Smartphone Price Prediction in Retail Industry Using Machine Learning Techniques

Chandrashekhara, K.; Thungamani, M.; N, Gireesh Babu C; Manjunath, T N

doi:10.1007/978-981-13-5802-9_34

Cited by 11 publications

(6 citation statements)

References 6 publications

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“…The machine learning algorithm demonstrates exceptional accuracy in accurately forecasting the price scope of mobile phones [2]. When predicting mobile phone prices, a diverse range of variables related to mobile phones are utilized to optimize the impact factor and achieve a predicted mobile phone price that aligns with real-world observations [3]. Employing supervised machine learning techniques in the domain of machine learning to train models and generate predictions, thereby determining the most suitable algorithm for the given dataset to make phone price predictions [4].…”

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%

Mobile phone price prediction: A comparative study among four models

Liang

2024

ACE

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“…Unlike the previous methods, regression models were utilized in [14]. These three regression methods were Support vector regression (SVR), Multiple linear regression, and Backpropagation neural network.…”

Section: Related Studymentioning

confidence: 99%

“…Furthermore, prediction and analysis of data price have played a crucial role in today's economy [1]. Examples of price prediction include stock prediction [2,3,4], electricity price forecasting [5], ticket pricing [6,7], cryptocurrency forecasting [8,9,10,11,12], product price prediction such as house [13] and smartphone [14].…”

Section: Introductionmentioning

confidence: 99%

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Multimodal price prediction

Zehtab-Salmasi,

Feizi-Derakhshi,

Nikzad-Khasmakhi

et al. 2020

Preprint

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Valorization is one of the most heated discussions in the business community, and commodities valorization is one subset in this task. Features of a product is an essential characteristic in valorization and features are categorized into two classes: graphical and non-graphical. Nowadays, the value of products is measured by price. The goal of this research is to achieve an arrangement to predict the price of a product based on specifications of that. We propose five deep learning models to predict the price range of a product, one unimodal and four multimodal systems. The multimodal methods predict based on the image and non-graphical specification of product. As a platform to evaluate the methods, a cellphones dataset has been gathered from GSMArena. In proposed methods, convolutional neural network is an infrastructure. The experimental results show 88.3% F1-score in the best method.

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“…E-commerce is a significant step that has changed the way of doing business, especially in retail market, plays an important role in the development of the national economy in the 21st century. Simultaneously with the development of the E-commerce industry, the massive growth of new terms such as Machine Learning (ML), Data Science (DS), Deep Learning (DL), Artificial Intelligence (AI) have also been paid attention due to their impact on the retailing market (Chandrashekhara et al, 2019;Jia et al, 2013;Narayana et al, 2021;Pundir et al, 2020). Akter and Wamba (2016) believe that data plays an important role in E-commerce and all business decisions.…”

Section: Introductionmentioning

confidence: 99%

E-commerce Price Suggestion Algorithm – A Machine Learning Application

Quang,

Thao,

Nhi

et al. 2022

Proceedings of the International Conference on Industrial Engineering and Operations Management

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E-commerce is a key move that has altered the way businesses are conducted, particularly in the retail industry.In the e-commerce business, pricing is one of the most significant aspects in determining profitability, and closely linked to the company's sales. The rise of online markets has necessitated the creation of a Machine Learning tool for pricing suggestions. The aim of this study is to select a machine learning model to create a price suggestion tool for Ecommerce enterprises. Three machine learning algorithms -Linear Regression, Random Forest and LightGBM -are tested on a dataset of an Ecommerce enterprise to indicate the performance of models when using in a dataset with several features and millions of rows. The study also processes to make improvement the output of models, including parameters tuning, feature selection or remove outstanding values. The result shows that Light GBM after Grid search CV process outperforms in terms of both prediction error and processing time.

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Smartphone Price Prediction in Retail Industry Using Machine Learning Techniques

Cited by 11 publications

References 6 publications

Mobile phone price prediction: A comparative study among four models

Mobile phone price prediction: A comparative study among four models

Multimodal price prediction

E-commerce Price Suggestion Algorithm – A Machine Learning Application

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