Application of Machine Learning and Data Mining with Python for Business Analysis.

Md Faisal Ibn Haque

Master's thesis

Abstract:

Organizations need to use business data to analsysis with different kinds of techniques and tools in order to analysis their business data. In this context, implementing machine learning (ML) and data mining (DM) analysis techniques and tools could be very helpful for dealing with business challenges. Therefore, in this master’s thesis, a comprehensive literature review is summarized to providing an overview of how machine learning and data mining techniques can be used to solve business issues with analytical actions. In this section briefly described data mining stanard processes, data mining architecture and applications that has been widely used in the business field, such as fraud detection, target marketing, and safe online transactions. There are various types of machine learning algorithms mentioned in the field, including supervised, unsupervised, semi-supervised, and reinforcement learning. And also, how machine learning algorithms are applied to the datasets and are evaluated using performance metrics. Use the Python programming language and its standard libraries, such as Numpy, Scipy, Matplotlib, and Scikit-Learn, as an analytical tools for the practical section. These open-source Python programs are capable of performing data preparation, statistical analysis, data insight visualization, ML modeling and evaluation in order to discover hidden patterns and significant features in the dataset to compete prediction model.

Keywords

Data mining, Machine learning, Python library, Data visualization, Python, Data analysis, Statistics, Data warehosue, Algorithms

Language used: English

Date on which the thesis was submitted / produced: 1. 12. 2022

Thesis defence

• Supervisor: doc. Ing. Jan Tyrychtr, Ph.D.

Czech University of Life Sciences Prague

Faculty of Economics and Management

Master programme / field:
Systems Engineering and Informatics / Informatics