A hybrid model for predicting Mobile Price Range using machine learning techniques

Sakib, Ahsanul Hoque; Shakir, Asif Khan; Sutradhar, Shanjoy; Saleh, Md. Abu; Akram, Washim; Biplop, Khalid Been MD.Badruzzaman

doi:10.1145/3512850.3512860

Cited by 5 publications

(1 citation statement)

References 3 publications

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“…Additionally, within the context of cooperative games, examining the relative contributions of different variables can enhance the precision in predicting mobile phone prices [7]. The price of a mobile phone can be predicted employing a random forest machine learning technique, which enables parameter pruning in the model and consequently enhances the accuracy and precision of the decision tree, bring benefits for prediction of mobile phone prices [8]. Models were constructed using XGB Regressor and Support Vector Machine methods, utilizing historical data on mobile phone price, which helps to improve the accuracy of predicting mobile phone prices [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Mobile phone price prediction: A comparative study among four models

Liang

2024

ACE

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As science and technology is advancing by leaps and bounds, mobile phones have become part and parcel of people's life. Because the different models of mobile phones which have different structural foundations, the prices of mobile phones are constantly fluctuating. Mobile phone prices forecasts are becoming more precise as artificial intelligence develops. This article compares various machine learning approaches, and the importance of the variables is ranked in order to determine the most accurate way to forecast the prices of mobile phones. The machine learning techniques used are linear regression (LR), random forest regressor (RFR), XGB Regressor and Support Vector Machine regressor (SVM). In order to determine which model predicts the most accurate mobile phone prices, R^2 evaluation is used. The XGB Regressor model had the greatest score (R-squared = 0.95) for prediction of mobile phone prices, compared to the other three models. In a word, with XGB Regressor methodology as a priority for future mobile phone price predicting, which can improve the accuracy of price predicting.

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Section: Literature Reviewmentioning

confidence: 99%