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Supervised Machine Learning Algorithm to Classify Gender in Google Colaboratory
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Supervised machine learning uses the label dataset to classify data. In this paper supervised machine learning algorithm Random Forest Classifier and Support Vector Machine learning algorithm are used to classify gender. Google Colaboratory is a Cloud based service which is also known as Colab. Google Colab is based on Jupyter Notebook where machine learning and deep learning concepts can be implemented. Google Colab provides free access to GPU which is very much required to disseminate Random Forest and Support Vector Machine Learning concepts. This paper made an attempt to classify gender based on the person’s height, weight, and shoe size using Random Forest and Support Vector Machine. The result shows that Random Forest Classifier accuracy is relatively better when compared with Support Vector Machine learning algorithm.
Keywords
Gender Classification, Google Colab, Jupyter Notebook, Random Forest Classifier, Support Vector Classifier.
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- Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no., pp. 436 – 444, 2015. [Online]. Available: https://doi.org/10.1038/nature14539
- A. Brodtkorb, C. Dyken, T. R. Hagen, J. M. Hjelmervik, and O. O. Storaasli, “State-of-the-art in heterogeneous computing,” Scientific Programming, vol. 18, no. 1, pp. 1–33, 2010. https://doi.org/10.3233/SPR-2009-0296
- NVIDIA Corporation, “Tesla V100 performance guide: Deep learning and HPC applications,” NVIDIA Corporation Whitepaper, 2016.
- M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. H. Katz, A. Konwinski, G. Lee, D. A. Patterson, A. Rabkin, I. Stoica, and M. Zaharia, “Above the clouds: A Berkeley view of Cloud computing,” Tech. Rep. UCB/EECS-2009–28, EECS Dept., University of California, Berkeley, Tech. Rep., 2009. [Online]. Available: https://www2.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-28.pdf
- NVIDIA Corporation, “Introduction to NVIDIA GPU cloud,” NVIDIA Corporation Application Note, 2018.
- Google, “Colaboratory: Frequently asked questions,” 2018. [Online]. Available: https://research.google.com/colaboratory/faq.html. Accessed on: June 6, 2018.
- G. Juve, E. Deelman, K. Vahi, G. Mehta, B. Berriman, B. P. Berman, and P. Maechling, “Scientific workflow applications on Amazon EC2,” in 2009 5th IEEE Int. Conf. on E-Sci., Workshops, IEEE, 2009, pp. 59–66. https://doi.org/10.1109/ESCIW.2009.5408002
- K. R. Jackson, L. Ramakrishnan, K. Muriki, S. Canon, S. Cholia, J. Shalf, H. J. Wasserman, and N. J. Wright, “Performance analysis of high-performance computing applications on the Amazon web services cloud,” in 2010 IEEE Second Int. Conf. on Cloud Computing Technol. and Sci., pp. 159–168. [Online]. Available: 10.1109/CloudCom.2010.69
- R. R. Expósito, G. L. Taboada, S. Ramos, J. Touriño, and R. Doallo, “Performance analysis of HPC applications in the cloud,” Future Generation Comput. Syst., vol. 29, no. 1, pp. 218–229, 2013.
- N. Sharma, V. Jain, and A. Mishra, “An analysis of convolutional neural networks for image classification”, Procedia Comput. Sci., vol. 132, pp. 377–384, 2018. [Online]. Available: https://doi.org/10.1016/j.procs.2018.05.198
- M. Shaha and M. Pawar, “Transfer learning for image classification,” In 2018 Second Int. Conf. on Electronics, Communication, and Aerospace Technol. (ICECA), IEEE, pp. 656–660, 2018.
- J. P. Gujjar and R. P. Kumar H., “Sentimental analysis for running text in email conversation,” Int. J. of Comput. Sci. and Eng., vol. 9, no. 4, pp. 67 – 68, 2020.
- T. Manjunatha and P. Gujjar, “Performance analysis of Indian Inform. Technol. companies using DuPont Model,” IUP J. of Manage. Res., vol. 17, no. 4, pp. 7–14, 2018.
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