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The Survived column is the target variable. If Suvival = 1 the passenger survived, otherwise he's dead. The is the variable we're going to predict.The other variables describe the passengers.
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They are the features. PassengerId: and id given to each traveler on the boat. Pclass: the passenger class. It has three possible values: 1,2,3 (first, second and third class).
The Name of the passeger. The Sex. The Age. SibSp: number of siblings and spouses traveling with the passenger. Parch: number of parents and children traveling with the passenger. The ticket number.
The ticket Fare. The cabin number. The embarkation. This describe three possible areas of the Titanic from which the people embark.
Three possible values S,C,Q. In the previous part, we flirted with the data and spotted some interesting correlations.In this part, we'll see how to process and transform these variables in such a way the data becomes manageable by a machine learning algorithm.We'll also create, or 'engineer' additional features that will be useful in building the model.We'll see along the way how to process text variables like the passenger names and integrate this information in our model.We will break our code in separate functions for more clarity. Def getcombineddata : # reading train data train = pd. Readcsv ( './data/train.csv' ) # reading test data test = pd. Readcsv ( './data/test.csv' ) # extracting and then removing the targets from the training data targets = train. Survived train.
Drop ( 'Survived' , 1, inplace = True ) # merging train data and test data for future feature engineering # we'll also remove the PassengerID since this is not an informative feature combined = train. Append ( test ) combined.
Resetindex ( inplace = True ) combined. Drop ( 'index', 'PassengerId' , inplace = True, axis = 1 ) return combined. Extracting the passenger titlesWhen looking at the passenger names one could wonder how to process them to extract a useful information.If you look closely at these first examples:.
Braund, Mr. Owen Harris. Heikkinen, Miss. Laina. Oliva y Ocana, Dona.
Fermina. Peter, Master.
Michael JYou will notice that each name has a title in it! This can be a simple Miss. But it can be sometimes something more sophisticated like Master, Sir or Dona. In that case, we might introduce an additional information about the social status by simply parsing the name and extracting the title and converting to a binary variable.Let's see how we'll do that in the function below. Processing the agesWe have seen in the first part that the Age variable was missing 177 values. This is a large number ( 13% of the dataset).
Simply replacing them with the mean or the median age might not be the best solution since the age may differ by groups and categories of passengers.To understand why, let's group our dataset by sex, Title and passenger class and for each subset compute the median age.To avoid data leakage from the test set, we fill in missing ages in the train using the train set and we fill in ages in the test set using values calculated from the train set as well. Def fillage ( row ): condition = ( ( groupedmediantrain 'Sex' row 'Sex' ) & ( groupedmediantrain 'Title' row 'Title' ) & ( groupedmediantrain 'Pclass' row 'Pclass' ) ) return groupedmediantrain condition 'Age'. Values 0 def processage : global combined # a function that fills the missing values of the Age variable combined 'Age' = combined. Apply ( lambda row: fillage ( row ) if np. Isnan ( row 'Age' ) else row 'Age' , axis = 1 ) status ( 'age' ) return combined. Def processcabin : global combined # replacing missing cabins with U (for Uknown) combined. Fillna ( 'U', inplace = True ) # mapping each Cabin value with the cabin letter combined 'Cabin' = combined 'Cabin'.
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Map ( lambda c: c 0 ) # dummy encoding. Cabindummies = pd. Getdummies ( combined 'Cabin' , prefix = 'Cabin' ) combined = pd. Concat ( combined, cabindummies , axis = 1 ) combined.
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Drop ( 'Cabin', axis = 1, inplace = True ) status ( 'cabin' ) return combined. PclassSexAgeSibSpParchTicketFareTitleMasterTitleMissTitleMrTitleMrsTitleOfficerTitleRoyaltyEmbarkedCEmbarkedQEmbarkedSCabinACabinBCabinCCabinDCabinECabinFCabinGCabinTCabinU03male22.010A/5 2female38.010PC 17female26.000STON/O2. Def processticket : global combined # a function that extracts each prefix of the ticket, returns 'XXX' if no prefix (i.e the ticket is a digit) def cleanTicket ( ticket ): ticket = ticket.
Replace ( '.' , ' ) ticket = ticket. Replace ( '/', ' ) ticket = ticket. Split ticket = map ( lambda t: t. Strip , ticket ) ticket = filter ( lambda t: not t. Isdigit , ticket ) if len ( ticket ) 0: return ticket 0 else: return 'XXX' # Extracting dummy variables from tickets: combined 'Ticket' = combined 'Ticket'. Map ( cleanTicket ) ticketsdummies = pd.
Getdummies ( combined 'Ticket' , prefix = 'Ticket' ) combined = pd. Concat ( combined, ticketsdummies , axis = 1 ) combined.
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Drop ( 'Ticket', inplace = True, axis = 1 ) status ( 'Ticket' ) return combined. Def processfamily : global combined # introducing a new feature: the size of families (including the passenger) combined 'FamilySize' = combined 'Parch' + combined 'SibSp' + 1 # introducing other features based on the family size combined 'Singleton' = combined 'FamilySize'. Map ( lambda s: 1 if s 1 else 0 ) combined 'SmallFamily' = combined 'FamilySize'. Map ( lambda s: 1 if 2. In this part, we use our knowledge of the passengers based on the features we created and then build a statistical model. You can think of this model as a box that crunches the information of any new passenger and decides whether or not he survives.There is a wide variety of models to use, from logistic regression to decision trees and more sophisticated ones such as random forests and gradient boosted trees.We'll be using Random Forests. Random Froests has proven a great efficiency in Kaggle competitions.For more details about why ensemble methods perform well, you can refer to these posts:.Back to our problem, we now have to:.
Break the combined dataset in train set and test set. Use the train set to build a predictive model. Evaluate the model using the train set. Test the model using the test set and generate and output file for the submission.Keep in mind that we'll have to reiterate on 2. Until an acceptable evaluation score is achieved.
Trainedmodels = for model in models: model. Fit ( train, targets ) trainedmodels. Append ( model ) predictions = for model in trainedmodels: predictions. Append ( model. Predictproba ( test ):, 1 ) predictionsdf = pd.
DataFrame ( predictions ). T predictionsdf 'out' = predictionsdf. Mean ( axis = 1 ) predictionsdf 'PassengerId' = aux 'PassengerId' predictionsdf 'out' = predictionsdf 'out'. Map ( lambda s: 1 if s = 0.5 else 0 ) predictionsdf = predictionsdf 'PassengerId', 'out' predictionsdf. Columns = 'PassengerId', 'Survived'.
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