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Systematic understanding and critical awareness of discipline knowledge of machine learning and data mining including the theory and methodology, and the current development of technologies.
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An understanding and critical knowledge for applying machine learning and data mining techniques for decision making and gaining insight from data.
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Independent ability of evaluating and applying appropriate machine learning and data mining techniques for practical problems.
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Independent ability of defining, explaining and interpreting the results obtained from machine learning and data mining.
This is a dataset from Kaggle, Credit Card Fraud Detection.
This data visualisation programming language is R and development platform is R Studio. It is a R Markdown file. Also, the source code will be uploaded as Main.Rmd.
- Which proposed machine learning models do perform better in fraud detection?
After logistic regression, results revealed that ten attributes are significant to the fraud transaction (Class).
- Using the dataset only included these ten significant attributes and Class, which proposed machine learning models do perform better in fraud detection?