Titanic
Peter Hagen
June 27, 2016
train <- read.csv("~/Desktop/Kaggle/Titanic/train (3).csv")
library(ggplot2)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(purrr)##
## Attaching package: 'purrr'## The following object is masked from 'package:dplyr':
##
## order_bytrain$Survived.lgl<- map_lgl(train$Survived, function(x) {x == 1})
train$Outcome[train$Survived.lgl == TRUE] <- "Survived"
train$Outcome[train$Survived.lgl == FALSE] <- "Died"
train$Embarked2[train$Embarked == "C"] <- "Cherbourg"
train$Embarked2[train$Embarked == "Q"] <- "Queenstown"
train$Embarked2[train$Embarked == "S"] <- "Southhampton"Exploratory Graphs
The following section includes some exploratory graphs to get an idea of the datasets and set some possible hypothesis on factor causality on survival.
outcomes.gender <- train %>% group_by(Sex, Outcome) %>% summarise(Totalnum = n())
ggplot(outcomes.gender, aes(x = Sex, y = Totalnum, fill = Outcome)) +
geom_bar(stat = 'identity') + labs(y = "Total Individuals", x = "Gender", title = "Gender")
outcomes.class <- train %>% group_by(Pclass, Outcome) %>% summarise(Totalnum = n())
ggplot(outcomes.class, aes(x = Pclass, y = Totalnum, fill = Outcome)) +
geom_bar(stat = 'identity') + labs(y = "Total Individuals", x = "Passenger Class", title = "Class")
outcomes.siblings <- train %>% group_by(SibSp, Outcome) %>% summarise(Totalnum = n())
ggplot(outcomes.siblings, aes(x = SibSp, y = Totalnum, fill = Outcome)) +
geom_bar(stat = 'identity') + labs(y = "Total Individuals", x = "Number of Siblings or Spouses Aboard", title = "Sibling & Spouse")
outcomes.parch <- train %>% group_by(Parch, Outcome) %>% summarise(Totalnum = n())
ggplot(outcomes.parch, aes(x = Parch, y = Totalnum, fill = Outcome)) +
geom_bar(stat = 'identity') + labs(y = "Total Individuals", x = "Number of Parents or Children Aboard", title = "Parents & Children")
outcomes.Embarked2 <- train %>% group_by(Embarked2, Outcome) %>% summarise(Totalnum = n())
ggplot(outcomes.Embarked2, aes(x = Embarked2, y = Totalnum, fill = Outcome)) +
geom_bar(stat = 'identity') + labs(y = "Total Individuals", x = "Location Where Boarded Titanic", title = "Board Location")## Warning: Removed 1 rows containing missing values (position_stack).
ggplot(train, aes(x = Outcome, y = Age)) +
geom_violin(aes(fill = Outcome)) +
geom_boxplot(width = 0.1, aes(fill = Outcome)) ## Warning: Removed 177 rows containing non-finite values (stat_ydensity).## Warning: Removed 177 rows containing non-finite values (stat_boxplot).
ggplot(train, aes(x = Outcome, y = Age)) +
geom_violin(aes(fill = Outcome)) +
geom_boxplot(width = 0.1, aes(fill = Outcome)) +
facet_wrap(~ Sex)## Warning: Removed 177 rows containing non-finite values (stat_ydensity).## Warning: Removed 177 rows containing non-finite values (stat_boxplot).
ggplot(train, aes(x = Outcome, y = Fare)) +
geom_violin(aes(fill = Outcome)) +
geom_boxplot(width = 0.1, aes(fill = Outcome))
ggplot(train, aes(x = Outcome, y = Fare)) +
geom_violin(aes(fill = Outcome)) +
geom_boxplot(width = 0.1, aes(fill = Outcome)) +
facet_wrap(~Sex)
Models (rough rough draft)
Classification Tree Model
test <- read.csv("~/Desktop/Kaggle/Titanic/test (3).csv")
library(caret)## Loading required package: lattice##
## Attaching package: 'caret'## The following object is masked from 'package:purrr':
##
## liftlibrary(rpart)
library(rattle)## Rattle: A free graphical interface for data mining with R.
## Version 4.1.0 Copyright (c) 2006-2015 Togaware Pty Ltd.
## Type 'rattle()' to shake, rattle, and roll your data.train.trim <- train[,c("Survived", "Pclass", "Sex", "Age", "SibSp", "Parch", "Fare", "Embarked")]
set.seed(121212)
inTrain = createDataPartition(train.trim$Survived, p = 0.60, list=FALSE)
train2 = train.trim[inTrain,]
train2.test = train.trim[-inTrain,]
set.seed(12321123)
modFit <- rpart(Survived ~., data = train2, method = "class")
fancyRpartPlot(modFit)
set.seed(12321312)
pred.mod <- predict(modFit, train2, type = "class")
confusionMatrix(pred.mod, train2$Survived)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 309 69
## 1 29 128
##
## Accuracy : 0.8168
## 95% CI : (0.7814, 0.8487)
## No Information Rate : 0.6318
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.5889
## Mcnemar's Test P-Value : 8.162e-05
##
## Sensitivity : 0.9142
## Specificity : 0.6497
## Pos Pred Value : 0.8175
## Neg Pred Value : 0.8153
## Prevalence : 0.6318
## Detection Rate : 0.5776
## Detection Prevalence : 0.7065
## Balanced Accuracy : 0.7820
##
## 'Positive' Class : 0
## set.seed(12321312)
pred.mod <- predict(modFit, train2.test, type = "class")
confusionMatrix(pred.mod, train2.test$Survived)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 194 46
## 1 17 99
##
## Accuracy : 0.823
## 95% CI : (0.7793, 0.8613)
## No Information Rate : 0.5927
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.6216
## Mcnemar's Test P-Value : 0.0004192
##
## Sensitivity : 0.9194
## Specificity : 0.6828
## Pos Pred Value : 0.8083
## Neg Pred Value : 0.8534
## Prevalence : 0.5927
## Detection Rate : 0.5449
## Detection Prevalence : 0.6742
## Balanced Accuracy : 0.8011
##
## 'Positive' Class : 0
## test.pred <- predict(modFit, test)
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## 418 0.8096591 0.19034091test2 <- cbind.data.frame(test, test.pred)Random Forest Model
library(randomForest)## randomForest 4.6-12## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:dplyr':
##
## combine## The following object is masked from 'package:ggplot2':
##
## marginlibrary(party)## Loading required package: grid## Loading required package: mvtnorm## Loading required package: modeltools## Loading required package: stats4## Loading required package: strucchange## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric## Loading required package: sandwichtrain22 <- train2[complete.cases(train2),]
str(train22)## 'data.frame': 423 obs. of 8 variables:
## $ Survived: int 1 1 1 0 0 1 0 0 1 1 ...
## $ Pclass : int 1 3 1 3 3 2 3 2 2 3 ...
## $ Sex : Factor w/ 2 levels "female","male": 1 1 1 2 1 1 2 2 2 1 ...
## $ Age : num 38 26 58 20 14 55 2 35 34 15 ...
## $ SibSp : int 1 0 0 0 0 0 4 0 0 0 ...
## $ Parch : int 0 0 0 0 0 0 1 0 0 0 ...
## $ Fare : num 71.28 7.92 26.55 8.05 7.85 ...
## $ Embarked: Factor w/ 4 levels "","C","Q","S": 2 4 4 4 4 4 3 4 4 3 ...train22$Survived <- as.factor(train22$Survived)
train22$Pclass <- as.factor(train22$Pclass)
train22$SibSp <- as.factor(train22$SibSp)
train22$Parch <- as.factor(train22$Parch)
set.seed(123123)
modFit2 <- randomForest(Survived ~ ., data = train22, ntree = 400, importance=TRUE)
set.seed(12321312)
trainingPred <- predict(modFit2, train22)
confusionMatrix(trainingPred, train22$Survived)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 255 30
## 1 4 134
##
## Accuracy : 0.9196
## 95% CI : (0.8895, 0.9437)
## No Information Rate : 0.6123
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8256
## Mcnemar's Test P-Value : 1.807e-05
##
## Sensitivity : 0.9846
## Specificity : 0.8171
## Pos Pred Value : 0.8947
## Neg Pred Value : 0.9710
## Prevalence : 0.6123
## Detection Rate : 0.6028
## Detection Prevalence : 0.6738
## Balanced Accuracy : 0.9008
##
## 'Positive' Class : 0
## varImpPlot(modFit2)
plot(modFit2, log="y")
getTree(modFit2)## left daughter right daughter split var split point status prediction
## 1 2 3 7 2.00000 1 0
## 2 4 5 2 1.00000 1 0
## 3 6 7 6 10.82500 1 0
## 4 8 9 6 36.08750 1 0
## 5 10 11 3 34.00000 1 0
## 6 12 13 3 15.50000 1 0
## 7 14 15 2 1.00000 1 0
## 8 16 17 3 11.50000 1 0
## 9 0 0 0 0.00000 -1 2
## 10 18 19 6 16.62085 1 0
## 11 0 0 0 0.00000 -1 2
## 12 20 21 5 1.00000 1 0
## 13 22 23 3 17.50000 1 0
## 14 24 25 4 19.00000 1 0
## 15 26 27 4 22.00000 1 0
## 16 0 0 0 0.00000 -1 1
## 17 28 29 6 12.84795 1 0
## 18 0 0 0 0.00000 -1 1
## 19 30 31 5 1.00000 1 0
## 20 0 0 0 0.00000 -1 2
## 21 0 0 0 0.00000 -1 1
## 22 0 0 0 0.00000 -1 1
## 23 32 33 2 1.00000 1 0
## 24 34 35 4 3.00000 1 0
## 25 36 37 5 1.00000 1 0
## 26 38 39 1 1.00000 1 0
## 27 40 41 4 1.00000 1 0
## 28 0 0 0 0.00000 -1 2
## 29 42 43 4 1.00000 1 0
## 30 44 45 1 1.00000 1 0
## 31 0 0 0 0.00000 -1 1
## 32 46 47 4 1.00000 1 0
## 33 48 49 6 7.91040 1 0
## 34 50 51 6 22.03750 1 0
## 35 0 0 0 0.00000 -1 2
## 36 0 0 0 0.00000 -1 2
## 37 52 53 6 154.95000 1 0
## 38 54 55 3 60.00000 1 0
## 39 56 57 3 11.50000 1 0
## 40 58 59 6 18.31250 1 0
## 41 0 0 0 0.00000 -1 1
## 42 0 0 0 0.00000 -1 1
## 43 0 0 0 0.00000 -1 2
## 44 0 0 0 0.00000 -1 1
## 45 0 0 0 0.00000 -1 1
## 46 0 0 0 0.00000 -1 1
## 47 0 0 0 0.00000 -1 1
## 48 60 61 1 1.00000 1 0
## 49 62 63 5 1.00000 1 0
## 50 64 65 5 7.00000 1 0
## 51 66 67 4 1.00000 1 0
## 52 0 0 0 0.00000 -1 1
## 53 0 0 0 0.00000 -1 2
## 54 68 69 6 101.73750 1 0
## 55 0 0 0 0.00000 -1 1
## 56 70 71 3 2.50000 1 0
## 57 0 0 0 0.00000 -1 1
## 58 72 73 6 13.25000 1 0
## 59 74 75 1 2.00000 1 0
## 60 0 0 0 0.00000 -1 1
## 61 76 77 4 1.00000 1 0
## 62 78 79 4 1.00000 1 0
## 63 0 0 0 0.00000 -1 1
## 64 80 81 6 12.82500 1 0
## 65 0 0 0 0.00000 -1 1
## 66 0 0 0 0.00000 -1 2
## 67 82 83 5 3.00000 1 0
## 68 0 0 0 0.00000 -1 2
## 69 0 0 0 0.00000 -1 2
## 70 0 0 0 0.00000 -1 1
## 71 0 0 0 0.00000 -1 2
## 72 0 0 0 0.00000 -1 1
## 73 0 0 0 0.00000 -1 1
## 74 0 0 0 0.00000 -1 1
## 75 84 85 5 3.00000 1 0
## 76 0 0 0 0.00000 -1 1
## 77 86 87 3 27.00000 1 0
## 78 0 0 0 0.00000 -1 1
## 79 0 0 0 0.00000 -1 1
## 80 0 0 0 0.00000 -1 2
## 81 88 89 4 1.00000 1 0
## 82 0 0 0 0.00000 -1 2
## 83 90 91 3 3.50000 1 0
## 84 92 93 1 1.00000 1 0
## 85 0 0 0 0.00000 -1 2
## 86 94 95 6 7.81460 1 0
## 87 0 0 0 0.00000 -1 1
## 88 96 97 5 1.00000 1 0
## 89 98 99 6 18.17500 1 0
## 90 0 0 0 0.00000 -1 1
## 91 0 0 0 0.00000 -1 2
## 92 100 101 5 1.00000 1 0
## 93 0 0 0 0.00000 -1 1
## 94 0 0 0 0.00000 -1 2
## 95 0 0 0 0.00000 -1 1
## 96 0 0 0 0.00000 -1 2
## 97 0 0 0 0.00000 -1 2
## 98 0 0 0 0.00000 -1 1
## 99 0 0 0 0.00000 -1 2
## 100 102 103 6 26.46875 1 0
## 101 0 0 0 0.00000 -1 1
## 102 0 0 0 0.00000 -1 2
## 103 0 0 0 0.00000 -1 1