Machine Learning Algorithms for Prediction of Mobile Phone Prices
- Title
- Machine Learning Algorithms for Prediction of Mobile Phone Prices
- Creator
- Jose J.; Raj V.; Seaban S.V.; Jose D.V.
- Description
- The drastic growth of technology helps us to reduce the man work in our day-to-day life. Especially mobile technology has a vital role in all areas of our lives today. This work focused on a data-driven method to estimate the price of a new smartphone by utilizing historical data on smartphone pricing, and key feature sets to build a model. Our goal was to forecast the cost of the phone by using a dataset with 21 characteristics related to price prediction. Logistic regression (LR), decision tree (DT), support vector machine (SVM), Naive Bayes algorithm (NB), K-nearest neighbor (KNN) algorithm, XGBoost, and AdaBoost are only a few of the popular machine learning techniques used for the prediction. The support vector machine achieved the highest accuracy (97%) compared to the other four classifiers we tested. K-nearest neighbors 94% accuracy was close to that of the support vector machine. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
- Source
- Lecture Notes in Networks and Systems, Vol-537 LNNS, pp. 81-89.
- Date
- 2023-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- K-nearest neighbor; Machine learning; Mobile phone; Phone price prediction; Price range; Support vector machine
- Coverage
- Jose J., Department of Computer Science, Rajagiri College of Social Sciences, Kalamassery, India; Raj V., Department of Computer Science, Rajagiri College of Social Sciences, Kalamassery, India; Seaban S.V., Department of Computer Science, Rajagiri College of Social Sciences, Kalamassery, India; Jose D.V., Department of Computer Science, Christ University, Bangalore, India
- Rights
- Restricted Access
- Relation
- ISSN: 23673370; ISBN: 978-981993009-8
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Jose J.; Raj V.; Seaban S.V.; Jose D.V., “Machine Learning Algorithms for Prediction of Mobile Phone Prices,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19795.