The overall purpose of this thesis is applying machine learning, random forest and multilayer perceptron specifically, to solve realistic problem. The thesis consists of 3 parts, “Introduction to machine learning” which briefly introduce the concept of machine learning and its application, “Theoretical background” presents the concept of classifiers will be used in solving problem, lastly “Case study” applies all above theories into real-life problem. After solving, there are some important values can be concluded, such as interesting insights into dataset and how to build the best possible prediction model, etc.
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The overall purpose of this thesis is applying machine learning, random forest and multilayer perceptron specifically, to solve realistic problem. The thesis consists of 3 parts, “Introduction to machine learning” which briefly introduce the concept of machine learning and its application, “Theoretical background” presents the concept of classifiers will be used in solving problem, lastly “Case study” applies all above theories into real-life problem. After solving, there are some important values can be concluded, such as interesting insights into dataset and how to build the best possible prediction model, etc.
