Titanic(Kaggle Competition Dataset)
Ranga Sudharshan
11/1/2020
INTRODUCTION
This project is a part of a kaggle competition that uses a dataset of details of passengers who were in the titanic ship. The goal of the project is to make a predictive model and predict whether a passenger in the test dataset suvived or not.
Dataset
Dataset for this project can be downloaded here
Data Dictionary Variable Definition Key survival Survival 0 = No, 1 = Yes pclass Ticket class 1 = 1st, 2 = 2nd, 3 = 3rd sex Sex
Age Age in years
sibsp # of siblings / spouses aboard the Titanic
parch # of parents / children aboard the Titanic
ticket Ticket number
fare Passenger fare
cabin Cabin number
embarked Port of Embarkation C = Cherbourg, Q = Queenstown, S = Southampton
- Variable Notes pclass: A proxy for socio-economic status (SES) 1st = Upper 2nd = Middle 3rd = Lower
age: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5
sibsp: The dataset defines family relations in this way… Sibling = brother, sister, stepbrother, stepsister Spouse = husband, wife (mistresses and fiancés were ignored)
parch: The dataset defines family relations in this way… Parent = mother, father Child = daughter, son, stepdaughter, stepson Some children travelled only with a nanny, therefore parch=0 for them.
train<- read.csv('train.csv', stringsAsFactors = FALSE)
test<- read.csv('test.csv', stringsAsFactors = FALSE)
#in order to combine both the test and training datasets, the columns needs to be same
test$is_train<- FALSE
train$is_train<- TRUE
test$Survived<- NA
titanic_complete<-rbind.data.frame(train,test)
table(titanic_complete$is_train)##
## FALSE TRUE
## 418 891In order to clean the dataset to proceed ahead with a model, a predictive model is structured to predict the missing values(NAs) in the dataset.
#checking the indexes and number of all the missing values in the complete dataframe
na_info<-apply(is.na(titanic_complete),2 , which)
str(na_info)## List of 13
## $ PassengerId: int(0)
## $ Survived : int [1:418] 892 893 894 895 896 897 898 899 900 901 ...
## $ Pclass : int(0)
## $ Name : int(0)
## $ Sex : int(0)
## $ Age : int [1:263] 6 18 20 27 29 30 32 33 37 43 ...
## $ SibSp : int(0)
## $ Parch : int(0)
## $ Ticket : int(0)
## $ Fare : int 1044
## $ Cabin : int(0)
## $ Embarked : int(0)
## $ is_train : int(0)#checking NA values
sum(is.na(titanic_complete$Embarked))## [1] 0table(titanic_complete$Embarked)##
## C Q S
## 2 270 123 914#replacing the missing values with S
titanic_complete[(titanic_complete$Embarked==''),'Embarked']<-'S'Building a regression model to find the NAs of the Ages and Fares of some of the passengers. Also,there are some outliers that could hamper the mode, hence we should filter the data and remove the outliers.
#getting a good idea of number of outliers in the fare column
boxplot(titanic_complete$Fare)
Building a regression model
#considering data which are less than the 5th quantile to remove the outliers
filter.boundary<-boxplot.stats(titanic_complete$Fare)$stats[5]
filter.data<- titanic_complete[titanic_complete$Fare<filter.boundary,]
equation_fare=" Fare~ Pclass +Sex+ Parch +SibSp+ +Embarked"
equation_fare<-as.formula(equation_fare)
#applying liner regression model
fare.model<-lm(
formula = equation_fare,
data = filter.data
)
#converting the dataset and considering only the columns based on which the model was made
fare.rows<- titanic_complete[is.na(titanic_complete$Fare),c("Pclass", "Sex", "Parch", "SibSp","Embarked")]
#replacing the NAs with the values that are predicted by the model
fare.predictions<-predict(fare.model, newdata =fare.rows)
titanic_complete[is.na(titanic_complete$Fare),"Fare"]<-fare.predictionsChecking the number of NAs in the dataset.
na_info<-apply(is.na(titanic_complete),2 ,which)
str(na_info)## List of 13
## $ PassengerId: int(0)
## $ Survived : int [1:418] 892 893 894 895 896 897 898 899 900 901 ...
## $ Pclass : int(0)
## $ Name : int(0)
## $ Sex : int(0)
## $ Age : int [1:263] 6 18 20 27 29 30 32 33 37 43 ...
## $ SibSp : int(0)
## $ Parch : int(0)
## $ Ticket : int(0)
## $ Fare : int(0)
## $ Cabin : int(0)
## $ Embarked : int(0)
## $ is_train : int(0)Now, applying the same model over the Age column.
boxplot(titanic_complete$Age)
filter.age<- titanic_complete[(titanic_complete$Age <
boxplot.stats(titanic_complete$Age)$stats[5]),]
equation_age<-" Age~ Pclass+Sex+SibSp+Parch+Fare+Embarked"
equation_age<-as.formula(equation_age)
age.rows<- titanic_complete[is.na(titanic_complete$Age),c("Pclass","Sex","SibSp","Parch","Fare","Embarked")]
age.model<-lm(formula = equation_age,data = filter.age)
age.predictions<- predict(age.model, newdata = age.rows)
titanic_complete[is.na(titanic_complete$Age),"Age"]<-age.predictionsChecking the number of NAs in the dataset.
na_info<-apply(is.na(titanic_complete),2 ,which)
str(na_info)## List of 13
## $ PassengerId: int(0)
## $ Survived : int [1:418] 892 893 894 895 896 897 898 899 900 901 ...
## $ Pclass : int(0)
## $ Name : int(0)
## $ Sex : int(0)
## $ Age : int(0)
## $ SibSp : int(0)
## $ Parch : int(0)
## $ Ticket : int(0)
## $ Fare : int(0)
## $ Cabin : int(0)
## $ Embarked : int(0)
## $ is_train : int(0)Applying randomForrest algorithm on the train dataset to train the model and predicting the value of survived(alive=1,dead=0) in the test dataset.
#converting the data variables as factors
titanic_complete$Pclass<- as.factor(titanic_complete$Pclass)
titanic_complete$sex<- as.factor(titanic_complete$Sex)
titanic_complete$Survived<- as.factor(titanic_complete$Survived)
titanic_complete$Embarked<- as.factor(titanic_complete$Embarked)
#splitting the primary dataset into train and test again by the column is_train
str(titanic_complete)## 'data.frame': 1309 obs. of 14 variables:
## $ PassengerId: int 1 2 3 4 5 6 7 8 9 10 ...
## $ Survived : Factor w/ 2 levels "0","1": 1 2 2 2 1 1 1 1 2 2 ...
## $ Pclass : Factor w/ 3 levels "1","2","3": 3 1 3 1 3 3 1 3 3 2 ...
## $ Name : chr "Braund, Mr. Owen Harris" "Cumings, Mrs. John Bradley (Florence Briggs Thayer)" "Heikkinen, Miss. Laina" "Futrelle, Mrs. Jacques Heath (Lily May Peel)" ...
## $ Sex : chr "male" "female" "female" "female" ...
## $ Age : num 22 38 26 35 35 ...
## $ SibSp : int 1 1 0 1 0 0 0 3 0 1 ...
## $ Parch : int 0 0 0 0 0 0 0 1 2 0 ...
## $ Ticket : chr "A/5 21171" "PC 17599" "STON/O2. 3101282" "113803" ...
## $ Fare : num 7.25 71.28 7.92 53.1 8.05 ...
## $ Cabin : chr "" "C85" "" "C123" ...
## $ Embarked : Factor w/ 3 levels "C","Q","S": 3 1 3 3 3 2 3 3 3 1 ...
## $ is_train : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
## $ sex : Factor w/ 2 levels "female","male": 2 1 1 1 2 2 2 2 1 1 ...train<-titanic_complete[titanic_complete$is_train==T,]
test<-titanic_complete[titanic_complete$is_train==F,]
str(train)## 'data.frame': 891 obs. of 14 variables:
## $ PassengerId: int 1 2 3 4 5 6 7 8 9 10 ...
## $ Survived : Factor w/ 2 levels "0","1": 1 2 2 2 1 1 1 1 2 2 ...
## $ Pclass : Factor w/ 3 levels "1","2","3": 3 1 3 1 3 3 1 3 3 2 ...
## $ Name : chr "Braund, Mr. Owen Harris" "Cumings, Mrs. John Bradley (Florence Briggs Thayer)" "Heikkinen, Miss. Laina" "Futrelle, Mrs. Jacques Heath (Lily May Peel)" ...
## $ Sex : chr "male" "female" "female" "female" ...
## $ Age : num 22 38 26 35 35 ...
## $ SibSp : int 1 1 0 1 0 0 0 3 0 1 ...
## $ Parch : int 0 0 0 0 0 0 0 1 2 0 ...
## $ Ticket : chr "A/5 21171" "PC 17599" "STON/O2. 3101282" "113803" ...
## $ Fare : num 7.25 71.28 7.92 53.1 8.05 ...
## $ Cabin : chr "" "C85" "" "C123" ...
## $ Embarked : Factor w/ 3 levels "C","Q","S": 3 1 3 3 3 2 3 3 3 1 ...
## $ is_train : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
## $ sex : Factor w/ 2 levels "female","male": 2 1 1 1 2 2 2 2 1 1 ...str(titanic_complete)## 'data.frame': 1309 obs. of 14 variables:
## $ PassengerId: int 1 2 3 4 5 6 7 8 9 10 ...
## $ Survived : Factor w/ 2 levels "0","1": 1 2 2 2 1 1 1 1 2 2 ...
## $ Pclass : Factor w/ 3 levels "1","2","3": 3 1 3 1 3 3 1 3 3 2 ...
## $ Name : chr "Braund, Mr. Owen Harris" "Cumings, Mrs. John Bradley (Florence Briggs Thayer)" "Heikkinen, Miss. Laina" "Futrelle, Mrs. Jacques Heath (Lily May Peel)" ...
## $ Sex : chr "male" "female" "female" "female" ...
## $ Age : num 22 38 26 35 35 ...
## $ SibSp : int 1 1 0 1 0 0 0 3 0 1 ...
## $ Parch : int 0 0 0 0 0 0 0 1 2 0 ...
## $ Ticket : chr "A/5 21171" "PC 17599" "STON/O2. 3101282" "113803" ...
## $ Fare : num 7.25 71.28 7.92 53.1 8.05 ...
## $ Cabin : chr "" "C85" "" "C123" ...
## $ Embarked : Factor w/ 3 levels "C","Q","S": 3 1 3 3 3 2 3 3 3 1 ...
## $ is_train : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
## $ sex : Factor w/ 2 levels "female","male": 2 1 1 1 2 2 2 2 1 1 ...train$Survived<- as.factor(train$Survived)
#form a equation that consists of all the necessary variables for the model based randomForest algorithm
equation<-"Survived ~ Pclass +Sex+ Age+SibSp+ Fare+Embarked"
equation<-as.formula(equation)
Survived<-randomForest(formula(equation),data = train, mtry=3, ntree=500,
nodesize=0.01*nrow(test))
predictions<-predict(Survived, newdata = test)
final_prediction<-as.data.frame(test$PassengerId, row.names = F)## Warning in as.data.frame.integer(test$PassengerId, row.names = F): 'row.names'
## is not a character vector of length 418 -- omitting it. Will be an error!names(final_prediction)[1]<-"PassengerId"
final_prediction$Survived<-predictions
final_prediction## PassengerId Survived
## 1 892 0
## 2 893 0
## 3 894 0
## 4 895 0
## 5 896 1
## 6 897 0
## 7 898 0
## 8 899 0
## 9 900 1
## 10 901 0
## 11 902 0
## 12 903 0
## 13 904 1
## 14 905 0
## 15 906 1
## 16 907 1
## 17 908 0
## 18 909 0
## 19 910 0
## 20 911 0
## 21 912 0
## 22 913 1
## 23 914 1
## 24 915 0
## 25 916 1
## 26 917 0
## 27 918 1
## 28 919 0
## 29 920 1
## 30 921 0
## 31 922 0
## 32 923 0
## 33 924 1
## 34 925 0
## 35 926 1
## 36 927 0
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## 46 937 0
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## 49 940 1
## 50 941 1
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## 53 944 1
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## 55 946 0
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