EXERCISE OF MACHINE LEARNING USING SOME PYTHON TOOLS AND TECHNIQUES

Ertan Mustafa Geldiev, Nayden Valkov Nenkov, Mariana Mateeva Petrova

Abstract


One of the goals of predictive analytics training using Python tools is to create a "Model" from classified examples that classifies new examples from a Dataset. The purpose of different strategies and experiments is to create a more accurate prediction model. The goals we set out in the study are to achieve successive steps to find an accurate model for a dataset and preserving it for its subsequent use using the python instruments. Once we have found the right model, we save it and load it later, to classify if we have "phishing" in our case. In the case that the path we reach to the discovery of the search model, we can ask ourselves how much we can automate everything and whether a computer program can be written to automatically go through the unified steps and to find the right model? Due to the fact that the steps for finding the exact model are often unified and repetitive for different types of data, we have offered a hypothetical algorithm that could write a complex computer program searching for a model, for example when we have a classification task. This algorithm is rather directional and does not claim to be all-encompassing. The research explores some features of Python Scientific Python Packages like Numpy, Pandas, Matplotlib, Scipy and scycit-learn to create a more accurate model. The Dataset used for the research was downloaded free from the UCI Machine Learning Repository (UCI Machine Learning Repository, 2017).

Keywords


machine learning, Predictive Analytics Training with Python, data sets

Full Text:

PDF

References


Brownlee, J. (2017). Machine Learning Mastery With Python Understand Your Data, Create Accurate Model sand Work Projects End-To-End, 123-145.

Guido S., Müller A. (2016, October), Introduction to Machine Learning with Python, A Guide for scikit-learn, 123-145.

Hamilton, H. (2009). Knowledge Discovery in Databases, Retrieved from http://www2.cs.uregina.ca/~dbd/cs831/index.html

HanJ., Kamber, M.(2006). Data Mining: Concepts and Techniques. Second Edition, 78-35

Idris I. (2016). Python Data Analysis Cookbook, ISBN-10: 178528228X, 123-456.

Nenkov, N., Tasinov, T., &Petrova, M. (2017). Software system for document management at the Faculty to University, 4th International Multidisciplinary Scientific Conference on Social Sciences and Arts, SGEM 2017. Conference Proceedings, VOL V Science and Society. Book 3, Education & Educational Research, Pages: 457-464, DOI:10.5593/SGEMSOCIAL 2017/35/S13.060, ISBN 978-619-7408-22-5, ISSN 2367-5659

Nenkov, N., Dyachenko, Y., Petrova, M., Bondarenko, G.,&Pustovit, V. (2017). Intelligent and Cognitive Technologies in Education of International Economic Relations Students and Human Resource Development in Enterprises: Methodology in Language. European Journal of Sustainable Development. Publisher: European Center of Sustainable Development, Rome, Italy, Vol 6D, No.4, pp.353-360,DOI: 10.14207/ejsd.2017.v6.n4.p353

Nenkov, N., Petrova, M.,&Dyachenko, Y. (2016). Intelligence Technologies in Management and Administration of Justice, 3rd International Multidisciplinary Scientific Conference on Social Sciences and Arts, SGEM 2016. BK 2: POLITICAL SCIENCES, LAW, FINANCE, ECONOMICS AND TOURISM CONFERENCE PROCEEDINGS, VOL V Book Series: International Multidisciplinary Scientific Conferences on Social Sciences and Arts, DOI: 10.5593/SGEMSOCIAL2016/B25/S07.050, Pages: 385-¬392, WOS: 000395727200050

Nenkov, N. Petrova, M. (2015). Instruments and criteria for research and analysis of Internet visibility of Bulgarian judicial institutions WEB-space, International Journal of Advanced Research in Artificial Intelligence (IJARAI), ISSN: 2165-4050(Print), 2165-4069(Online), Volume 4 (9) 2015, (DOI): http://dx.doi.org/10.14569/IJARAI.2015.040902, pp. 6-9.

Schneider, J., Moore, A. (1997). A locally Weighted Learning tutorial using Vizier 1.0.

Seguí, L. (2017). Introduction to Data Science A Python Approach to Concepts, Techniques and Applications, 5-28.

Witten, I., Frank E., Hall, M.,& Pal, Ch. (2017).Data Mining Practical Machine Learning Tools and Techniques Fourth Edition, 45-74.

UCI Machine Learning Repository. (2017). Retrieved from http://archive.ics.uci.edu/ml/index.php




DOI: http://dx.doi.org/10.12955/cbup.v6.1295

Refbacks

  • There are currently no refbacks.


Print ISSN 1805-997X, Online ISSN 1805-9961

(c) 2018 CBU Research Institute s.r.o.

For more information on the conference visit cbuic.cz