Gosia Sandal Parveen,
Aqeel Khalique,
Rahbre Islam,
Imran Hussain,
- Student Department of Computer Science & Engineering, SEST, Jamia Hamdard, New Delhi Delhi India
- Assistant Professor Department of Computer Science & Engineering, SEST, Jamia Hamdard, New Delhi Delhi India
- Assistant Professor Department of Computer Science & Engineering, SEST, Jamia Hamdard, New Delhi Delhi India
- Research Scholar Department of Computer Science & Engineering, SEST, Jamia Hamdard, New Delhi 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(joaira)]
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Journal of Artificial Intelligence Research & Advances
| Volume | 11 |
| Issue | 02 |
| Received | May 24, 2024 |
| Accepted | June 22, 2024 |
| Published | July 10, 2024 |