Utilize este identificador para referenciar este registo: http://hdl.handle.net/10362/32410
Título: Application of machine learning techniques for solving real world business problems : the case study - target marketing of insurance policies
Autor: Juozenaite, Ineta
Orientador: Castelli, Mauro
Palavras-chave: Machine Learning
Logistic Regression
Decision Tree CART
Artificial Neural Network
Backpropagation learning algorithm
Support Vector Machine
Kernel Gaussian
Data de Defesa: 9-Mar-2018
Resumo: The concept of machine learning has been around for decades, but now it is becoming more and more popular not only in the business, but everywhere else as well. It is because of increased amount of data, cheaper data storage, more powerful and affordable computational processing. The complexity of business environment leads companies to use data-driven decision making to work more efficiently. The most common machine learning methods, like Logistic Regression, Decision Tree, Artificial Neural Network and Support Vector Machine, with their applications are reviewed in this work. Insurance industry has one of the most competitive business environment and as a result, the use of machine learning techniques is growing in this industry. In this work, above mentioned machine learning methods are used to build predictive model for target marketing campaign of caravan insurance policies to achieve greater profitability. Information Gain and Chi-squared metrics, Regression Stepwise, R package “Boruta”, Spearman correlation analysis, distribution graphs by target variable, as well as basic statistics of all variables are used for feature selection. To solve this real-world business problem, the best final chosen predictive model is Multilayer Perceptron with backpropagation learning algorithm with 1 hidden layer and 12 hidden neurons.
URI: http://hdl.handle.net/10362/32410
Designação: Mestrado em Gestão de Informação
Aparece nas colecções:NIMS - Dissertações de Mestrado em Gestão da Informação

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