ARCHIVES
Research Article
Smartphone Price Prediction Using Machine Learning Techniques
Richard Honey1
Department of Economics, Christ (Deemed to be University), Bangalore, Delhi NCR Campus, India.
Published Online: March-April 2023
Pages: 603-607
Cite this article
↗ 10.59256/ijire.2023040233References
[1]. Asim, M., & Khan, Z. (2018, March). Mobile Price Class prediction using Machine Learning Techniques. International Journal of
Computer Applications (0975 – 8887) Volume 179 – No.29. https://doi.org/10.5120/ijca2018916555
[2]. Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/a:1010933404324
[3]. Cavallo, D., Stocchero, M., Gorrasi, G., Logrieco, A., &Attolico, G. (2017). Contactless and non-destructive chlorophyll content
prediction by random forest regression: A case study on fresh-cut rocket leaves. Computers and Electronics in Agriculture, 140, 303–
310. https://doi.org/10.1016/j.compag.2017.06.012
[4]. Chandrashekhara, K. T., M, T., Babu, C. N. G., &Manjunath, T. N. (2019). Smartphone Price Prediction in Retail Industry Using
Machine Learning Techniques. Emerging Research in Electronics, Computer Science and Technology. Lecture Notes in Electrical
Engineering, vol 545. https://doi.org/10.1007/978-981-13-5802-9_34
[5]. Hjerpe, A. (2016). Computing Random Forests Variable Importance Measures (VIM) on Mixed Continuous and Categorical Data.
KTH Royal Institute of Technology School of Computer Science and Communication. https://www.divaportal.org/smash/get/diva2:921542/FULLTEXT01.pdf
[6]. Liaw, A., & Wiener, M. (2022). Classification and Regression by Random Forest. R News, 2.
https://cogns.northwestern.edu/cbmg/LiawAndWiener2002.pdf
[7]. Menze, B. H., Kelm, B. M., Masuch, R., Himmelreich, U., Bachert, P., Petrich, W., &Hamprecht, F. A. (2009). A comparison of
random forest and its gini importance with standard chemometric methods for the feature selection and classification of spectral data.
BMC Bioinformatics, 10(1). https://doi.org/10.1186/1471-2105-10-213
[8]. Pekel, E. (2020). Estimation of soil moisture using decision tree regression. Theoretical and Applied Climatology, 139(3–4), 1111–
1119. https://doi.org/10.1007/s00704-019-03048-8
[9]. Pudaruth, S. (n.d.). Predicting the Price of Used Cars using Machine Learning Techniques. International Journal of Information &
Computation Technology, ISSN 0974-2239 Volume 4, Number 7, 753–764.
[10]. Pushpa, S. K., Manjunath, T. N., Mrunal, T. V., Singh, A., &Suhas, C. (2017). Class result prediction using machine learning. Institute
of Electrical and Electronics Engineers. https://doi.org/10.1109/smarttechcon.2017.8358559
[11]. Quader, N., &Gani, M. O. (2017). A machine learning approach to predict movie box-office success. Institute of Electrical and
Electronics Engineers. https://doi.org/10.1109/iccitechn.2017.8281839
[12]. Sawant, R., Jangid, Y. K., Tiwari, T. K., Jain, S., & Gupta, A. (2018a). Comprehensive Analysis of Housing Price Prediction in Pune
Using Multi-Featured Random Forest Approach. International Conference on Computing Communication Control and Automation.
https://doi.org/10.1109/iccubea.2018.8697402
[13]. Subhiksha, S., Thota, S., & Sangeetha, J. (2019). Prediction of Phone Prices Using Machine Learning Techniques. Advances in
Intelligent Systems and Computing, Volume 1079. https://doi.org/10.1007/978-981-15-1097-7_65
[14]. Surjuse, V., Lohakare, S., Barapatre, A., &Chapke, A. (2022, January). Laptop Price Prediction using Machine Learning. International
Journal of Computer Science and Mobile Computing, Vol. 11, Issue. 1, January 2022, Pg.164 – 168.
https://doi.org/10.47760/ijcsmc.2022.v11i01.021
[15]. Tziridis, K., Kalampokas, T., Papakostas, G. A., &Diamantaras, K. I. (2017). Airfare prices prediction using machine learning
techniques. Institute of Electrical and Electronics Engineers. https://doi.org/10.23919/eusipco.2017.8081365
[16]. Xu, Z., Lian, J., Bin, L., Hua, K., Xu, K., & Chan, H. Y. (2019). Water Price Prediction for Increasing Market Efficiency Using Random
Forest Regression: A Case Study in the Western United States. Water, 11(2), 228. https://doi.org/10.3390/w11020228
[17]. Zehtab-Salmasi, A., Feizi-Derakhshi, A., Nikzad-Khasmakhi, N., Asgari-Chenaghlu, M., &Nabipour, S. (2020). Multimodal Price
Prediction. Annals of Data Science. https://doi.org/10.1007/s40745-021-00326-z
[18]. Zhong, S., Xie, X., & Lin, L. (2015). Two-layer random forests model for case reuse in case-based reasoning. Expert Systems With
Applications, 42(24), 9412–9425. https://doi.org/10.1016/j.eswa.2015.08.005
Computer Applications (0975 – 8887) Volume 179 – No.29. https://doi.org/10.5120/ijca2018916555
[2]. Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/a:1010933404324
[3]. Cavallo, D., Stocchero, M., Gorrasi, G., Logrieco, A., &Attolico, G. (2017). Contactless and non-destructive chlorophyll content
prediction by random forest regression: A case study on fresh-cut rocket leaves. Computers and Electronics in Agriculture, 140, 303–
310. https://doi.org/10.1016/j.compag.2017.06.012
[4]. Chandrashekhara, K. T., M, T., Babu, C. N. G., &Manjunath, T. N. (2019). Smartphone Price Prediction in Retail Industry Using
Machine Learning Techniques. Emerging Research in Electronics, Computer Science and Technology. Lecture Notes in Electrical
Engineering, vol 545. https://doi.org/10.1007/978-981-13-5802-9_34
[5]. Hjerpe, A. (2016). Computing Random Forests Variable Importance Measures (VIM) on Mixed Continuous and Categorical Data.
KTH Royal Institute of Technology School of Computer Science and Communication. https://www.divaportal.org/smash/get/diva2:921542/FULLTEXT01.pdf
[6]. Liaw, A., & Wiener, M. (2022). Classification and Regression by Random Forest. R News, 2.
https://cogns.northwestern.edu/cbmg/LiawAndWiener2002.pdf
[7]. Menze, B. H., Kelm, B. M., Masuch, R., Himmelreich, U., Bachert, P., Petrich, W., &Hamprecht, F. A. (2009). A comparison of
random forest and its gini importance with standard chemometric methods for the feature selection and classification of spectral data.
BMC Bioinformatics, 10(1). https://doi.org/10.1186/1471-2105-10-213
[8]. Pekel, E. (2020). Estimation of soil moisture using decision tree regression. Theoretical and Applied Climatology, 139(3–4), 1111–
1119. https://doi.org/10.1007/s00704-019-03048-8
[9]. Pudaruth, S. (n.d.). Predicting the Price of Used Cars using Machine Learning Techniques. International Journal of Information &
Computation Technology, ISSN 0974-2239 Volume 4, Number 7, 753–764.
[10]. Pushpa, S. K., Manjunath, T. N., Mrunal, T. V., Singh, A., &Suhas, C. (2017). Class result prediction using machine learning. Institute
of Electrical and Electronics Engineers. https://doi.org/10.1109/smarttechcon.2017.8358559
[11]. Quader, N., &Gani, M. O. (2017). A machine learning approach to predict movie box-office success. Institute of Electrical and
Electronics Engineers. https://doi.org/10.1109/iccitechn.2017.8281839
[12]. Sawant, R., Jangid, Y. K., Tiwari, T. K., Jain, S., & Gupta, A. (2018a). Comprehensive Analysis of Housing Price Prediction in Pune
Using Multi-Featured Random Forest Approach. International Conference on Computing Communication Control and Automation.
https://doi.org/10.1109/iccubea.2018.8697402
[13]. Subhiksha, S., Thota, S., & Sangeetha, J. (2019). Prediction of Phone Prices Using Machine Learning Techniques. Advances in
Intelligent Systems and Computing, Volume 1079. https://doi.org/10.1007/978-981-15-1097-7_65
[14]. Surjuse, V., Lohakare, S., Barapatre, A., &Chapke, A. (2022, January). Laptop Price Prediction using Machine Learning. International
Journal of Computer Science and Mobile Computing, Vol. 11, Issue. 1, January 2022, Pg.164 – 168.
https://doi.org/10.47760/ijcsmc.2022.v11i01.021
[15]. Tziridis, K., Kalampokas, T., Papakostas, G. A., &Diamantaras, K. I. (2017). Airfare prices prediction using machine learning
techniques. Institute of Electrical and Electronics Engineers. https://doi.org/10.23919/eusipco.2017.8081365
[16]. Xu, Z., Lian, J., Bin, L., Hua, K., Xu, K., & Chan, H. Y. (2019). Water Price Prediction for Increasing Market Efficiency Using Random
Forest Regression: A Case Study in the Western United States. Water, 11(2), 228. https://doi.org/10.3390/w11020228
[17]. Zehtab-Salmasi, A., Feizi-Derakhshi, A., Nikzad-Khasmakhi, N., Asgari-Chenaghlu, M., &Nabipour, S. (2020). Multimodal Price
Prediction. Annals of Data Science. https://doi.org/10.1007/s40745-021-00326-z
[18]. Zhong, S., Xie, X., & Lin, L. (2015). Two-layer random forests model for case reuse in case-based reasoning. Expert Systems With
Applications, 42(24), 9412–9425. https://doi.org/10.1016/j.eswa.2015.08.005
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