Python-based machine learning algorithm
Abstract
Machine learning is the study of instructions that are guided by a method. It occurs when machines initially follow a learning pattern before attempting to acquire various rules and information that determine how the machine will behave. With the aid of a case study in linear regression and logistic regression using the Python programming language, the different capabilities and practical applications of machine learning as a tool are examined in this article. By predicting a variety of occurrences that are taken into account in this study, machine learning algorithms are also shown to be coherent and effective.
References
Bhatia, V., & Bhatia, G. (2013a). Room temperature based fan speed control system using pulse width modulation technique. International Journal of Computer Applications, 81(5).
Bhatia, V., & Whig, P. (2013b). A secured dual tune multi frequency based smart elevator control system. International Journal of Research in Engineering and Advanced Technology, 4(1), 1163–2319.
Lahade, S. v, & Hirekhan, S. R. (2015a). Intelligent and adaptive traffic light controller (IA-TLC) using FPGA. 2015 International Conference on Industrial Instrumentation and Control (ICIC), 618–623.
Nadikattu, R. R. (2014a). Content analysis of American & Indian Comics on Instagram using Machine learning. International Journal of Creative Research Thoughts (IJCRT), ISSN, 2320–2882.
Rupani, A., Whig, P., Sujediya, G., & Vyas, P. (2017a). A robust technique for image processing based on interfacing of Raspberry-Pi and FPGA using IoT. 2017 International Conference on Computer, Communications and Electronics (Comptelix), 350–353.
Rupani, A., Whig, P., Sujediya, G., & Vyas, P. (2017b). A robust technique for image processing based on interfacing of Raspberry-Pi and FPGA using IoT. 2017 International Conference on Computer, Communications and Electronics (Comptelix), 350–353.
Sharma, A., Kumar, A., & Whig, P. (2015b). On the performance of CDTA based novel analog inverse low pass filter using 0.35 µm CMOS parameter. International Journal of Science, Technology & Management, 4(1), 594–601.
Whig, P. (2019a). A Novel Multi-Center and Threshold Ternary Pattern. International Journal of Machine Learning for Sustainable Development, 1(2), 1–10.
Whig, P. (2019d). Exploration of Viral Diseases mortality risk using machine learning. International Journal of Machine Learning for Sustainable Development, 1(1), 11–20.
Whig, P., & Ahmad, S. N. (2011a). On the performance of ISFET-based device for water quality monitoring. Int’l J. of Communications, Network and System Sciences, 4(11), 709.
Whig, P., & Ahmad, S. N. (2012a). A CMOS integrated CC-ISFET device for water quality monitoring. International Journal of Computer Science Issues, 9(4), 1694–1814.
Whig, P., & Ahmad, S. N. (2012f). Performance analysis of various readout circuits for monitoring quality of water using analog integrated circuits. International Journal of Intelligent Systems and Applications, 4(11), 103.
Whig, P., & Ahmad, S. N. (2013a). A novel pseudo-PMOS integrated ISFET device for water quality monitoring. Active and Passive Electronic Components, 2013.
Whig, P., & Ahmad, S. N. (2014a). Development of economical ASIC for PCS for water quality monitoring. Journal of Circuits, Systems and Computers, 23(06), 1450079.
Whig, P., & Ahmad, S. N. (2014c). Simulation of linear dynamic macro model of photo catalytic sensor in SPICE. COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering.