborn.pydata.org/. [Accessed: Feb. 21], [2024].
- P. Virtanen et al., "SciPy 1.0: Fundamental algorithms for scientific computing in Python," Nat. Methods, vol. 17, no. 3, pp. 261–272, 2020.
- M. Trusov, A. V. Bodapati, and R. E. Bucklin, "Determining influential users in internet social networks," J. Mark. Res., vol. 47, no. 4, pp. 643–658, Aug. 2010.
- S. Chib and E. Greenberg, "Understanding the Metropolis-Hastings algorithm," Amer. Statistician, vol. 49, no. 4, pp. 327–335, Nov. 1995.
- J. F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson, Multivariate Data Analysis, 8th ed. Cengage Learning, 2019.
- A. G. Bluman, Elementary Statistics: A Step by Step Approach, 10th ed. McGraw-Hill Education, 2018.
- D. C. Montgomery and G. C. Runger, Applied Statistics and Probability for Engineers, 7th ed. John Wiley & Sons, 2018.
- S. Chatterjee and A. S. Hadi, Regression Analysis by Example, 5th ed. John Wiley & Sons, 2015.
- R. A. Johnson and D. W. Wichern, Applied Multivariate Statistical Analysis, 6th ed. Pearson Education, 2014.
- J. W. Tukey, "Exploratory data analysis," Addison-Wesley Series in Behavioral Science: Quantitative Methods, 1977.
- D. Freedman, R. Pisani, and R. Purves, Statistics, 4th ed. W. W. Norton & Company, 2007.
- C. Rasmussen and Z. Ghahramani, "Infinite mixtures of Gaussian process experts," in Advances in Neural Information Processing Systems, T. Dietterich, S. Becker, and Z. Ghahramani, Eds. MIT Press, 2002, vol. 14, pp. 881–888.
This research work presents a granular analysis of the contemporary laptop market, focusing on the intersection
of hardware configurations, pricing strategies, and consumer satisfaction. By examining a curated dataset of 48 distinct
laptop models, the study investigates how technical specifications including processor architecture, RAM capacity, storage
media, and display technology influence market positioning and user perception. The scope of this analysis covers a broad
economic spectrum, with unit prices ranging from ₹23,990 to ₹81,990, providing a comprehensive view of both entry-level
and mid-to-high-tier segments. Utilizing a combination of data visualization and statistical inference, the paper quantifies
the relationship between hardware utility and customer ratings (ranging from 3.5 to 4.7).
Keywords :
Data Visualization, Processor Architecture, RAM Capacity, Storage Media, Display Technology, Economic Spectrum, Statistical Inference.