Prediction of Mobile Phone Price Using Machine Learning Classifiers

Year : 2024 | Volume : 11 | Issue : 02 | Page : 101 108
    By

    Gosia Sandal Parveen,

  • Aqeel Khalique,

  • Rahbre Islam,

  • Imran Hussain,

  1. Student, Department of Computer Science & Engineering, School of Engineering Sciences & Technology, Jamia Hamdard, New Delhi, India
  2. Assistant Professor, Department of Computer Science & Engineering, School of Engineering Sciences & Technology, Jamia Hamdard, New Delhi, India
  3. Research Scholar, Department of Computer Science & Engineering, School of Engineering Sciences & Technology, Jamia Hamdard, New Delhi, India
  4. Assistant Professor, Department of Computer Science & Engineering, School of Engineering Sciences & Technology, Jamia Hamdard, New Delhi, India

Abstract

One cannot imagine one’s life without mobile phones; in today’s digital era, mobile phones have become a necessity for everyone to fulfil their various demands like messaging, communication, entertainment, productivity, research, shopping and many more. In a thriving market of mobile phones where new smartphones are launched every year with new advanced features and various designs, determining the expense of a mobile can be a trouble-some tasks for consumers. In this study, we propose an approach to predict mobile phone prices using machine learning. The dataset, sourced from kaggle.com, encompasses various attributes such as RAM, ROM, storage capacity, display size, battery life, and camera quality among others. While several machine learning algorithms exist including linear regression, decision trees, and Naive Bayes, our methodology focuses on Support Vector Machine (SVM), Decision Tree, Random Forest, and K-Nearest Neighbors (KNN) for training the model and assessing its accuracy, F1 score, precision, and recall. Through rigorous experimentation we decipher the most effective algorithms of Machine Learning and feature sets, to predict the price of mobile phone accurately. Such a model can provide valuable help rights and assistance to the consumers while buying new mobile phones and the model also helps the manufacturers to adapt to changing market conditions based on consumer behavior, thus making more profit and market share.

Keywords: Prediction model, machine learning, Support Vector Machine (SVM), Decision Tree, Random Forest, and K-Nearest Neighbors (KNN), RAM, ROM

[This article belongs to Journal of Artificial Intelligence Research & Advances ]

How to cite this article:
Gosia Sandal Parveen, Aqeel Khalique, Rahbre Islam, Imran Hussain. Prediction of Mobile Phone Price Using Machine Learning Classifiers. Journal of Artificial Intelligence Research & Advances. 2024; 11(02):101-108.
How to cite this URL:
Gosia Sandal Parveen, Aqeel Khalique, Rahbre Islam, Imran Hussain. Prediction of Mobile Phone Price Using Machine Learning Classifiers. Journal of Artificial Intelligence Research & Advances. 2024; 11(02):101-108. Available from: https://journals.stmjournals.com/joaira/article=2024/view=155857


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Regular Issue Subscription Review Article
Volume 11
Issue 02
Received 24/05/2024
Accepted 22/06/2024
Published 10/07/2024
806

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