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Titanic: Machine Learning from Disaster
Added on -2019-09-22
The aim is to able to find out whether any specific features was responsible for the survival or death of an individual on the titanic disaster. This a supervised classification problem, since we already have the labels for the various attributes, the definition of the attributes is given in the data overview.
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Data:Title:Titanic: Machine Learning from DisasterLink:https://www.kaggle.com/c/titanic/dataOverview:The data has been split into two groups:training set (train.csv)test set (test.csv)The training setwas used to build your machine learning models. The test setshould be used to see how well your model performs on unseen data. For the test set, we do not provide the ground truth for each passenger. It is your job to predict these outcomes. For each passenger in the test set, use the model you trained to predict whether or not they survived the sinking of the Titanic.Data DictionaryVariableDefinitionKeysurvivalSurvival0 = No, 1 = YesPclassTicket class1 = 1st, 2 = 2nd, 3 = 3rdSexSexAgeAge in yearsSibsp# of siblings / spouses aboard the TitanicParch# of parents / children aboard the TitanicTicketTicket numberFarePassenger fare
CabinCabin numberembarkedPort of EmbarkationC = Cherbourg, Q = Queenstown, S = SouthamptonVariable Notespclass: A proxy for socio-economic status (SES)1st = Upper2nd = Middle3rd = Lowerage: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5sibsp: The dataset defines family relations in this way...Sibling = brother, sister, stepbrother, stepsisterSpouse = husband, wife (mistresses and fiancés were ignored)parch: The dataset defines family relations in this way...Parent = mother, fatherChild = daughter, son, stepdaughter, stepsonSome children travelled only with a nanny, therefore parch=0 for them.Question 2:Please check the twbx file.Question 3:
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