Human Centric Computing Applications for Laptop Price Prediction

Mehboob Zahedi, Danish Jamal, Abhishek Das
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Abstract

With the rapid enhancement of modern technology, we are more engaged with online shopping due to its high comfort, ease to use, safety etc. So we find a problem for laptop product evaluation in the online as well as offline market. The demand for laptops were rapidly increased after the lockdown in India. In the June quarter of 2021, 4.1 million units were shipped and which is the highest shipment in five years. In laptops, the price is acquired from its RAM, ROM, CPU, GPU, Touch screen, model, trends etc. Sometimes it is very much difficult for the customer as well as the retailer to fix a price with the certain characteristics of laptops so that both can evaluate the price and be satisfied with it. So we are going to develop a model for predicting the laptop price as per its properties. Because of any casual customer, this model will help in selecting and deciding on a laptop whether to buy or not, and also will reduce the time and effort that anyone will have to spend manually researching the market price. This paper will focus on Human-centric computing applications for laptop price prediction because it can be analyzed by those well- structured data that itself enhanced machine learning techniques, easily representable as a set of qualified parameters etc. So, we will develop an attribute-based prediction model for laptops using Regression machine learning algorithm.
以人为本的电脑价格预测应用
随着现代科技的飞速发展,网上购物以其高舒适性、易用性、安全性等优点吸引了越来越多的人。因此,无论是线上市场还是线下市场,我们都发现了笔记本电脑产品评估的一个问题。在印度封锁后,对笔记本电脑的需求迅速增加。在2021年6月季度,出货量为410万台,这是五年来的最高出货量。在笔记本电脑中,价格取决于它的RAM, ROM, CPU, GPU,触摸屏,型号,趋势等。有时,顾客和零售商很难根据笔记本电脑的某些特性确定价格,以便双方都能评估价格并对其感到满意。因此,我们将开发一个模型,根据笔记本电脑的性能来预测其价格。因为对于普通消费者来说,这种机型将有助于他们选择和决定是否购买笔记本电脑,也将减少人们花费在手工研究市场价格上的时间和精力。本文将重点关注以人为中心的笔记本电脑价格预测应用,因为它可以通过那些结构良好的数据进行分析,这些数据本身增强了机器学习技术,很容易表示为一组合格的参数等。因此,我们将使用回归机器学习算法开发基于属性的笔记本电脑预测模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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