knitr::opts_chunk$set(echo = TRUE)

Our Project is about Determining whether a person is dead or was lucky enough to survive from the titanic trajedgy

train <- read.csv('train.csv')
test  <- read.csv('test.csv')

Looking at our data

str(train)

## 'data.frame':    891 obs. of  12 variables:
##  $ PassengerId: int  1 2 3 4 5 6 7 8 9 10 ...
##  $ Survived   : int  0 1 1 1 0 0 0 0 1 1 ...
##  $ Pclass     : int  3 1 3 1 3 3 1 3 3 2 ...
##  $ Name       : Factor w/ 891 levels "Abbing, Mr. Anthony",..: 109 191 358 277 16 559 520 629 417 581 ...
##  $ Sex        : Factor w/ 2 levels "female","male": 2 1 1 1 2 2 2 2 1 1 ...
##  $ Age        : num  22 38 26 35 35 NA 54 2 27 14 ...
##  $ 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     : Factor w/ 681 levels "110152","110413",..: 524 597 670 50 473 276 86 396 345 133 ...
##  $ Fare       : num  7.25 71.28 7.92 53.1 8.05 ...
##  $ Cabin      : Factor w/ 148 levels "","A10","A14",..: 1 83 1 57 1 1 131 1 1 1 ...
##  $ Embarked   : Factor w/ 4 levels "","C","Q","S": 4 2 4 4 4 3 4 4 4 2 ...

str(test)

## 'data.frame':    418 obs. of  11 variables:
##  $ PassengerId: int  892 893 894 895 896 897 898 899 900 901 ...
##  $ Pclass     : int  3 3 2 3 3 3 3 2 3 3 ...
##  $ Name       : Factor w/ 418 levels "Abbott, Master. Eugene Joseph",..: 210 409 273 414 182 370 85 58 5 104 ...
##  $ Sex        : Factor w/ 2 levels "female","male": 2 1 2 2 1 2 1 2 1 2 ...
##  $ Age        : num  34.5 47 62 27 22 14 30 26 18 21 ...
##  $ SibSp      : int  0 1 0 0 1 0 0 1 0 2 ...
##  $ Parch      : int  0 0 0 0 1 0 0 1 0 0 ...
##  $ Ticket     : Factor w/ 363 levels "110469","110489",..: 153 222 74 148 139 262 159 85 101 270 ...
##  $ Fare       : num  7.83 7 9.69 8.66 12.29 ...
##  $ Cabin      : Factor w/ 77 levels "","A11","A18",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Embarked   : Factor w/ 3 levels "C","Q","S": 2 3 2 3 3 3 2 3 1 3 ...

First thing to notice, Some Features has variables with type numeric or int that can be turned to factor as it's a Discrete Feature. (Survived, Pclass, Sibsb, Parch)

But before that, We combine both train and test in a single DataFrame

Test dataset contains all train dataset Features except the Survived feature since it's the Dependent Variable in Our Challenge (that we should predict its value)

So we add the Survived Column, thus we can combine 2 datasets by row

test.survived <- data.frame(Survived = rep("None" , nrow(test)), test[,])

dim(train)

## [1] 891  12

dim(test.survived)

## [1] 418  12

data.combined <- rbind(train , test.survived)

Making sure that these features can be treated as cateorical data

  table(data.combined$SibSp)

## 
##   0   1   2   3   4   5   8 
## 891 319  42  20  22   6   9

  table(data.combined$Parch)

## 
##    0    1    2    3    4    5    6    9 
## 1002  170  113    8    6    6    2    2

So we can convert data-type of those features to be Categorical

data.combined$Survived <- as.factor(data.combined$Survived)
data.combined$Pclass <- as.factor(data.combined$Pclass)
data.combined$SibSp <- as.factor(data.combined$SibSp)
data.combined$Parch <- as.factor(data.combined$Parch)

Plotting is the Best way to analyse your data

Well the data is Skewed to people didn't make it.

ggplot(data.combined[which(data.combined$Survived != "None"),],aes(x= Survived))+
       geom_bar(fill = "#1ec97f")+
       ggtitle("Survival Ratio")

The Same previous plot, according to classes

ggplot(data.combined[data.combined$Survived != "None",], aes(x = Pclass, fill = Survived))+
  geom_bar( position = "dodge")+
  xlab("Pclass")+
  ylab("Total Count")+
  labs(fill = "Survived")+
  ggtitle("Survival across Classes")

First class has the Highest probability for Survivnig While the third class is absolute opposite Second class fifty-fifty

which means Class is an important Feature, that we should take into consideration while building our model

Moving to Names Feature

head(data.combined$Name)

## [1] Braund, Mr. Owen Harris                            
## [2] Cumings, Mrs. John Bradley (Florence Briggs Thayer)
## [3] Heikkinen, Miss. Laina                             
## [4] Futrelle, Mrs. Jacques Heath (Lily May Peel)       
## [5] Allen, Mr. William Henry                           
## [6] Moran, Mr. James                                   
## 1307 Levels: Abbing, Mr. Anthony ... Zakarian, Mr. Ortin

"Mr", "Mrs", "Miss", "Master" these names may help us in our analysis, or we can create a new Feature depends on these titles

let's see if that hypothese is true

We extract "Misses" only from all the passengers

► str_detect() takes : 1- vector of string (Name column) -- 2- pattern (the "Miss" string) Outputs: Logical vector (Miss or Not)

misses <- data.combined[which(str_detect(data.combined$Name , "Miss.")) , ]
nrow(misses)

## [1] 260

Now plotting misses to see if it's an effective feature or not

ggplot(misses[misses$Survived != "None" ,] ,
       aes(x = Pclass , fill = Survived))+
       geom_bar(position = "dodge")+
       ggtitle("Survival Of Misses across Classes")

Well it seems that in Class 1, class 2 : we can say All misses Survived class 3 : it looks like a coin luck.

So doing same through another title

Mrs <- data.combined[which(str_detect(data.combined$Name , "Mrs.")) ,]
nrow(Mrs)

## [1] 201

ggplot(Mrs[Mrs$Survived != "None" , ] ,aes(x = Pclass , fill = Survived))+
  geom_bar(position = "dodge")+
  ggtitle("Survival of Mrs across Classes")

Looks the same as the Miss title

So it may be a good feature to create

first thing we make a method that returns whatever title it is then add it into a vector that we then insert to our data.combined dataFrame

►grep() method takes : pattern first string value (Name) outputs: 1 (match) integer(0) (didn't match)

we convert the integer(0) -> 0 using length();

assign_titles <- function(name){
  
  name <- as.character(name)
  
  if(length(grep("Miss." , name) > 0))
    return("Miss.")
  if(length(grep("Mrs."  , name) > 0))
    return("Mrs.")
  if(length(grep("Mr." , name) > 0))
    return("Mr.")
  if(length(grep("Master" , name) > 0))
    return("Master.")
  else
    return("Other")
}

Now initalizing the title vector

titles <- NULL

and inserting new titles using our methods

for(i in 1:nrow(data.combined)){
  titles <- c(titles , assign_titles(data.combined[i , "Name"]))
}

finally, adding it to our dataframe

data.combined$titles <- as.factor(titles)

Here is our new Feature, with corresponding names, token randomly

sample_n(data.combined[, c("Name", "titles")], 5)

##                                               Name titles
## 92                      Andreasson, Mr. Paul Edvin    Mr.
## 920                        Brady, Mr. John Bertram    Mr.
## 1082                          Angle, Mr. William A    Mr.
## 855  Carter, Mrs. Ernest Courtenay (Lilian Hughes)   Mrs.
## 139                            Osen, Mr. Olaf Elon    Mr.

See what this feature say

ggplot(data.combined[which(data.combined$Survived != "None"),],aes(x = titles, fill=Survived))+
  geom_bar(position = "dodge")+
  ggtitle("Survival across titles")

which means that Men are the most likely to be dead and Miss, Mrs more likely to live while masters (young men) have equal propability (until now)

Now look at our data with respect to Class, title and Survived Features

ggplot(data.combined[data.combined$Survived != "None", ] , aes(x = titles , fill = Survived))+
  geom_bar()+
  facet_wrap(~Pclass)+
  ggtitle("Survival of Titles w.r.t Class")

we can see that Master: survived with 100% in class 1 and 2
Misses: almost same
Mrs : almost same
Mr : in class 1 - 50/50
in class 2 - more probable to be dead

in class 3 the Savage class.
Mr are the most likely to die
while others have almost 50/50 probability

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Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.