Phone Price Prediction Using ML

Registration ID: IJNRD_210468

Published ID: IJNRD2312144

DOI: Click Here to Get

G.Chamundeswari , K. Srihari Teja , Gorle Vasu , K.Uma Gayatri , K.Vikas

preprocessing, machine learning, metrics, predict, specifications

The market for mobile phones in india is highly competitive, with new models being introduced frequently. Consumers are always looking for the latest technology and features, and they are willing to pay a premium price for it. In this paper, we propose a machine learning-based approach to predict the price of mobile phones based on various features such as brand, model, screen type, camera quality, and battery life. A dataset of mobile phones is collected with their corresponding features and prices from various online retailers. The data is processed and then different machine learning algorithms are applied such as linear regression, decision trees, and random forests to predict the price of mobile phones. The performance of the algorithms is evaluated using metrics such as root mean squared error, R-squared value and mean absolute error. Finally, a model is selected based on the performance. The proposed system will help rural Indians in purchasing phones within their budget and with optimal specifications.

Published Paper   E-Certificate

G.Chamundeswari, K. Srihari Teja, Gorle Vasu, K.Uma Gayatri, & K.Vikas (December-2023). Phone Price Prediction Using ML. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(12), b247-b252. https://ijnrd.org/papers/IJNRD2312144.pdf

Volume 8 Issue 12, December-2023

Pages : b247-b252

Paper Reg. ID: IJNRD_210468

Published Paper Id: IJNRD2312144

Downloads: 000121993

Research Area: Computer Science & Technology 

Country: ELURU, Andhra Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2312144.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2312144

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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