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DataMining for Titanic Kaggle competition

###honorific-title-model.r Fills the missing age by using the honorific title, and take in account titles that belong to nobility, cabin class, port of embarkment, sex and if it has a family on ship or not. Uses simple boot cross validation on data.

Algorithm Kaggle Score
Logistic Regression 0.78947
Random Forests 0.78947
Support Vector Machines 0.79904

###discretized-age-gender-familyId.r Fills missing age values using a regression tree on initial features, plus family size and honorific title. Adds categorical age combined with sex, and a family id as a combination of surname and family size.

Algorithm Kaggle Score
Decision tree 0.81340
Naive Bayes Classifier 0.72249
Neural Network model 0.79426
Random forest with conditional inference trees 0.80383

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