Idris_Yusuf_Titanic_Project
Idris Yusuf
2024-04-13
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Load all required library
library(readr)
#library(caret)
library(C50)## Warning: package 'C50' was built under R version 4.3.3library(cluster)
library(fpc)## Warning: package 'fpc' was built under R version 4.3.3library (cluster)
library (vegan)## Warning: package 'vegan' was built under R version 4.3.3## Loading required package: permute## Warning: package 'permute' was built under R version 4.3.3## Loading required package: lattice## This is vegan 2.6-4library(ggplot2)
library(tidyr)
###install.packages("corrplot")
library(corrplot)## Warning: package 'corrplot' was built under R version 4.3.3## corrplot 0.92 loadedlibrary(GGally)## Warning: package 'GGally' was built under R version 4.3.3## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2library(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(caTools)## Warning: package 'caTools' was built under R version 4.3.3library(rpart)
library(rattle)## Warning: package 'rattle' was built under R version 4.3.3## Loading required package: tibble## Loading required package: bitops## Rattle: A free graphical interface for data science with R.
## Version 5.5.1 Copyright (c) 2006-2021 Togaware Pty Ltd.
## Type 'rattle()' to shake, rattle, and roll your data.library(rpart.plot)## Warning: package 'rpart.plot' was built under R version 4.3.3library(RColorBrewer)#1) Select import dataset as csv from https://goo.gl/At238b
titanic_ds <- read_csv("C:/GGTUAN/DREAMS/Yankee/TSU/MSc_TSU/Spring_2024/CS-583 Data Minning/Project_Data/titanic_ds.txt")## Rows: 1309 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (8): pclass, name, sex, ticket, cabin, embarked, boat, home.dest
## dbl (6): survived, age, sibsp, parch, fare, body
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.View(titanic_ds)
#titanic <- titanic_ds#2) Load into R into a variable called T3
T3 <- titanic_ds
str(T3)## spc_tbl_ [1,309 × 14] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ pclass : chr [1:1309] "1st" "1st" "1st" "1st" ...
## $ survived : num [1:1309] 1 1 0 0 0 1 1 0 1 0 ...
## $ name : chr [1:1309] "Allen, Miss. Elisabeth Walton" "Allison, Master. Hudson Trevor" "Allison, Miss. Helen Loraine" "Allison, Mr. Hudson Joshua Creighton" ...
## $ sex : chr [1:1309] "female" "male" "female" "male" ...
## $ age : num [1:1309] 29 0.92 2 30 25 48 63 39 53 71 ...
## $ sibsp : num [1:1309] 0 1 1 1 1 0 1 0 2 0 ...
## $ parch : num [1:1309] 0 2 2 2 2 0 0 0 0 0 ...
## $ ticket : chr [1:1309] "24160" "113781" "113781" "113781" ...
## $ fare : num [1:1309] 211 152 152 152 152 ...
## $ cabin : chr [1:1309] "B5" "C22 C26" "C22 C26" "C22 C26" ...
## $ embarked : chr [1:1309] "S" "S" "S" "S" ...
## $ boat : chr [1:1309] "2" "11" NA NA ...
## $ body : num [1:1309] NA NA NA 135 NA NA NA NA NA 22 ...
## $ home.dest: chr [1:1309] "St Louis, MO" "Montreal, PQ / Chesterville, ON" "Montreal, PQ / Chesterville, ON" "Montreal, PQ / Chesterville, ON" ...
## - attr(*, "spec")=
## .. cols(
## .. pclass = col_character(),
## .. survived = col_double(),
## .. name = col_character(),
## .. sex = col_character(),
## .. age = col_double(),
## .. sibsp = col_double(),
## .. parch = col_double(),
## .. ticket = col_character(),
## .. fare = col_double(),
## .. cabin = col_character(),
## .. embarked = col_character(),
## .. boat = col_character(),
## .. body = col_double(),
## .. home.dest = col_character()
## .. )
## - attr(*, "problems")=<externalptr>T3## # A tibble: 1,309 × 14
## pclass survived name sex age sibsp parch ticket fare cabin embarked
## <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <chr> <chr>
## 1 1st 1 Allen, M… fema… 29 0 0 24160 211. B5 S
## 2 1st 1 Allison,… male 0.92 1 2 113781 152. C22 … S
## 3 1st 0 Allison,… fema… 2 1 2 113781 152. C22 … S
## 4 1st 0 Allison,… male 30 1 2 113781 152. C22 … S
## 5 1st 0 Allison,… fema… 25 1 2 113781 152. C22 … S
## 6 1st 1 Anderson… male 48 0 0 19952 26.6 E12 S
## 7 1st 1 Andrews,… fema… 63 1 0 13502 78.0 D7 S
## 8 1st 0 Andrews,… male 39 0 0 112050 0 A36 S
## 9 1st 1 Appleton… fema… 53 2 0 11769 51.5 C101 S
## 10 1st 0 Artagave… male 71 0 0 PC 17… 49.5 <NA> C
## # ℹ 1,299 more rows
## # ℹ 3 more variables: boat <chr>, body <dbl>, home.dest <chr>#T4 <- list (T3$survived, T3$embarked, T3$age, T3$sex, T3$sibsp, T3$parch, T3$fare)
#str(T4)
#head(T4)
T5 <- data.frame(T3)
#T5#3) Build a new dataset, titanic, by selecting these features: survived, embarked, age, sex, sibsp, parch and fare
#titanic <- list(T5$survived , T5$embarked, T5$age, T5$sex, T5$sibsp, T5$parch, T5$fare)
titanic <- data.frame(T5$survived , T5$embarked, T5$age, T5$sex, T5$sibsp, T5$parch, T5$fare)
titanic## T5.survived T5.embarked T5.age T5.sex T5.sibsp T5.parch T5.fare
## 1 1 S 29.00 female 0 0 211.3375
## 2 1 S 0.92 male 1 2 151.5500
## 3 0 S 2.00 female 1 2 151.5500
## 4 0 S 30.00 male 1 2 151.5500
## 5 0 S 25.00 female 1 2 151.5500
## 6 1 S 48.00 male 0 0 26.5500
## 7 1 S 63.00 female 1 0 77.9583
## 8 0 S 39.00 male 0 0 0.0000
## 9 1 S 53.00 female 2 0 51.4792
## 10 0 C 71.00 male 0 0 49.5042
## 11 0 C 47.00 male 1 0 227.5250
## 12 1 C 18.00 female 1 0 227.5250
## 13 1 C 24.00 female 0 0 69.3000
## 14 1 S 26.00 female 0 0 78.8500
## 15 1 S 80.00 male 0 0 30.0000
## 16 0 S NA male 0 0 25.9250
## 17 0 C 24.00 male 0 1 247.5208
## 18 1 C 50.00 female 0 1 247.5208
## 19 1 C 32.00 female 0 0 76.2917
## 20 0 C 36.00 male 0 0 75.2417
## 21 1 S 37.00 male 1 1 52.5542
## 22 1 S 47.00 female 1 1 52.5542
## 23 1 C 26.00 male 0 0 30.0000
## 24 1 C 42.00 female 0 0 227.5250
## 25 1 S 29.00 female 0 0 221.7792
## 26 0 C 25.00 male 0 0 26.0000
## 27 1 C 25.00 male 1 0 91.0792
## 28 1 C 19.00 female 1 0 91.0792
## 29 1 S 35.00 female 0 0 135.6333
## 30 1 S 28.00 male 0 0 26.5500
## 31 0 S 45.00 male 0 0 35.5000
## 32 1 C 40.00 male 0 0 31.0000
## 33 1 S 30.00 female 0 0 164.8667
## 34 1 S 58.00 female 0 0 26.5500
## 35 0 S 42.00 male 0 0 26.5500
## 36 1 C 45.00 female 0 0 262.3750
## 37 1 S 22.00 female 0 1 55.0000
## 38 1 S NA male 0 0 26.5500
## 39 0 S 41.00 male 0 0 30.5000
## 40 0 C 48.00 male 0 0 50.4958
## 41 0 C NA male 0 0 39.6000
## 42 1 C 44.00 female 0 0 27.7208
## 43 1 S 59.00 female 2 0 51.4792
## 44 1 C 60.00 female 0 0 76.2917
## 45 1 C 41.00 female 0 0 134.5000
## 46 0 S 45.00 male 0 0 26.5500
## 47 0 S NA male 0 0 31.0000
## 48 1 S 42.00 male 0 0 26.2875
## 49 1 C 53.00 female 0 0 27.4458
## 50 1 C 36.00 male 0 1 512.3292
## 51 1 C 58.00 female 0 1 512.3292
## 52 0 S 33.00 male 0 0 5.0000
## 53 0 S 28.00 male 0 0 47.1000
## 54 0 S 17.00 male 0 0 47.1000
## 55 1 S 11.00 male 1 2 120.0000
## 56 1 S 14.00 female 1 2 120.0000
## 57 1 S 36.00 male 1 2 120.0000
## 58 1 S 36.00 female 1 2 120.0000
## 59 0 S 49.00 male 0 0 26.0000
## 60 1 C NA female 0 0 27.7208
## 61 0 S 36.00 male 1 0 78.8500
## 62 1 S 76.00 female 1 0 78.8500
## 63 0 S 46.00 male 1 0 61.1750
## 64 1 S 47.00 female 1 0 61.1750
## 65 1 S 27.00 male 1 0 53.1000
## 66 1 S 33.00 female 1 0 53.1000
## 67 1 C 36.00 female 0 0 262.3750
## 68 1 S 30.00 female 0 0 86.5000
## 69 1 C 45.00 male 0 0 29.7000
## 70 1 S NA female 0 1 55.0000
## 71 0 S NA male 0 0 0.0000
## 72 0 C 27.00 male 1 0 136.7792
## 73 1 C 26.00 female 1 0 136.7792
## 74 1 S 22.00 female 0 0 151.5500
## 75 0 S NA male 0 0 52.0000
## 76 0 S 47.00 male 0 0 25.5875
## 77 1 C 39.00 female 1 1 83.1583
## 78 0 C 37.00 male 1 1 83.1583
## 79 1 C 64.00 female 0 2 83.1583
## 80 1 S 55.00 female 2 0 25.7000
## 81 0 S NA male 0 0 26.5500
## 82 0 S 70.00 male 1 1 71.0000
## 83 1 S 36.00 female 0 2 71.0000
## 84 1 S 64.00 female 1 1 26.5500
## 85 0 C 39.00 male 1 0 71.2833
## 86 1 C 38.00 female 1 0 71.2833
## 87 1 S 51.00 male 0 0 26.5500
## 88 1 S 27.00 male 0 0 30.5000
## 89 1 S 33.00 female 0 0 151.5500
## 90 0 S 31.00 male 1 0 52.0000
## 91 1 S 27.00 female 1 2 52.0000
## 92 1 S 31.00 male 1 0 57.0000
## 93 1 S 17.00 female 1 0 57.0000
## 94 1 S 53.00 male 1 1 81.8583
## 95 1 S 4.00 male 0 2 81.8583
## 96 1 S 54.00 female 1 1 81.8583
## 97 0 C 50.00 male 1 0 106.4250
## 98 1 C 27.00 female 1 1 247.5208
## 99 1 C 48.00 female 1 0 106.4250
## 100 1 C 48.00 female 1 0 39.6000
## 101 1 C 49.00 male 1 0 56.9292
## 102 0 C 39.00 male 0 0 29.7000
## 103 1 C 23.00 female 0 1 83.1583
## 104 1 C 38.00 female 0 0 227.5250
## 105 1 C 54.00 female 1 0 78.2667
## 106 0 C 36.00 female 0 0 31.6792
## 107 0 S NA male 0 0 221.7792
## 108 1 S NA female 0 0 31.6833
## 109 1 C NA female 0 0 110.8833
## 110 1 S 36.00 male 0 0 26.3875
## 111 0 C 30.00 male 0 0 27.7500
## 112 1 S 24.00 female 3 2 263.0000
## 113 1 S 28.00 female 3 2 263.0000
## 114 1 S 23.00 female 3 2 263.0000
## 115 0 S 19.00 male 3 2 263.0000
## 116 0 S 64.00 male 1 4 263.0000
## 117 1 S 60.00 female 1 4 263.0000
## 118 1 C 30.00 female 0 0 56.9292
## 119 0 S NA male 0 0 26.5500
## 120 1 S 50.00 male 2 0 133.6500
## 121 1 C 43.00 male 1 0 27.7208
## 122 1 S NA female 1 0 133.6500
## 123 1 C 22.00 female 0 2 49.5000
## 124 1 C 60.00 male 1 1 79.2000
## 125 1 C 48.00 female 1 1 79.2000
## 126 0 S NA male 0 0 0.0000
## 127 0 S 37.00 male 1 0 53.1000
## 128 1 S 35.00 female 1 0 53.1000
## 129 0 S 47.00 male 0 0 38.5000
## 130 1 C 35.00 female 0 0 211.5000
## 131 1 C 22.00 female 0 1 59.4000
## 132 1 C 45.00 female 0 1 59.4000
## 133 0 C 24.00 male 0 0 79.2000
## 134 1 C 49.00 male 1 0 89.1042
## 135 1 C NA female 1 0 89.1042
## 136 0 C 71.00 male 0 0 34.6542
## 137 1 C 53.00 male 0 0 28.5000
## 138 1 S 19.00 female 0 0 30.0000
## 139 0 S 38.00 male 0 1 153.4625
## 140 1 S 58.00 female 0 1 153.4625
## 141 1 C 23.00 male 0 1 63.3583
## 142 1 C 45.00 female 0 1 63.3583
## 143 0 C 46.00 male 0 0 79.2000
## 144 1 C 25.00 male 1 0 55.4417
## 145 1 C 25.00 female 1 0 55.4417
## 146 1 C 48.00 male 1 0 76.7292
## 147 1 C 49.00 female 1 0 76.7292
## 148 0 S NA male 0 0 42.4000
## 149 0 S 45.00 male 1 0 83.4750
## 150 1 S 35.00 female 1 0 83.4750
## 151 0 S 40.00 male 0 0 0.0000
## 152 1 C 27.00 male 0 0 76.7292
## 153 1 S NA male 0 0 30.0000
## 154 1 C 24.00 female 0 0 83.1583
## 155 0 S 55.00 male 1 1 93.5000
## 156 1 S 52.00 female 1 1 93.5000
## 157 0 S 42.00 male 0 0 42.5000
## 158 0 S NA male 0 0 51.8625
## 159 0 S 55.00 male 0 0 50.0000
## 160 1 C 16.00 female 0 1 57.9792
## 161 1 C 44.00 female 0 1 57.9792
## 162 1 S 51.00 female 1 0 77.9583
## 163 0 S 42.00 male 1 0 52.0000
## 164 1 S 35.00 female 1 0 52.0000
## 165 1 C 35.00 male 0 0 26.5500
## 166 1 S 38.00 male 1 0 90.0000
## 167 0 C NA male 0 0 30.6958
## 168 1 S 35.00 female 1 0 90.0000
## 169 1 <NA> 38.00 female 0 0 80.0000
## 170 0 C 50.00 female 0 0 28.7125
## 171 1 S 49.00 male 0 0 0.0000
## 172 0 S 46.00 male 0 0 26.0000
## 173 0 S 50.00 male 0 0 26.0000
## 174 0 C 32.50 male 0 0 211.5000
## 175 0 C 58.00 male 0 0 29.7000
## 176 0 S 41.00 male 1 0 51.8625
## 177 1 S NA female 1 0 51.8625
## 178 1 S 42.00 male 1 0 52.5542
## 179 1 S 45.00 female 1 0 52.5542
## 180 0 S NA male 0 0 26.5500
## 181 1 S 39.00 female 0 0 211.3375
## 182 1 S 49.00 female 0 0 25.9292
## 183 1 C 30.00 female 0 0 106.4250
## 184 1 C 35.00 male 0 0 512.3292
## 185 0 C NA male 0 0 27.7208
## 186 0 S 42.00 male 0 0 26.5500
## 187 1 C 55.00 female 0 0 27.7208
## 188 1 S 16.00 female 0 1 39.4000
## 189 1 S 51.00 female 0 1 39.4000
## 190 0 S 29.00 male 0 0 30.0000
## 191 1 S 21.00 female 0 0 77.9583
## 192 0 S 30.00 male 0 0 45.5000
## 193 1 C 58.00 female 0 0 146.5208
## 194 1 S 15.00 female 0 1 211.3375
## 195 0 S 30.00 male 0 0 26.0000
## 196 1 S 16.00 female 0 0 86.5000
## 197 1 C NA male 0 0 29.7000
## 198 0 S 19.00 male 1 0 53.1000
## 199 1 S 18.00 female 1 0 53.1000
## 200 1 C 24.00 female 0 0 49.5042
## 201 0 C 46.00 male 0 0 75.2417
## 202 0 S 54.00 male 0 0 51.8625
## 203 1 S 36.00 male 0 0 26.2875
## 204 0 C 28.00 male 1 0 82.1708
## 205 1 C NA female 1 0 82.1708
## 206 0 S 65.00 male 0 0 26.5500
## 207 0 Q 44.00 male 2 0 90.0000
## 208 1 Q 33.00 female 1 0 90.0000
## 209 1 Q 37.00 female 1 0 90.0000
## 210 1 C 30.00 male 1 0 57.7500
## 211 0 S 55.00 male 0 0 30.5000
## 212 0 S 47.00 male 0 0 42.4000
## 213 0 C 37.00 male 0 1 29.7000
## 214 1 C 31.00 female 1 0 113.2750
## 215 1 C 23.00 female 1 0 113.2750
## 216 0 C 58.00 male 0 2 113.2750
## 217 1 S 19.00 female 0 2 26.2833
## 218 0 S 64.00 male 0 0 26.0000
## 219 1 C 39.00 female 0 0 108.9000
## 220 1 C NA male 0 0 25.7417
## 221 1 C 22.00 female 0 1 61.9792
## 222 0 C 65.00 male 0 1 61.9792
## 223 0 C 28.50 male 0 0 27.7208
## 224 0 S NA male 0 0 0.0000
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## 228 1 S 22.00 female 1 0 66.6000
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## 232 1 S 52.00 male 0 0 30.5000
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## 235 0 S 38.00 male 0 0 0.0000
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## 239 1 S 43.00 female 0 1 211.3375
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## 908 0 S 20.00 female 1 0 9.8250
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## 910 1 S 32.00 male 0 0 7.9250
## 911 0 S 17.00 male 0 0 7.1250
## 912 0 S 21.00 male 0 0 8.4333
## 913 0 S 30.00 male 0 0 7.8958
## 914 1 S 21.00 male 0 0 7.7958
## 915 0 S 33.00 male 0 0 7.8542
## 916 0 S 22.00 male 0 0 7.5208
## 917 1 C 4.00 female 0 1 13.4167
## 918 1 C 39.00 male 0 1 13.4167
## 919 0 C NA male 0 0 7.2292
## 920 0 C 18.50 male 0 0 7.2292
## 921 0 Q NA male 0 0 7.7500
## 922 0 S NA male 0 0 7.2500
## 923 1 Q NA female 0 0 7.7500
## 924 1 Q NA female 0 0 7.7500
## 925 0 Q 34.50 male 0 0 7.8292
## 926 0 S 44.00 male 0 0 8.0500
## 927 1 Q NA male 0 0 7.7500
## 928 0 C NA male 1 0 14.4542
## 929 0 C NA female 1 0 14.4542
## 930 0 Q NA male 1 0 7.7500
## 931 0 Q NA male 1 0 7.7500
## 932 0 Q NA male 0 0 7.7375
## 933 0 S 22.00 female 2 0 8.6625
## 934 0 S 26.00 male 2 0 8.6625
## 935 1 S 4.00 female 0 2 22.0250
## 936 1 S 29.00 male 3 1 22.0250
## 937 1 S 26.00 female 1 1 22.0250
## 938 0 S 1.00 female 1 1 12.1833
## 939 0 S 18.00 male 1 1 7.8542
## 940 0 S 36.00 female 0 2 12.1833
## 941 0 C NA male 0 0 7.8958
## 942 1 C 25.00 male 0 0 7.2292
## 943 0 C NA male 0 0 7.2250
## 944 0 S 37.00 female 0 0 9.5875
## 945 0 S NA male 0 0 7.8958
## 946 1 S NA male 0 0 56.4958
## 947 0 S NA male 0 0 56.4958
## 948 1 S 22.00 female 0 0 7.2500
## 949 0 Q NA male 0 0 7.7500
## 950 1 S 26.00 male 0 0 56.4958
## 951 0 S 29.00 male 0 0 9.4833
## 952 0 S 29.00 male 0 0 7.7750
## 953 0 S 22.00 male 0 0 7.7750
## 954 1 C 22.00 male 0 0 7.2250
## 955 0 S NA male 3 1 25.4667
## 956 0 S NA female 3 1 25.4667
## 957 0 S NA female 3 1 25.4667
## 958 0 S NA female 3 1 25.4667
## 959 0 S NA female 0 4 25.4667
## 960 0 S 32.00 male 0 0 7.9250
## 961 0 C 34.50 male 0 0 6.4375
## 962 0 Q NA female 1 0 15.5000
## 963 0 Q NA male 1 0 15.5000
## 964 0 S 36.00 male 0 0 0.0000
## 965 0 S 39.00 male 0 0 24.1500
## 966 0 S 24.00 male 0 0 9.5000
## 967 0 S 25.00 female 0 0 7.7750
## 968 0 S 45.00 female 0 0 7.7500
## 969 0 S 36.00 male 1 0 15.5500
## 970 0 S 30.00 female 1 0 15.5500
## 971 1 S 20.00 male 1 0 7.9250
## 972 0 Q NA male 0 0 7.8792
## 973 0 S 28.00 male 0 0 56.4958
## 974 0 S NA male 0 0 7.5500
## 975 0 S 30.00 male 1 0 16.1000
## 976 0 S 26.00 female 1 0 16.1000
## 977 0 S NA male 0 0 7.8792
## 978 0 S 20.50 male 0 0 7.2500
## 979 1 S 27.00 male 0 0 8.6625
## 980 0 S 51.00 male 0 0 7.0542
## 981 1 S 23.00 female 0 0 7.8542
## 982 1 S 32.00 male 0 0 7.5792
## 983 0 S NA male 0 0 7.8958
## 984 0 S NA male 0 0 7.5500
## 985 1 Q NA female 0 0 7.7500
## 986 1 S 24.00 male 0 0 7.1417
## 987 0 S 22.00 male 0 0 7.1250
## 988 0 Q NA female 0 0 7.8792
## 989 0 Q NA male 0 0 7.7500
## 990 0 S NA male 0 0 8.0500
## 991 0 S 29.00 male 0 0 7.9250
## 992 1 C NA male 0 0 7.2292
## 993 0 Q 30.50 female 0 0 7.7500
## 994 1 Q NA female 0 0 7.7375
## 995 0 C NA male 0 0 7.2292
## 996 0 C 35.00 male 0 0 7.8958
## 997 0 S 33.00 male 0 0 7.8958
## 998 1 C NA female 0 0 7.2250
## 999 0 C NA male 0 0 7.8958
## 1000 1 Q NA female 0 0 7.7500
## 1001 1 Q NA male 0 0 7.7500
## 1002 1 Q NA female 2 0 23.2500
## 1003 1 Q NA female 2 0 23.2500
## 1004 1 Q NA male 2 0 23.2500
## 1005 1 Q NA female 0 0 7.7875
## 1006 0 Q NA male 0 0 15.5000
## 1007 1 Q NA female 0 0 7.8792
## 1008 1 Q 15.00 female 0 0 8.0292
## 1009 0 Q 35.00 female 0 0 7.7500
## 1010 0 Q NA male 0 0 7.7500
## 1011 0 S 24.00 male 1 0 16.1000
## 1012 0 S 19.00 female 1 0 16.1000
## 1013 0 Q NA female 0 0 7.7500
## 1014 0 S NA female 0 0 8.0500
## 1015 0 S NA female 0 0 8.0500
## 1016 0 S 55.50 male 0 0 8.0500
## 1017 0 Q NA male 0 0 7.7500
## 1018 1 S 21.00 male 0 0 7.7750
## 1019 0 S NA male 0 0 8.0500
## 1020 0 S 24.00 male 0 0 7.8958
## 1021 0 S 21.00 male 0 0 7.8958
## 1022 0 S 28.00 male 0 0 7.8958
## 1023 0 S NA male 0 0 7.8958
## 1024 1 Q NA female 0 0 7.8792
## 1025 0 S 25.00 male 0 0 7.6500
## 1026 1 S 6.00 male 0 1 12.4750
## 1027 1 S 27.00 female 0 1 12.4750
## 1028 0 S NA male 0 0 8.0500
## 1029 1 Q NA female 1 0 24.1500
## 1030 0 Q NA male 1 0 24.1500
## 1031 0 Q NA male 0 0 8.4583
## 1032 0 S 34.00 male 0 0 8.0500
## 1033 0 Q NA male 0 0 7.7500
## 1034 1 S NA male 0 0 7.7750
## 1035 1 C NA male 1 1 15.2458
## 1036 1 C NA male 1 1 15.2458
## 1037 1 C NA female 0 2 15.2458
## 1038 1 C NA female 0 0 7.2292
## 1039 0 S NA male 0 0 8.0500
## 1040 1 Q NA female 0 0 7.7333
## 1041 1 Q 24.00 female 0 0 7.7500
## 1042 0 S NA male 0 0 8.0500
## 1043 1 Q NA female 1 0 15.5000
## 1044 1 Q NA female 1 0 15.5000
## 1045 1 Q NA female 0 0 15.5000
## 1046 0 S 18.00 male 0 0 7.7500
## 1047 0 S 22.00 male 0 0 7.8958
## 1048 1 C 15.00 female 0 0 7.2250
## 1049 1 C 1.00 female 0 2 15.7417
## 1050 1 C 20.00 male 1 1 15.7417
## 1051 1 C 19.00 female 1 1 15.7417
## 1052 0 S 33.00 male 0 0 8.0500
## 1053 0 S NA male 0 0 7.8958
## 1054 0 C NA male 0 0 7.2292
## 1055 0 Q NA female 0 0 7.7500
## 1056 0 S NA male 0 0 7.8958
## 1057 1 C 12.00 male 1 0 11.2417
## 1058 1 C 14.00 female 1 0 11.2417
## 1059 0 S 29.00 female 0 0 7.9250
## 1060 0 S 28.00 male 0 0 8.0500
## 1061 1 S 18.00 female 0 0 7.7750
## 1062 1 S 26.00 female 0 0 7.8542
## 1063 0 S 21.00 male 0 0 7.8542
## 1064 0 S 41.00 male 0 0 7.1250
## 1065 1 S 39.00 male 0 0 7.9250
## 1066 0 S 21.00 male 0 0 7.8000
## 1067 0 C 28.50 male 0 0 7.2292
## 1068 1 S 22.00 female 0 0 7.7500
## 1069 0 S 61.00 male 0 0 6.2375
## 1070 0 Q NA male 1 0 15.5000
## 1071 0 Q NA male 0 0 7.8292
## 1072 1 Q NA female 1 0 15.5000
## 1073 0 Q NA male 0 0 7.7333
## 1074 0 Q NA male 0 0 7.7500
## 1075 0 Q NA male 0 0 7.7500
## 1076 0 S 23.00 male 0 0 9.2250
## 1077 0 Q NA female 0 0 7.7500
## 1078 1 Q NA female 0 0 7.7500
## 1079 1 Q NA female 0 0 7.8792
## 1080 1 S 22.00 female 0 0 7.7750
## 1081 1 Q NA male 0 0 7.7500
## 1082 1 Q NA female 0 0 7.8292
## 1083 1 S 9.00 male 0 1 3.1708
## 1084 0 S 28.00 male 0 0 22.5250
## 1085 0 S 42.00 male 0 1 8.4042
## 1086 0 S NA male 0 0 7.3125
## 1087 0 S 31.00 female 0 0 7.8542
## 1088 0 S 28.00 male 0 0 7.8542
## 1089 1 S 32.00 male 0 0 7.7750
## 1090 0 S 20.00 male 0 0 9.2250
## 1091 0 S 23.00 female 0 0 8.6625
## 1092 0 S 20.00 female 0 0 8.6625
## 1093 0 S 20.00 male 0 0 8.6625
## 1094 0 S 16.00 male 0 0 9.2167
## 1095 1 S 31.00 female 0 0 8.6833
## 1096 0 Q NA female 0 0 7.6292
## 1097 0 S 2.00 male 3 1 21.0750
## 1098 0 S 6.00 male 3 1 21.0750
## 1099 0 S 3.00 female 3 1 21.0750
## 1100 0 S 8.00 female 3 1 21.0750
## 1101 0 S 29.00 female 0 4 21.0750
## 1102 0 S 1.00 male 4 1 39.6875
## 1103 0 S 7.00 male 4 1 39.6875
## 1104 0 S 2.00 male 4 1 39.6875
## 1105 0 S 16.00 male 4 1 39.6875
## 1106 0 S 14.00 male 4 1 39.6875
## 1107 0 S 41.00 female 0 5 39.6875
## 1108 0 S 21.00 male 0 0 8.6625
## 1109 0 S 19.00 male 0 0 14.5000
## 1110 0 C NA male 0 0 8.7125
## 1111 0 S 32.00 male 0 0 7.8958
## 1112 0 S 0.75 male 1 1 13.7750
## 1113 0 S 3.00 female 1 1 13.7750
## 1114 0 S 26.00 female 0 2 13.7750
## 1115 0 S NA male 0 0 7.0000
## 1116 0 S NA male 0 0 7.7750
## 1117 0 S NA male 0 0 8.0500
## 1118 0 S 21.00 male 0 0 7.9250
## 1119 0 S 25.00 male 0 0 7.9250
## 1120 0 S 22.00 male 0 0 7.2500
## 1121 1 S 25.00 male 1 0 7.7750
## 1122 1 C NA male 1 1 22.3583
## 1123 1 C NA female 1 1 22.3583
## 1124 1 C NA female 0 2 22.3583
## 1125 0 Q NA female 0 0 8.1375
## 1126 0 S 24.00 male 0 0 8.0500
## 1127 0 S 28.00 female 0 0 7.8958
## 1128 0 S 19.00 male 0 0 7.8958
## 1129 0 S NA male 0 0 7.8958
## 1130 0 S 25.00 male 1 0 7.7750
## 1131 0 S 18.00 female 0 0 7.7750
## 1132 1 S 32.00 male 0 0 8.0500
## 1133 0 S NA male 0 0 7.8958
## 1134 0 S 17.00 male 0 0 8.6625
## 1135 0 S 24.00 male 0 0 8.6625
## 1136 0 S NA male 0 0 7.8958
## 1137 0 S NA female 0 0 8.1125
## 1138 0 C NA male 0 0 7.2292
## 1139 0 S NA male 0 0 7.2500
## 1140 0 S 38.00 male 0 0 7.8958
## 1141 0 S 21.00 male 0 0 8.0500
## 1142 0 Q 10.00 male 4 1 29.1250
## 1143 0 Q 4.00 male 4 1 29.1250
## 1144 0 Q 7.00 male 4 1 29.1250
## 1145 0 Q 2.00 male 4 1 29.1250
## 1146 0 Q 8.00 male 4 1 29.1250
## 1147 0 Q 39.00 female 0 5 29.1250
## 1148 0 S 22.00 female 0 0 39.6875
## 1149 0 S 35.00 male 0 0 7.1250
## 1150 1 Q NA female 0 0 7.7208
## 1151 0 S NA male 0 0 14.5000
## 1152 0 S NA female 0 0 14.5000
## 1153 0 S 50.00 male 1 0 14.5000
## 1154 0 S 47.00 female 1 0 14.5000
## 1155 0 S NA male 0 0 8.0500
## 1156 0 S NA male 0 0 7.7750
## 1157 0 S 2.00 female 1 1 20.2125
## 1158 0 S 18.00 male 1 1 20.2125
## 1159 0 S 41.00 female 0 2 20.2125
## 1160 1 S NA female 0 0 8.0500
## 1161 0 S 50.00 male 0 0 8.0500
## 1162 0 S 16.00 male 0 0 8.0500
## 1163 1 Q NA male 0 0 7.7500
## 1164 0 Q NA male 0 0 24.1500
## 1165 0 C NA male 0 0 7.2292
## 1166 0 C 25.00 male 0 0 7.2250
## 1167 0 C NA male 0 0 7.2250
## 1168 0 Q NA male 0 0 7.7292
## 1169 0 S NA male 0 0 7.5750
## 1170 0 S 38.50 male 0 0 7.2500
## 1171 0 S NA male 8 2 69.5500
## 1172 0 S 14.50 male 8 2 69.5500
## 1173 0 S NA female 8 2 69.5500
## 1174 0 S NA female 8 2 69.5500
## 1175 0 S NA female 8 2 69.5500
## 1176 0 S NA female 8 2 69.5500
## 1177 0 S NA male 8 2 69.5500
## 1178 0 S NA male 8 2 69.5500
## 1179 0 S NA male 8 2 69.5500
## 1180 0 S NA male 1 9 69.5500
## 1181 0 S NA female 1 9 69.5500
## 1182 0 S 24.00 male 0 0 9.3250
## 1183 1 S 21.00 female 0 0 7.6500
## 1184 0 S 39.00 male 0 0 7.9250
## 1185 0 C NA male 2 0 21.6792
## 1186 0 C NA male 2 0 21.6792
## 1187 0 C NA male 2 0 21.6792
## 1188 1 S 1.00 female 1 1 16.7000
## 1189 1 S 24.00 female 0 2 16.7000
## 1190 1 S 4.00 female 1 1 16.7000
## 1191 1 S 25.00 male 0 0 9.5000
## 1192 0 S 20.00 male 0 0 8.0500
## 1193 0 S 24.50 male 0 0 8.0500
## 1194 0 Q NA male 0 0 7.7250
## 1195 0 S NA male 0 0 7.8958
## 1196 0 Q NA male 0 0 7.7500
## 1197 1 S 29.00 male 0 0 9.5000
## 1198 0 S NA male 0 0 15.1000
## 1199 1 Q NA female 0 0 7.7792
## 1200 0 S NA male 0 0 8.0500
## 1201 0 S NA male 0 0 8.0500
## 1202 0 C 22.00 male 0 0 7.2292
## 1203 0 S NA male 0 0 8.0500
## 1204 0 S 40.00 male 0 0 7.8958
## 1205 0 S 21.00 male 0 0 7.9250
## 1206 1 S 18.00 female 0 0 7.4958
## 1207 0 S 4.00 male 3 2 27.9000
## 1208 0 S 10.00 male 3 2 27.9000
## 1209 0 S 9.00 female 3 2 27.9000
## 1210 0 S 2.00 female 3 2 27.9000
## 1211 0 S 40.00 male 1 4 27.9000
## 1212 0 S 45.00 female 1 4 27.9000
## 1213 0 S NA male 0 0 7.8958
## 1214 0 S NA male 0 0 8.0500
## 1215 0 S NA male 0 0 8.6625
## 1216 0 Q NA male 0 0 7.7500
## 1217 1 Q NA female 0 0 7.7333
## 1218 0 S 19.00 male 0 0 7.6500
## 1219 0 S 30.00 male 0 0 8.0500
## 1220 0 S NA male 0 0 8.0500
## 1221 0 S 32.00 male 0 0 8.0500
## 1222 0 S NA male 0 0 7.8958
## 1223 0 C 33.00 male 0 0 8.6625
## 1224 1 S 23.00 female 0 0 7.5500
## 1225 0 S 21.00 male 0 0 8.0500
## 1226 0 S 60.50 male 0 0 NA
## 1227 0 S 19.00 male 0 0 7.8958
## 1228 0 S 22.00 female 0 0 9.8375
## 1229 1 S 31.00 male 0 0 7.9250
## 1230 0 S 27.00 male 0 0 8.6625
## 1231 0 S 2.00 female 0 1 10.4625
## 1232 0 S 29.00 female 1 1 10.4625
## 1233 1 S 16.00 male 0 0 8.0500
## 1234 1 S 44.00 male 0 0 7.9250
## 1235 0 S 25.00 male 0 0 7.0500
## 1236 0 S 74.00 male 0 0 7.7750
## 1237 1 S 14.00 male 0 0 9.2250
## 1238 0 S 24.00 male 0 0 7.7958
## 1239 1 S 25.00 male 0 0 7.7958
## 1240 0 S 34.00 male 0 0 8.0500
## 1241 1 C 0.42 male 0 1 8.5167
## 1242 0 C NA male 1 0 6.4375
## 1243 0 C NA male 0 0 6.4375
## 1244 0 C NA male 0 0 7.2250
## 1245 1 C 16.00 female 1 1 8.5167
## 1246 0 S NA male 0 0 8.0500
## 1247 0 S NA male 1 0 16.1000
## 1248 1 S NA female 1 0 16.1000
## 1249 0 S 32.00 male 0 0 7.9250
## 1250 0 Q NA male 0 0 7.7500
## 1251 0 S NA male 0 0 7.8958
## 1252 0 S 30.50 male 0 0 8.0500
## 1253 0 S 44.00 male 0 0 8.0500
## 1254 0 C NA male 0 0 7.2292
## 1255 1 S 25.00 male 0 0 0.0000
## 1256 0 C NA male 0 0 7.2292
## 1257 1 C 7.00 male 1 1 15.2458
## 1258 1 C 9.00 female 1 1 15.2458
## 1259 1 C 29.00 female 0 2 15.2458
## 1260 0 S 36.00 male 0 0 7.8958
## 1261 1 S 18.00 female 0 0 9.8417
## 1262 1 S 63.00 female 0 0 9.5875
## 1263 0 S NA male 1 1 14.5000
## 1264 0 S 11.50 male 1 1 14.5000
## 1265 0 S 40.50 male 0 2 14.5000
## 1266 0 S 10.00 female 0 2 24.1500
## 1267 0 S 36.00 male 1 1 24.1500
## 1268 0 S 30.00 female 1 1 24.1500
## 1269 0 S NA male 0 0 9.5000
## 1270 0 S 33.00 male 0 0 9.5000
## 1271 0 S 28.00 male 0 0 9.5000
## 1272 0 S 28.00 male 0 0 9.5000
## 1273 0 S 47.00 male 0 0 9.0000
## 1274 0 S 18.00 female 2 0 18.0000
## 1275 0 S 31.00 male 3 0 18.0000
## 1276 0 S 16.00 male 2 0 18.0000
## 1277 0 S 31.00 female 1 0 18.0000
## 1278 1 C 22.00 male 0 0 7.2250
## 1279 0 S 20.00 male 0 0 7.8542
## 1280 0 S 14.00 female 0 0 7.8542
## 1281 0 S 22.00 male 0 0 7.8958
## 1282 0 S 22.00 male 0 0 9.0000
## 1283 0 S NA male 0 0 8.0500
## 1284 0 S NA male 0 0 7.5500
## 1285 0 S NA male 0 0 8.0500
## 1286 0 S 32.50 male 0 0 9.5000
## 1287 1 C 38.00 female 0 0 7.2292
## 1288 0 S 51.00 male 0 0 7.7500
## 1289 0 S 18.00 male 1 0 6.4958
## 1290 0 S 21.00 male 1 0 6.4958
## 1291 1 S 47.00 female 1 0 7.0000
## 1292 0 S NA male 0 0 8.7125
## 1293 0 S NA male 0 0 7.5500
## 1294 0 S NA male 0 0 8.0500
## 1295 0 S 28.50 male 0 0 16.1000
## 1296 0 S 21.00 male 0 0 7.2500
## 1297 0 S 27.00 male 0 0 8.6625
## 1298 0 S NA male 0 0 7.2500
## 1299 0 S 36.00 male 0 0 9.5000
## 1300 0 C 27.00 male 1 0 14.4542
## 1301 1 C 15.00 female 1 0 14.4542
## 1302 0 C 45.50 male 0 0 7.2250
## 1303 0 C NA male 0 0 7.2250
## 1304 0 C NA male 0 0 14.4583
## 1305 0 C 14.50 female 1 0 14.4542
## 1306 0 C NA female 1 0 14.4542
## 1307 0 C 26.50 male 0 0 7.2250
## 1308 0 C 27.00 male 0 0 7.2250
## 1309 0 S 29.00 male 0 0 7.8750#4) Perform a statistical analysis of the titanic dataset
#Titanic Data Analysis
str(titanic)## 'data.frame': 1309 obs. of 7 variables:
## $ T5.survived: num 1 1 0 0 0 1 1 0 1 0 ...
## $ T5.embarked: chr "S" "S" "S" "S" ...
## $ T5.age : num 29 0.92 2 30 25 48 63 39 53 71 ...
## $ T5.sex : chr "female" "male" "female" "male" ...
## $ T5.sibsp : num 0 1 1 1 1 0 1 0 2 0 ...
## $ T5.parch : num 0 2 2 2 2 0 0 0 0 0 ...
## $ T5.fare : num 211 152 152 152 152 ...View(titanic)
summary(titanic)## T5.survived T5.embarked T5.age T5.sex
## Min. :0.000 Length:1309 Min. : 0.17 Length:1309
## 1st Qu.:0.000 Class :character 1st Qu.:21.00 Class :character
## Median :0.000 Mode :character Median :28.00 Mode :character
## Mean :0.382 Mean :29.88
## 3rd Qu.:1.000 3rd Qu.:39.00
## Max. :1.000 Max. :80.00
## NA's :263
## T5.sibsp T5.parch T5.fare
## Min. :0.0000 Min. :0.000 Min. : 0.000
## 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.: 7.896
## Median :0.0000 Median :0.000 Median : 14.454
## Mean :0.4989 Mean :0.385 Mean : 33.295
## 3rd Qu.:1.0000 3rd Qu.:0.000 3rd Qu.: 31.275
## Max. :8.0000 Max. :9.000 Max. :512.329
## NA's :1hist(titanic$T5.age)
hist(titanic$T5.survived)
hist(titanic$T5.fare)
hist(titanic$T5.parch)
sum(is.na(titanic))## [1] 266# summary(titanic_noNA$T5.fare)
# summary(titanic_noNA$T5.age)
# summary(titanic_noNA)#5) Survived is the dependent variable, find its proportion in the dataset
table(titanic$T5.survived)##
## 0 1
## 809 500prop.table(table(titanic$T5.survived))##
## 0 1
## 0.618029 0.381971#6) Remove NAs if any.
is.na(titanic)## T5.survived T5.embarked T5.age T5.sex T5.sibsp T5.parch T5.fare
## [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## [1300,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1301,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1302,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1303,] FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [1304,] FALSE FALSE TRUE FALSE FALSE FALSE FALSE
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## [1307,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1308,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1309,] FALSE FALSE FALSE FALSE FALSE FALSE FALSEsum(is.na(titanic))## [1] 266head(titanic)## T5.survived T5.embarked T5.age T5.sex T5.sibsp T5.parch T5.fare
## 1 1 S 29.00 female 0 0 211.3375
## 2 1 S 0.92 male 1 2 151.5500
## 3 0 S 2.00 female 1 2 151.5500
## 4 0 S 30.00 male 1 2 151.5500
## 5 0 S 25.00 female 1 2 151.5500
## 6 1 S 48.00 male 0 0 26.5500titanic_noNA <- na.omit(titanic)
titanic_noNA## T5.survived T5.embarked T5.age T5.sex T5.sibsp T5.parch T5.fare
## 1 1 S 29.00 female 0 0 211.3375
## 2 1 S 0.92 male 1 2 151.5500
## 3 0 S 2.00 female 1 2 151.5500
## 4 0 S 30.00 male 1 2 151.5500
## 5 0 S 25.00 female 1 2 151.5500
## 6 1 S 48.00 male 0 0 26.5500
## 7 1 S 63.00 female 1 0 77.9583
## 8 0 S 39.00 male 0 0 0.0000
## 9 1 S 53.00 female 2 0 51.4792
## 10 0 C 71.00 male 0 0 49.5042
## 11 0 C 47.00 male 1 0 227.5250
## 12 1 C 18.00 female 1 0 227.5250
## 13 1 C 24.00 female 0 0 69.3000
## 14 1 S 26.00 female 0 0 78.8500
## 15 1 S 80.00 male 0 0 30.0000
## 17 0 C 24.00 male 0 1 247.5208
## 18 1 C 50.00 female 0 1 247.5208
## 19 1 C 32.00 female 0 0 76.2917
## 20 0 C 36.00 male 0 0 75.2417
## 21 1 S 37.00 male 1 1 52.5542
## 22 1 S 47.00 female 1 1 52.5542
## 23 1 C 26.00 male 0 0 30.0000
## 24 1 C 42.00 female 0 0 227.5250
## 25 1 S 29.00 female 0 0 221.7792
## 26 0 C 25.00 male 0 0 26.0000
## 27 1 C 25.00 male 1 0 91.0792
## 28 1 C 19.00 female 1 0 91.0792
## 29 1 S 35.00 female 0 0 135.6333
## 30 1 S 28.00 male 0 0 26.5500
## 31 0 S 45.00 male 0 0 35.5000
## 32 1 C 40.00 male 0 0 31.0000
## 33 1 S 30.00 female 0 0 164.8667
## 34 1 S 58.00 female 0 0 26.5500
## 35 0 S 42.00 male 0 0 26.5500
## 36 1 C 45.00 female 0 0 262.3750
## 37 1 S 22.00 female 0 1 55.0000
## 39 0 S 41.00 male 0 0 30.5000
## 40 0 C 48.00 male 0 0 50.4958
## 42 1 C 44.00 female 0 0 27.7208
## 43 1 S 59.00 female 2 0 51.4792
## 44 1 C 60.00 female 0 0 76.2917
## 45 1 C 41.00 female 0 0 134.5000
## 46 0 S 45.00 male 0 0 26.5500
## 48 1 S 42.00 male 0 0 26.2875
## 49 1 C 53.00 female 0 0 27.4458
## 50 1 C 36.00 male 0 1 512.3292
## 51 1 C 58.00 female 0 1 512.3292
## 52 0 S 33.00 male 0 0 5.0000
## 53 0 S 28.00 male 0 0 47.1000
## 54 0 S 17.00 male 0 0 47.1000
## 55 1 S 11.00 male 1 2 120.0000
## 56 1 S 14.00 female 1 2 120.0000
## 57 1 S 36.00 male 1 2 120.0000
## 58 1 S 36.00 female 1 2 120.0000
## 59 0 S 49.00 male 0 0 26.0000
## 61 0 S 36.00 male 1 0 78.8500
## 62 1 S 76.00 female 1 0 78.8500
## 63 0 S 46.00 male 1 0 61.1750
## 64 1 S 47.00 female 1 0 61.1750
## 65 1 S 27.00 male 1 0 53.1000
## 66 1 S 33.00 female 1 0 53.1000
## 67 1 C 36.00 female 0 0 262.3750
## 68 1 S 30.00 female 0 0 86.5000
## 69 1 C 45.00 male 0 0 29.7000
## 72 0 C 27.00 male 1 0 136.7792
## 73 1 C 26.00 female 1 0 136.7792
## 74 1 S 22.00 female 0 0 151.5500
## 76 0 S 47.00 male 0 0 25.5875
## 77 1 C 39.00 female 1 1 83.1583
## 78 0 C 37.00 male 1 1 83.1583
## 79 1 C 64.00 female 0 2 83.1583
## 80 1 S 55.00 female 2 0 25.7000
## 82 0 S 70.00 male 1 1 71.0000
## 83 1 S 36.00 female 0 2 71.0000
## 84 1 S 64.00 female 1 1 26.5500
## 85 0 C 39.00 male 1 0 71.2833
## 86 1 C 38.00 female 1 0 71.2833
## 87 1 S 51.00 male 0 0 26.5500
## 88 1 S 27.00 male 0 0 30.5000
## 89 1 S 33.00 female 0 0 151.5500
## 90 0 S 31.00 male 1 0 52.0000
## 91 1 S 27.00 female 1 2 52.0000
## 92 1 S 31.00 male 1 0 57.0000
## 93 1 S 17.00 female 1 0 57.0000
## 94 1 S 53.00 male 1 1 81.8583
## 95 1 S 4.00 male 0 2 81.8583
## 96 1 S 54.00 female 1 1 81.8583
## 97 0 C 50.00 male 1 0 106.4250
## 98 1 C 27.00 female 1 1 247.5208
## 99 1 C 48.00 female 1 0 106.4250
## 100 1 C 48.00 female 1 0 39.6000
## 101 1 C 49.00 male 1 0 56.9292
## 102 0 C 39.00 male 0 0 29.7000
## 103 1 C 23.00 female 0 1 83.1583
## 104 1 C 38.00 female 0 0 227.5250
## 105 1 C 54.00 female 1 0 78.2667
## 106 0 C 36.00 female 0 0 31.6792
## 110 1 S 36.00 male 0 0 26.3875
## 111 0 C 30.00 male 0 0 27.7500
## 112 1 S 24.00 female 3 2 263.0000
## 113 1 S 28.00 female 3 2 263.0000
## 114 1 S 23.00 female 3 2 263.0000
## 115 0 S 19.00 male 3 2 263.0000
## 116 0 S 64.00 male 1 4 263.0000
## 117 1 S 60.00 female 1 4 263.0000
## 118 1 C 30.00 female 0 0 56.9292
## 120 1 S 50.00 male 2 0 133.6500
## 121 1 C 43.00 male 1 0 27.7208
## 123 1 C 22.00 female 0 2 49.5000
## 124 1 C 60.00 male 1 1 79.2000
## 125 1 C 48.00 female 1 1 79.2000
## 127 0 S 37.00 male 1 0 53.1000
## 128 1 S 35.00 female 1 0 53.1000
## 129 0 S 47.00 male 0 0 38.5000
## 130 1 C 35.00 female 0 0 211.5000
## 131 1 C 22.00 female 0 1 59.4000
## 132 1 C 45.00 female 0 1 59.4000
## 133 0 C 24.00 male 0 0 79.2000
## 134 1 C 49.00 male 1 0 89.1042
## 136 0 C 71.00 male 0 0 34.6542
## 137 1 C 53.00 male 0 0 28.5000
## 138 1 S 19.00 female 0 0 30.0000
## 139 0 S 38.00 male 0 1 153.4625
## 140 1 S 58.00 female 0 1 153.4625
## 141 1 C 23.00 male 0 1 63.3583
## 142 1 C 45.00 female 0 1 63.3583
## 143 0 C 46.00 male 0 0 79.2000
## 144 1 C 25.00 male 1 0 55.4417
## 145 1 C 25.00 female 1 0 55.4417
## 146 1 C 48.00 male 1 0 76.7292
## 147 1 C 49.00 female 1 0 76.7292
## 149 0 S 45.00 male 1 0 83.4750
## 150 1 S 35.00 female 1 0 83.4750
## 151 0 S 40.00 male 0 0 0.0000
## 152 1 C 27.00 male 0 0 76.7292
## 154 1 C 24.00 female 0 0 83.1583
## 155 0 S 55.00 male 1 1 93.5000
## 156 1 S 52.00 female 1 1 93.5000
## 157 0 S 42.00 male 0 0 42.5000
## 159 0 S 55.00 male 0 0 50.0000
## 160 1 C 16.00 female 0 1 57.9792
## 161 1 C 44.00 female 0 1 57.9792
## 162 1 S 51.00 female 1 0 77.9583
## 163 0 S 42.00 male 1 0 52.0000
## 164 1 S 35.00 female 1 0 52.0000
## 165 1 C 35.00 male 0 0 26.5500
## 166 1 S 38.00 male 1 0 90.0000
## 168 1 S 35.00 female 1 0 90.0000
## 170 0 C 50.00 female 0 0 28.7125
## 171 1 S 49.00 male 0 0 0.0000
## 172 0 S 46.00 male 0 0 26.0000
## 173 0 S 50.00 male 0 0 26.0000
## 174 0 C 32.50 male 0 0 211.5000
## 175 0 C 58.00 male 0 0 29.7000
## 176 0 S 41.00 male 1 0 51.8625
## 178 1 S 42.00 male 1 0 52.5542
## 179 1 S 45.00 female 1 0 52.5542
## 181 1 S 39.00 female 0 0 211.3375
## 182 1 S 49.00 female 0 0 25.9292
## 183 1 C 30.00 female 0 0 106.4250
## 184 1 C 35.00 male 0 0 512.3292
## 186 0 S 42.00 male 0 0 26.5500
## 187 1 C 55.00 female 0 0 27.7208
## 188 1 S 16.00 female 0 1 39.4000
## 189 1 S 51.00 female 0 1 39.4000
## 190 0 S 29.00 male 0 0 30.0000
## 191 1 S 21.00 female 0 0 77.9583
## 192 0 S 30.00 male 0 0 45.5000
## 193 1 C 58.00 female 0 0 146.5208
## 194 1 S 15.00 female 0 1 211.3375
## 195 0 S 30.00 male 0 0 26.0000
## 196 1 S 16.00 female 0 0 86.5000
## 198 0 S 19.00 male 1 0 53.1000
## 199 1 S 18.00 female 1 0 53.1000
## 200 1 C 24.00 female 0 0 49.5042
## 201 0 C 46.00 male 0 0 75.2417
## 202 0 S 54.00 male 0 0 51.8625
## 203 1 S 36.00 male 0 0 26.2875
## 204 0 C 28.00 male 1 0 82.1708
## 206 0 S 65.00 male 0 0 26.5500
## 207 0 Q 44.00 male 2 0 90.0000
## 208 1 Q 33.00 female 1 0 90.0000
## 209 1 Q 37.00 female 1 0 90.0000
## 210 1 C 30.00 male 1 0 57.7500
## 211 0 S 55.00 male 0 0 30.5000
## 212 0 S 47.00 male 0 0 42.4000
## 213 0 C 37.00 male 0 1 29.7000
## 214 1 C 31.00 female 1 0 113.2750
## 215 1 C 23.00 female 1 0 113.2750
## 216 0 C 58.00 male 0 2 113.2750
## 217 1 S 19.00 female 0 2 26.2833
## 218 0 S 64.00 male 0 0 26.0000
## 219 1 C 39.00 female 0 0 108.9000
## 221 1 C 22.00 female 0 1 61.9792
## 222 0 C 65.00 male 0 1 61.9792
## 223 0 C 28.50 male 0 0 27.7208
## 225 0 S 45.50 male 0 0 28.5000
## 226 0 S 23.00 male 0 0 93.5000
## 227 0 S 29.00 male 1 0 66.6000
## 228 1 S 22.00 female 1 0 66.6000
## 229 0 C 18.00 male 1 0 108.9000
## 230 1 C 17.00 female 1 0 108.9000
## 231 1 S 30.00 female 0 0 93.5000
## 232 1 S 52.00 male 0 0 30.5000
## 233 0 S 47.00 male 0 0 52.0000
## 234 1 C 56.00 female 0 1 83.1583
## 235 0 S 38.00 male 0 0 0.0000
## 237 0 C 22.00 male 0 0 135.6333
## 239 1 S 43.00 female 0 1 211.3375
## 240 0 S 31.00 male 0 0 50.4958
## 241 1 S 45.00 male 0 0 26.5500
## 243 1 C 33.00 female 0 0 27.7208
## 244 0 C 46.00 male 0 0 79.2000
## 245 0 C 36.00 male 0 0 40.1250
## 246 1 S 33.00 female 0 0 86.5000
## 247 0 C 55.00 male 1 0 59.4000
## 248 1 C 54.00 female 1 0 59.4000
## 249 0 S 33.00 male 0 0 26.5500
## 250 1 C 13.00 male 2 2 262.3750
## 251 1 C 18.00 female 2 2 262.3750
## 252 1 C 21.00 female 2 2 262.3750
## 253 0 C 61.00 male 1 3 262.3750
## 254 1 C 48.00 female 1 3 262.3750
## 256 1 C 24.00 female 0 0 69.3000
## 258 1 C 35.00 female 1 0 57.7500
## 259 1 C 30.00 female 0 0 31.0000
## 260 1 S 34.00 male 0 0 26.5500
## 261 1 S 40.00 female 0 0 153.4625
## 262 1 S 35.00 male 0 0 26.2875
## 263 0 S 50.00 male 1 0 55.9000
## 264 1 S 39.00 female 1 0 55.9000
## 265 1 C 56.00 male 0 0 35.5000
## 266 1 S 28.00 male 0 0 35.5000
## 267 0 S 56.00 male 0 0 26.5500
## 268 0 C 56.00 male 0 0 30.6958
## 269 0 S 24.00 male 1 0 60.0000
## 271 1 S 18.00 female 1 0 60.0000
## 272 1 S 24.00 male 1 0 82.2667
## 273 1 S 23.00 female 1 0 82.2667
## 274 1 C 6.00 male 0 2 134.5000
## 275 1 C 45.00 male 1 1 134.5000
## 276 1 C 40.00 female 1 1 134.5000
## 277 0 C 57.00 male 1 0 146.5208
## 279 1 C 32.00 male 0 0 30.5000
## 280 0 S 62.00 male 0 0 26.5500
## 281 1 C 54.00 male 1 0 55.4417
## 282 1 C 43.00 female 1 0 55.4417
## 283 1 C 52.00 female 1 0 78.2667
## 286 0 S 67.00 male 1 0 221.7792
## 287 0 S 63.00 female 1 0 221.7792
## 288 0 S 61.00 male 0 0 32.3208
## 289 1 S 48.00 female 0 0 25.9292
## 290 1 S 18.00 female 0 2 79.6500
## 291 0 S 52.00 male 1 1 79.6500
## 292 1 S 39.00 female 1 1 79.6500
## 293 1 S 48.00 male 1 0 52.0000
## 295 0 C 49.00 male 1 1 110.8833
## 296 1 C 17.00 male 0 2 110.8833
## 297 1 C 39.00 female 1 1 110.8833
## 299 1 C 31.00 male 0 0 28.5375
## 300 0 C 40.00 male 0 0 27.7208
## 301 0 S 61.00 male 0 0 33.5000
## 302 0 S 47.00 male 0 0 34.0208
## 303 1 C 35.00 female 0 0 512.3292
## 304 0 C 64.00 male 1 0 75.2500
## 305 1 C 60.00 female 1 0 75.2500
## 306 0 S 60.00 male 0 0 26.5500
## 307 0 S 54.00 male 0 1 77.2875
## 308 0 S 21.00 male 0 1 77.2875
## 309 1 C 55.00 female 0 0 135.6333
## 310 1 S 31.00 female 0 2 164.8667
## 311 0 S 57.00 male 1 1 164.8667
## 312 1 S 45.00 female 1 1 164.8667
## 313 0 C 50.00 male 1 1 211.5000
## 314 0 C 27.00 male 0 2 211.5000
## 315 1 C 50.00 female 1 1 211.5000
## 316 1 S 21.00 female 0 0 26.5500
## 317 0 C 51.00 male 0 1 61.3792
## 318 1 C 21.00 male 0 1 61.3792
## 320 1 C 31.00 female 0 0 134.5000
## 322 0 S 62.00 male 0 0 26.5500
## 323 1 C 36.00 female 0 0 135.6333
## 324 0 C 30.00 male 1 0 24.0000
## 325 1 C 28.00 female 1 0 24.0000
## 326 0 S 30.00 male 0 0 13.0000
## 327 0 S 18.00 male 0 0 11.5000
## 328 0 S 25.00 male 0 0 10.5000
## 329 0 S 34.00 male 1 0 26.0000
## 330 1 S 36.00 female 1 0 26.0000
## 331 0 S 57.00 male 0 0 13.0000
## 332 0 S 18.00 male 0 0 11.5000
## 333 0 S 23.00 male 0 0 10.5000
## 334 1 S 36.00 female 0 0 13.0000
## 335 0 S 28.00 male 0 0 10.5000
## 336 0 S 51.00 male 0 0 12.5250
## 337 1 S 32.00 male 1 0 26.0000
## 338 1 S 19.00 female 1 0 26.0000
## 339 0 S 28.00 male 0 0 26.0000
## 340 1 S 1.00 male 2 1 39.0000
## 341 1 S 4.00 female 2 1 39.0000
## 342 1 S 12.00 female 2 1 39.0000
## 343 1 S 36.00 female 0 3 39.0000
## 344 1 S 34.00 male 0 0 13.0000
## 345 1 S 19.00 female 0 0 13.0000
## 346 0 S 23.00 male 0 0 13.0000
## 347 0 S 26.00 male 0 0 13.0000
## 348 0 S 42.00 male 0 0 13.0000
## 349 0 S 27.00 male 0 0 13.0000
## 350 1 S 24.00 female 0 0 13.0000
## 351 1 S 15.00 female 0 2 39.0000
## 352 0 S 60.00 male 1 1 39.0000
## 353 1 S 40.00 female 1 1 39.0000
## 354 1 S 20.00 female 1 0 26.0000
## 355 0 S 25.00 male 1 0 26.0000
## 356 1 S 36.00 female 0 0 13.0000
## 357 0 S 25.00 male 0 0 13.0000
## 358 0 S 42.00 male 0 0 13.0000
## 359 1 S 42.00 female 0 0 13.0000
## 360 1 S 0.83 male 0 2 29.0000
## 361 1 S 26.00 male 1 1 29.0000
## 362 1 S 22.00 female 1 1 29.0000
## 363 1 S 35.00 female 0 0 21.0000
## 365 0 S 19.00 male 0 0 13.0000
## 366 0 S 44.00 female 1 0 26.0000
## 367 0 S 54.00 male 1 0 26.0000
## 368 0 S 52.00 male 0 0 13.5000
## 369 0 S 37.00 male 1 0 26.0000
## 370 0 S 29.00 female 1 0 26.0000
## 371 1 S 25.00 female 1 1 30.0000
## 372 1 S 45.00 female 0 2 30.0000
## 373 0 S 29.00 male 1 0 26.0000
## 374 1 S 28.00 female 1 0 26.0000
## 375 0 S 29.00 male 0 0 10.5000
## 376 0 S 28.00 male 0 0 13.0000
## 377 1 S 24.00 male 0 0 10.5000
## 378 1 S 8.00 female 0 2 26.2500
## 379 0 S 31.00 male 1 1 26.2500
## 380 1 S 31.00 female 1 1 26.2500
## 381 1 S 22.00 female 0 0 10.5000
## 382 0 S 30.00 female 0 0 13.0000
## 384 0 S 21.00 male 0 0 11.5000
## 386 1 S 8.00 male 1 1 36.7500
## 387 0 S 18.00 male 0 0 73.5000
## 388 1 S 48.00 female 0 2 36.7500
## 389 1 S 28.00 female 0 0 13.0000
## 390 0 S 32.00 male 0 0 13.0000
## 391 0 S 17.00 male 0 0 73.5000
## 392 0 C 29.00 male 1 0 27.7208
## 393 1 C 24.00 female 1 0 27.7208
## 394 0 S 25.00 male 0 0 31.5000
## 395 0 S 18.00 male 0 0 73.5000
## 396 1 S 18.00 female 0 1 23.0000
## 397 1 S 34.00 female 0 1 23.0000
## 398 0 S 54.00 male 0 0 26.0000
## 399 1 S 8.00 male 0 2 32.5000
## 400 0 S 42.00 male 1 1 32.5000
## 401 1 S 34.00 female 1 1 32.5000
## 402 1 C 27.00 female 1 0 13.8583
## 403 1 C 30.00 female 1 0 13.8583
## 404 0 S 23.00 male 0 0 13.0000
## 405 0 S 21.00 male 0 0 13.0000
## 406 0 S 18.00 male 0 0 13.0000
## 407 0 S 40.00 male 1 0 26.0000
## 408 1 S 29.00 female 1 0 26.0000
## 409 0 S 18.00 male 0 0 10.5000
## 410 0 S 36.00 male 0 0 13.0000
## 412 0 S 38.00 female 0 0 13.0000
## 413 0 S 35.00 male 0 0 26.0000
## 414 0 S 38.00 male 1 0 21.0000
## 415 0 S 34.00 male 1 0 21.0000
## 416 1 S 34.00 female 0 0 13.0000
## 417 0 S 16.00 male 0 0 26.0000
## 418 0 S 26.00 male 0 0 10.5000
## 419 0 S 47.00 male 0 0 10.5000
## 420 0 S 21.00 male 1 0 11.5000
## 421 0 S 21.00 male 1 0 11.5000
## 422 0 S 24.00 male 0 0 13.5000
## 423 0 S 24.00 male 0 0 13.0000
## 424 0 S 34.00 male 0 0 13.0000
## 425 0 S 30.00 male 0 0 13.0000
## 426 0 S 52.00 male 0 0 13.0000
## 427 0 S 30.00 male 0 0 13.0000
## 428 1 S 0.67 male 1 1 14.5000
## 429 1 S 24.00 female 0 2 14.5000
## 430 0 S 44.00 male 0 0 13.0000
## 431 1 S 6.00 female 0 1 33.0000
## 432 0 S 28.00 male 0 1 33.0000
## 433 1 S 62.00 male 0 0 10.5000
## 434 0 S 30.00 male 0 0 10.5000
## 435 1 S 7.00 female 0 2 26.2500
## 436 0 S 43.00 male 1 1 26.2500
## 437 1 S 45.00 female 1 1 26.2500
## 438 1 S 24.00 female 1 2 65.0000
## 439 1 S 24.00 female 1 2 65.0000
## 440 0 S 49.00 male 1 2 65.0000
## 441 1 S 48.00 female 1 2 65.0000
## 442 1 S 55.00 female 0 0 16.0000
## 443 0 S 24.00 male 2 0 73.5000
## 444 0 S 32.00 male 2 0 73.5000
## 445 0 S 21.00 male 2 0 73.5000
## 446 0 S 18.00 female 1 1 13.0000
## 447 1 S 20.00 female 2 1 23.0000
## 448 0 S 23.00 male 2 1 11.5000
## 449 0 S 36.00 male 0 0 13.0000
## 450 1 S 54.00 female 1 3 23.0000
## 451 0 S 50.00 male 0 0 13.0000
## 452 0 S 44.00 male 1 0 26.0000
## 453 1 S 29.00 female 1 0 26.0000
## 454 0 S 21.00 male 0 0 73.5000
## 455 1 S 42.00 male 0 0 13.0000
## 456 0 S 63.00 male 1 0 26.0000
## 457 0 S 60.00 female 1 0 26.0000
## 458 0 S 33.00 male 0 0 12.2750
## 459 1 S 17.00 female 0 0 10.5000
## 460 0 S 42.00 male 1 0 27.0000
## 461 1 S 24.00 female 2 1 27.0000
## 462 0 S 47.00 male 0 0 15.0000
## 463 0 S 24.00 male 2 0 31.5000
## 464 0 S 22.00 male 2 0 31.5000
## 465 0 S 32.00 male 0 0 10.5000
## 466 1 C 23.00 female 0 0 13.7917
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## 1111 0 S 32.00 male 0 0 7.8958
## 1112 0 S 0.75 male 1 1 13.7750
## 1113 0 S 3.00 female 1 1 13.7750
## 1114 0 S 26.00 female 0 2 13.7750
## 1118 0 S 21.00 male 0 0 7.9250
## 1119 0 S 25.00 male 0 0 7.9250
## 1120 0 S 22.00 male 0 0 7.2500
## 1121 1 S 25.00 male 1 0 7.7750
## 1126 0 S 24.00 male 0 0 8.0500
## 1127 0 S 28.00 female 0 0 7.8958
## 1128 0 S 19.00 male 0 0 7.8958
## 1130 0 S 25.00 male 1 0 7.7750
## 1131 0 S 18.00 female 0 0 7.7750
## 1132 1 S 32.00 male 0 0 8.0500
## 1134 0 S 17.00 male 0 0 8.6625
## 1135 0 S 24.00 male 0 0 8.6625
## 1140 0 S 38.00 male 0 0 7.8958
## 1141 0 S 21.00 male 0 0 8.0500
## 1142 0 Q 10.00 male 4 1 29.1250
## 1143 0 Q 4.00 male 4 1 29.1250
## 1144 0 Q 7.00 male 4 1 29.1250
## 1145 0 Q 2.00 male 4 1 29.1250
## 1146 0 Q 8.00 male 4 1 29.1250
## 1147 0 Q 39.00 female 0 5 29.1250
## 1148 0 S 22.00 female 0 0 39.6875
## 1149 0 S 35.00 male 0 0 7.1250
## 1153 0 S 50.00 male 1 0 14.5000
## 1154 0 S 47.00 female 1 0 14.5000
## 1157 0 S 2.00 female 1 1 20.2125
## 1158 0 S 18.00 male 1 1 20.2125
## 1159 0 S 41.00 female 0 2 20.2125
## 1161 0 S 50.00 male 0 0 8.0500
## 1162 0 S 16.00 male 0 0 8.0500
## 1166 0 C 25.00 male 0 0 7.2250
## 1170 0 S 38.50 male 0 0 7.2500
## 1172 0 S 14.50 male 8 2 69.5500
## 1182 0 S 24.00 male 0 0 9.3250
## 1183 1 S 21.00 female 0 0 7.6500
## 1184 0 S 39.00 male 0 0 7.9250
## 1188 1 S 1.00 female 1 1 16.7000
## 1189 1 S 24.00 female 0 2 16.7000
## 1190 1 S 4.00 female 1 1 16.7000
## 1191 1 S 25.00 male 0 0 9.5000
## 1192 0 S 20.00 male 0 0 8.0500
## 1193 0 S 24.50 male 0 0 8.0500
## 1197 1 S 29.00 male 0 0 9.5000
## 1202 0 C 22.00 male 0 0 7.2292
## 1204 0 S 40.00 male 0 0 7.8958
## 1205 0 S 21.00 male 0 0 7.9250
## 1206 1 S 18.00 female 0 0 7.4958
## 1207 0 S 4.00 male 3 2 27.9000
## 1208 0 S 10.00 male 3 2 27.9000
## 1209 0 S 9.00 female 3 2 27.9000
## 1210 0 S 2.00 female 3 2 27.9000
## 1211 0 S 40.00 male 1 4 27.9000
## 1212 0 S 45.00 female 1 4 27.9000
## 1218 0 S 19.00 male 0 0 7.6500
## 1219 0 S 30.00 male 0 0 8.0500
## 1221 0 S 32.00 male 0 0 8.0500
## 1223 0 C 33.00 male 0 0 8.6625
## 1224 1 S 23.00 female 0 0 7.5500
## 1225 0 S 21.00 male 0 0 8.0500
## 1227 0 S 19.00 male 0 0 7.8958
## 1228 0 S 22.00 female 0 0 9.8375
## 1229 1 S 31.00 male 0 0 7.9250
## 1230 0 S 27.00 male 0 0 8.6625
## 1231 0 S 2.00 female 0 1 10.4625
## 1232 0 S 29.00 female 1 1 10.4625
## 1233 1 S 16.00 male 0 0 8.0500
## 1234 1 S 44.00 male 0 0 7.9250
## 1235 0 S 25.00 male 0 0 7.0500
## 1236 0 S 74.00 male 0 0 7.7750
## 1237 1 S 14.00 male 0 0 9.2250
## 1238 0 S 24.00 male 0 0 7.7958
## 1239 1 S 25.00 male 0 0 7.7958
## 1240 0 S 34.00 male 0 0 8.0500
## 1241 1 C 0.42 male 0 1 8.5167
## 1245 1 C 16.00 female 1 1 8.5167
## 1249 0 S 32.00 male 0 0 7.9250
## 1252 0 S 30.50 male 0 0 8.0500
## 1253 0 S 44.00 male 0 0 8.0500
## 1255 1 S 25.00 male 0 0 0.0000
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## 1258 1 C 9.00 female 1 1 15.2458
## 1259 1 C 29.00 female 0 2 15.2458
## 1260 0 S 36.00 male 0 0 7.8958
## 1261 1 S 18.00 female 0 0 9.8417
## 1262 1 S 63.00 female 0 0 9.5875
## 1264 0 S 11.50 male 1 1 14.5000
## 1265 0 S 40.50 male 0 2 14.5000
## 1266 0 S 10.00 female 0 2 24.1500
## 1267 0 S 36.00 male 1 1 24.1500
## 1268 0 S 30.00 female 1 1 24.1500
## 1270 0 S 33.00 male 0 0 9.5000
## 1271 0 S 28.00 male 0 0 9.5000
## 1272 0 S 28.00 male 0 0 9.5000
## 1273 0 S 47.00 male 0 0 9.0000
## 1274 0 S 18.00 female 2 0 18.0000
## 1275 0 S 31.00 male 3 0 18.0000
## 1276 0 S 16.00 male 2 0 18.0000
## 1277 0 S 31.00 female 1 0 18.0000
## 1278 1 C 22.00 male 0 0 7.2250
## 1279 0 S 20.00 male 0 0 7.8542
## 1280 0 S 14.00 female 0 0 7.8542
## 1281 0 S 22.00 male 0 0 7.8958
## 1282 0 S 22.00 male 0 0 9.0000
## 1286 0 S 32.50 male 0 0 9.5000
## 1287 1 C 38.00 female 0 0 7.2292
## 1288 0 S 51.00 male 0 0 7.7500
## 1289 0 S 18.00 male 1 0 6.4958
## 1290 0 S 21.00 male 1 0 6.4958
## 1291 1 S 47.00 female 1 0 7.0000
## 1295 0 S 28.50 male 0 0 16.1000
## 1296 0 S 21.00 male 0 0 7.2500
## 1297 0 S 27.00 male 0 0 8.6625
## 1299 0 S 36.00 male 0 0 9.5000
## 1300 0 C 27.00 male 1 0 14.4542
## 1301 1 C 15.00 female 1 0 14.4542
## 1302 0 C 45.50 male 0 0 7.2250
## 1305 0 C 14.50 female 1 0 14.4542
## 1307 0 C 26.50 male 0 0 7.2250
## 1308 0 C 27.00 male 0 0 7.2250
## 1309 0 S 29.00 male 0 0 7.8750summary(titanic_noNA)## T5.survived T5.embarked T5.age T5.sex
## Min. :0.0000 Length:1043 Min. : 0.17 Length:1043
## 1st Qu.:0.0000 Class :character 1st Qu.:21.00 Class :character
## Median :0.0000 Mode :character Median :28.00 Mode :character
## Mean :0.4075 Mean :29.81
## 3rd Qu.:1.0000 3rd Qu.:39.00
## Max. :1.0000 Max. :80.00
## T5.sibsp T5.parch T5.fare
## Min. :0.0000 Min. :0.0000 Min. : 0.00
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.: 8.05
## Median :0.0000 Median :0.0000 Median : 15.75
## Mean :0.5043 Mean :0.4219 Mean : 36.60
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.: 35.08
## Max. :8.0000 Max. :6.0000 Max. :512.33dim(titanic) ### titanic with NA## [1] 1309 7dim(titanic_noNA) #### Titanic without NA## [1] 1043 7ncol(titanic_noNA)## [1] 7str(titanic_noNA)## 'data.frame': 1043 obs. of 7 variables:
## $ T5.survived: num 1 1 0 0 0 1 1 0 1 0 ...
## $ T5.embarked: chr "S" "S" "S" "S" ...
## $ T5.age : num 29 0.92 2 30 25 48 63 39 53 71 ...
## $ T5.sex : chr "female" "male" "female" "male" ...
## $ T5.sibsp : num 0 1 1 1 1 0 1 0 2 0 ...
## $ T5.parch : num 0 2 2 2 2 0 0 0 0 0 ...
## $ T5.fare : num 211 152 152 152 152 ...
## - attr(*, "na.action")= 'omit' Named int [1:266] 16 38 41 47 60 70 71 75 81 107 ...
## ..- attr(*, "names")= chr [1:266] "16" "38" "41" "47" ...titanic_noNA## T5.survived T5.embarked T5.age T5.sex T5.sibsp T5.parch T5.fare
## 1 1 S 29.00 female 0 0 211.3375
## 2 1 S 0.92 male 1 2 151.5500
## 3 0 S 2.00 female 1 2 151.5500
## 4 0 S 30.00 male 1 2 151.5500
## 5 0 S 25.00 female 1 2 151.5500
## 6 1 S 48.00 male 0 0 26.5500
## 7 1 S 63.00 female 1 0 77.9583
## 8 0 S 39.00 male 0 0 0.0000
## 9 1 S 53.00 female 2 0 51.4792
## 10 0 C 71.00 male 0 0 49.5042
## 11 0 C 47.00 male 1 0 227.5250
## 12 1 C 18.00 female 1 0 227.5250
## 13 1 C 24.00 female 0 0 69.3000
## 14 1 S 26.00 female 0 0 78.8500
## 15 1 S 80.00 male 0 0 30.0000
## 17 0 C 24.00 male 0 1 247.5208
## 18 1 C 50.00 female 0 1 247.5208
## 19 1 C 32.00 female 0 0 76.2917
## 20 0 C 36.00 male 0 0 75.2417
## 21 1 S 37.00 male 1 1 52.5542
## 22 1 S 47.00 female 1 1 52.5542
## 23 1 C 26.00 male 0 0 30.0000
## 24 1 C 42.00 female 0 0 227.5250
## 25 1 S 29.00 female 0 0 221.7792
## 26 0 C 25.00 male 0 0 26.0000
## 27 1 C 25.00 male 1 0 91.0792
## 28 1 C 19.00 female 1 0 91.0792
## 29 1 S 35.00 female 0 0 135.6333
## 30 1 S 28.00 male 0 0 26.5500
## 31 0 S 45.00 male 0 0 35.5000
## 32 1 C 40.00 male 0 0 31.0000
## 33 1 S 30.00 female 0 0 164.8667
## 34 1 S 58.00 female 0 0 26.5500
## 35 0 S 42.00 male 0 0 26.5500
## 36 1 C 45.00 female 0 0 262.3750
## 37 1 S 22.00 female 0 1 55.0000
## 39 0 S 41.00 male 0 0 30.5000
## 40 0 C 48.00 male 0 0 50.4958
## 42 1 C 44.00 female 0 0 27.7208
## 43 1 S 59.00 female 2 0 51.4792
## 44 1 C 60.00 female 0 0 76.2917
## 45 1 C 41.00 female 0 0 134.5000
## 46 0 S 45.00 male 0 0 26.5500
## 48 1 S 42.00 male 0 0 26.2875
## 49 1 C 53.00 female 0 0 27.4458
## 50 1 C 36.00 male 0 1 512.3292
## 51 1 C 58.00 female 0 1 512.3292
## 52 0 S 33.00 male 0 0 5.0000
## 53 0 S 28.00 male 0 0 47.1000
## 54 0 S 17.00 male 0 0 47.1000
## 55 1 S 11.00 male 1 2 120.0000
## 56 1 S 14.00 female 1 2 120.0000
## 57 1 S 36.00 male 1 2 120.0000
## 58 1 S 36.00 female 1 2 120.0000
## 59 0 S 49.00 male 0 0 26.0000
## 61 0 S 36.00 male 1 0 78.8500
## 62 1 S 76.00 female 1 0 78.8500
## 63 0 S 46.00 male 1 0 61.1750
## 64 1 S 47.00 female 1 0 61.1750
## 65 1 S 27.00 male 1 0 53.1000
## 66 1 S 33.00 female 1 0 53.1000
## 67 1 C 36.00 female 0 0 262.3750
## 68 1 S 30.00 female 0 0 86.5000
## 69 1 C 45.00 male 0 0 29.7000
## 72 0 C 27.00 male 1 0 136.7792
## 73 1 C 26.00 female 1 0 136.7792
## 74 1 S 22.00 female 0 0 151.5500
## 76 0 S 47.00 male 0 0 25.5875
## 77 1 C 39.00 female 1 1 83.1583
## 78 0 C 37.00 male 1 1 83.1583
## 79 1 C 64.00 female 0 2 83.1583
## 80 1 S 55.00 female 2 0 25.7000
## 82 0 S 70.00 male 1 1 71.0000
## 83 1 S 36.00 female 0 2 71.0000
## 84 1 S 64.00 female 1 1 26.5500
## 85 0 C 39.00 male 1 0 71.2833
## 86 1 C 38.00 female 1 0 71.2833
## 87 1 S 51.00 male 0 0 26.5500
## 88 1 S 27.00 male 0 0 30.5000
## 89 1 S 33.00 female 0 0 151.5500
## 90 0 S 31.00 male 1 0 52.0000
## 91 1 S 27.00 female 1 2 52.0000
## 92 1 S 31.00 male 1 0 57.0000
## 93 1 S 17.00 female 1 0 57.0000
## 94 1 S 53.00 male 1 1 81.8583
## 95 1 S 4.00 male 0 2 81.8583
## 96 1 S 54.00 female 1 1 81.8583
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## 98 1 C 27.00 female 1 1 247.5208
## 99 1 C 48.00 female 1 0 106.4250
## 100 1 C 48.00 female 1 0 39.6000
## 101 1 C 49.00 male 1 0 56.9292
## 102 0 C 39.00 male 0 0 29.7000
## 103 1 C 23.00 female 0 1 83.1583
## 104 1 C 38.00 female 0 0 227.5250
## 105 1 C 54.00 female 1 0 78.2667
## 106 0 C 36.00 female 0 0 31.6792
## 110 1 S 36.00 male 0 0 26.3875
## 111 0 C 30.00 male 0 0 27.7500
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## 113 1 S 28.00 female 3 2 263.0000
## 114 1 S 23.00 female 3 2 263.0000
## 115 0 S 19.00 male 3 2 263.0000
## 116 0 S 64.00 male 1 4 263.0000
## 117 1 S 60.00 female 1 4 263.0000
## 118 1 C 30.00 female 0 0 56.9292
## 120 1 S 50.00 male 2 0 133.6500
## 121 1 C 43.00 male 1 0 27.7208
## 123 1 C 22.00 female 0 2 49.5000
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## 125 1 C 48.00 female 1 1 79.2000
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## 128 1 S 35.00 female 1 0 53.1000
## 129 0 S 47.00 male 0 0 38.5000
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## 132 1 C 45.00 female 0 1 59.4000
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## 134 1 C 49.00 male 1 0 89.1042
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## 138 1 S 19.00 female 0 0 30.0000
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## 140 1 S 58.00 female 0 1 153.4625
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## 142 1 C 45.00 female 0 1 63.3583
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## 145 1 C 25.00 female 1 0 55.4417
## 146 1 C 48.00 male 1 0 76.7292
## 147 1 C 49.00 female 1 0 76.7292
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## 150 1 S 35.00 female 1 0 83.4750
## 151 0 S 40.00 male 0 0 0.0000
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## 154 1 C 24.00 female 0 0 83.1583
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## 156 1 S 52.00 female 1 1 93.5000
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## 160 1 C 16.00 female 0 1 57.9792
## 161 1 C 44.00 female 0 1 57.9792
## 162 1 S 51.00 female 1 0 77.9583
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## 164 1 S 35.00 female 1 0 52.0000
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## 168 1 S 35.00 female 1 0 90.0000
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## 179 1 S 45.00 female 1 0 52.5542
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## 188 1 S 16.00 female 0 1 39.4000
## 189 1 S 51.00 female 0 1 39.4000
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## 191 1 S 21.00 female 0 0 77.9583
## 192 0 S 30.00 male 0 0 45.5000
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## 194 1 S 15.00 female 0 1 211.3375
## 195 0 S 30.00 male 0 0 26.0000
## 196 1 S 16.00 female 0 0 86.5000
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## 199 1 S 18.00 female 1 0 53.1000
## 200 1 C 24.00 female 0 0 49.5042
## 201 0 C 46.00 male 0 0 75.2417
## 202 0 S 54.00 male 0 0 51.8625
## 203 1 S 36.00 male 0 0 26.2875
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## 206 0 S 65.00 male 0 0 26.5500
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## 209 1 Q 37.00 female 1 0 90.0000
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## 251 1 C 18.00 female 2 2 262.3750
## 252 1 C 21.00 female 2 2 262.3750
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## 273 1 S 23.00 female 1 0 82.2667
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## 276 1 C 40.00 female 1 1 134.5000
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## 341 1 S 4.00 female 2 1 39.0000
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## 345 1 S 19.00 female 0 0 13.0000
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## 352 0 S 60.00 male 1 1 39.0000
## 353 1 S 40.00 female 1 1 39.0000
## 354 1 S 20.00 female 1 0 26.0000
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## 358 0 S 42.00 male 0 0 13.0000
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## 361 1 S 26.00 male 1 1 29.0000
## 362 1 S 22.00 female 1 1 29.0000
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## 389 1 S 28.00 female 0 0 13.0000
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## 993 0 Q 30.50 female 0 0 7.7500
## 996 0 C 35.00 male 0 0 7.8958
## 997 0 S 33.00 male 0 0 7.8958
## 1008 1 Q 15.00 female 0 0 8.0292
## 1009 0 Q 35.00 female 0 0 7.7500
## 1011 0 S 24.00 male 1 0 16.1000
## 1012 0 S 19.00 female 1 0 16.1000
## 1016 0 S 55.50 male 0 0 8.0500
## 1018 1 S 21.00 male 0 0 7.7750
## 1020 0 S 24.00 male 0 0 7.8958
## 1021 0 S 21.00 male 0 0 7.8958
## 1022 0 S 28.00 male 0 0 7.8958
## 1025 0 S 25.00 male 0 0 7.6500
## 1026 1 S 6.00 male 0 1 12.4750
## 1027 1 S 27.00 female 0 1 12.4750
## 1032 0 S 34.00 male 0 0 8.0500
## 1041 1 Q 24.00 female 0 0 7.7500
## 1046 0 S 18.00 male 0 0 7.7500
## 1047 0 S 22.00 male 0 0 7.8958
## 1048 1 C 15.00 female 0 0 7.2250
## 1049 1 C 1.00 female 0 2 15.7417
## 1050 1 C 20.00 male 1 1 15.7417
## 1051 1 C 19.00 female 1 1 15.7417
## 1052 0 S 33.00 male 0 0 8.0500
## 1057 1 C 12.00 male 1 0 11.2417
## 1058 1 C 14.00 female 1 0 11.2417
## 1059 0 S 29.00 female 0 0 7.9250
## 1060 0 S 28.00 male 0 0 8.0500
## 1061 1 S 18.00 female 0 0 7.7750
## 1062 1 S 26.00 female 0 0 7.8542
## 1063 0 S 21.00 male 0 0 7.8542
## 1064 0 S 41.00 male 0 0 7.1250
## 1065 1 S 39.00 male 0 0 7.9250
## 1066 0 S 21.00 male 0 0 7.8000
## 1067 0 C 28.50 male 0 0 7.2292
## 1068 1 S 22.00 female 0 0 7.7500
## 1069 0 S 61.00 male 0 0 6.2375
## 1076 0 S 23.00 male 0 0 9.2250
## 1080 1 S 22.00 female 0 0 7.7750
## 1083 1 S 9.00 male 0 1 3.1708
## 1084 0 S 28.00 male 0 0 22.5250
## 1085 0 S 42.00 male 0 1 8.4042
## 1087 0 S 31.00 female 0 0 7.8542
## 1088 0 S 28.00 male 0 0 7.8542
## 1089 1 S 32.00 male 0 0 7.7750
## 1090 0 S 20.00 male 0 0 9.2250
## 1091 0 S 23.00 female 0 0 8.6625
## 1092 0 S 20.00 female 0 0 8.6625
## 1093 0 S 20.00 male 0 0 8.6625
## 1094 0 S 16.00 male 0 0 9.2167
## 1095 1 S 31.00 female 0 0 8.6833
## 1097 0 S 2.00 male 3 1 21.0750
## 1098 0 S 6.00 male 3 1 21.0750
## 1099 0 S 3.00 female 3 1 21.0750
## 1100 0 S 8.00 female 3 1 21.0750
## 1101 0 S 29.00 female 0 4 21.0750
## 1102 0 S 1.00 male 4 1 39.6875
## 1103 0 S 7.00 male 4 1 39.6875
## 1104 0 S 2.00 male 4 1 39.6875
## 1105 0 S 16.00 male 4 1 39.6875
## 1106 0 S 14.00 male 4 1 39.6875
## 1107 0 S 41.00 female 0 5 39.6875
## 1108 0 S 21.00 male 0 0 8.6625
## 1109 0 S 19.00 male 0 0 14.5000
## 1111 0 S 32.00 male 0 0 7.8958
## 1112 0 S 0.75 male 1 1 13.7750
## 1113 0 S 3.00 female 1 1 13.7750
## 1114 0 S 26.00 female 0 2 13.7750
## 1118 0 S 21.00 male 0 0 7.9250
## 1119 0 S 25.00 male 0 0 7.9250
## 1120 0 S 22.00 male 0 0 7.2500
## 1121 1 S 25.00 male 1 0 7.7750
## 1126 0 S 24.00 male 0 0 8.0500
## 1127 0 S 28.00 female 0 0 7.8958
## 1128 0 S 19.00 male 0 0 7.8958
## 1130 0 S 25.00 male 1 0 7.7750
## 1131 0 S 18.00 female 0 0 7.7750
## 1132 1 S 32.00 male 0 0 8.0500
## 1134 0 S 17.00 male 0 0 8.6625
## 1135 0 S 24.00 male 0 0 8.6625
## 1140 0 S 38.00 male 0 0 7.8958
## 1141 0 S 21.00 male 0 0 8.0500
## 1142 0 Q 10.00 male 4 1 29.1250
## 1143 0 Q 4.00 male 4 1 29.1250
## 1144 0 Q 7.00 male 4 1 29.1250
## 1145 0 Q 2.00 male 4 1 29.1250
## 1146 0 Q 8.00 male 4 1 29.1250
## 1147 0 Q 39.00 female 0 5 29.1250
## 1148 0 S 22.00 female 0 0 39.6875
## 1149 0 S 35.00 male 0 0 7.1250
## 1153 0 S 50.00 male 1 0 14.5000
## 1154 0 S 47.00 female 1 0 14.5000
## 1157 0 S 2.00 female 1 1 20.2125
## 1158 0 S 18.00 male 1 1 20.2125
## 1159 0 S 41.00 female 0 2 20.2125
## 1161 0 S 50.00 male 0 0 8.0500
## 1162 0 S 16.00 male 0 0 8.0500
## 1166 0 C 25.00 male 0 0 7.2250
## 1170 0 S 38.50 male 0 0 7.2500
## 1172 0 S 14.50 male 8 2 69.5500
## 1182 0 S 24.00 male 0 0 9.3250
## 1183 1 S 21.00 female 0 0 7.6500
## 1184 0 S 39.00 male 0 0 7.9250
## 1188 1 S 1.00 female 1 1 16.7000
## 1189 1 S 24.00 female 0 2 16.7000
## 1190 1 S 4.00 female 1 1 16.7000
## 1191 1 S 25.00 male 0 0 9.5000
## 1192 0 S 20.00 male 0 0 8.0500
## 1193 0 S 24.50 male 0 0 8.0500
## 1197 1 S 29.00 male 0 0 9.5000
## 1202 0 C 22.00 male 0 0 7.2292
## 1204 0 S 40.00 male 0 0 7.8958
## 1205 0 S 21.00 male 0 0 7.9250
## 1206 1 S 18.00 female 0 0 7.4958
## 1207 0 S 4.00 male 3 2 27.9000
## 1208 0 S 10.00 male 3 2 27.9000
## 1209 0 S 9.00 female 3 2 27.9000
## 1210 0 S 2.00 female 3 2 27.9000
## 1211 0 S 40.00 male 1 4 27.9000
## 1212 0 S 45.00 female 1 4 27.9000
## 1218 0 S 19.00 male 0 0 7.6500
## 1219 0 S 30.00 male 0 0 8.0500
## 1221 0 S 32.00 male 0 0 8.0500
## 1223 0 C 33.00 male 0 0 8.6625
## 1224 1 S 23.00 female 0 0 7.5500
## 1225 0 S 21.00 male 0 0 8.0500
## 1227 0 S 19.00 male 0 0 7.8958
## 1228 0 S 22.00 female 0 0 9.8375
## 1229 1 S 31.00 male 0 0 7.9250
## 1230 0 S 27.00 male 0 0 8.6625
## 1231 0 S 2.00 female 0 1 10.4625
## 1232 0 S 29.00 female 1 1 10.4625
## 1233 1 S 16.00 male 0 0 8.0500
## 1234 1 S 44.00 male 0 0 7.9250
## 1235 0 S 25.00 male 0 0 7.0500
## 1236 0 S 74.00 male 0 0 7.7750
## 1237 1 S 14.00 male 0 0 9.2250
## 1238 0 S 24.00 male 0 0 7.7958
## 1239 1 S 25.00 male 0 0 7.7958
## 1240 0 S 34.00 male 0 0 8.0500
## 1241 1 C 0.42 male 0 1 8.5167
## 1245 1 C 16.00 female 1 1 8.5167
## 1249 0 S 32.00 male 0 0 7.9250
## 1252 0 S 30.50 male 0 0 8.0500
## 1253 0 S 44.00 male 0 0 8.0500
## 1255 1 S 25.00 male 0 0 0.0000
## 1257 1 C 7.00 male 1 1 15.2458
## 1258 1 C 9.00 female 1 1 15.2458
## 1259 1 C 29.00 female 0 2 15.2458
## 1260 0 S 36.00 male 0 0 7.8958
## 1261 1 S 18.00 female 0 0 9.8417
## 1262 1 S 63.00 female 0 0 9.5875
## 1264 0 S 11.50 male 1 1 14.5000
## 1265 0 S 40.50 male 0 2 14.5000
## 1266 0 S 10.00 female 0 2 24.1500
## 1267 0 S 36.00 male 1 1 24.1500
## 1268 0 S 30.00 female 1 1 24.1500
## 1270 0 S 33.00 male 0 0 9.5000
## 1271 0 S 28.00 male 0 0 9.5000
## 1272 0 S 28.00 male 0 0 9.5000
## 1273 0 S 47.00 male 0 0 9.0000
## 1274 0 S 18.00 female 2 0 18.0000
## 1275 0 S 31.00 male 3 0 18.0000
## 1276 0 S 16.00 male 2 0 18.0000
## 1277 0 S 31.00 female 1 0 18.0000
## 1278 1 C 22.00 male 0 0 7.2250
## 1279 0 S 20.00 male 0 0 7.8542
## 1280 0 S 14.00 female 0 0 7.8542
## 1281 0 S 22.00 male 0 0 7.8958
## 1282 0 S 22.00 male 0 0 9.0000
## 1286 0 S 32.50 male 0 0 9.5000
## 1287 1 C 38.00 female 0 0 7.2292
## 1288 0 S 51.00 male 0 0 7.7500
## 1289 0 S 18.00 male 1 0 6.4958
## 1290 0 S 21.00 male 1 0 6.4958
## 1291 1 S 47.00 female 1 0 7.0000
## 1295 0 S 28.50 male 0 0 16.1000
## 1296 0 S 21.00 male 0 0 7.2500
## 1297 0 S 27.00 male 0 0 8.6625
## 1299 0 S 36.00 male 0 0 9.5000
## 1300 0 C 27.00 male 1 0 14.4542
## 1301 1 C 15.00 female 1 0 14.4542
## 1302 0 C 45.50 male 0 0 7.2250
## 1305 0 C 14.50 female 1 0 14.4542
## 1307 0 C 26.50 male 0 0 7.2250
## 1308 0 C 27.00 male 0 0 7.2250
## 1309 0 S 29.00 male 0 0 7.8750str(titanic_noNA)## 'data.frame': 1043 obs. of 7 variables:
## $ T5.survived: num 1 1 0 0 0 1 1 0 1 0 ...
## $ T5.embarked: chr "S" "S" "S" "S" ...
## $ T5.age : num 29 0.92 2 30 25 48 63 39 53 71 ...
## $ T5.sex : chr "female" "male" "female" "male" ...
## $ T5.sibsp : num 0 1 1 1 1 0 1 0 2 0 ...
## $ T5.parch : num 0 2 2 2 2 0 0 0 0 0 ...
## $ T5.fare : num 211 152 152 152 152 ...
## - attr(*, "na.action")= 'omit' Named int [1:266] 16 38 41 47 60 70 71 75 81 107 ...
## ..- attr(*, "names")= chr [1:266] "16" "38" "41" "47" ...#7) Make Survived embarked and sex as factors
titanic_noNA$T5.survived <- as.integer (factor(titanic_noNA$T5.survived))
titanic_noNA$T5.embarked <- as.integer (factor(titanic_noNA$T5.embarked))
titanic_noNA$T5.sex <- as.integer (factor(titanic_noNA$T5.sex))
str(titanic_noNA)## 'data.frame': 1043 obs. of 7 variables:
## $ T5.survived: int 2 2 1 1 1 2 2 1 2 1 ...
## $ T5.embarked: int 3 3 3 3 3 3 3 3 3 1 ...
## $ T5.age : num 29 0.92 2 30 25 48 63 39 53 71 ...
## $ T5.sex : int 1 2 1 2 1 2 1 2 1 2 ...
## $ T5.sibsp : num 0 1 1 1 1 0 1 0 2 0 ...
## $ T5.parch : num 0 2 2 2 2 0 0 0 0 0 ...
## $ T5.fare : num 211 152 152 152 152 ...
## - attr(*, "na.action")= 'omit' Named int [1:266] 16 38 41 47 60 70 71 75 81 107 ...
## ..- attr(*, "names")= chr [1:266] "16" "38" "41" "47" ...#8) Find the correlation matrix between survival and the other features
View(titanic_noNA)
cor(x=titanic_noNA[-1], y=titanic_noNA$T5.survived)## [,1]
## T5.embarked -0.20225751
## T5.age -0.05741486
## T5.sex -0.53633212
## T5.sibsp -0.01140343
## T5.parch 0.11543601
## T5.fare 0.24785762cor(x=titanic_noNA$T5.survived, y= titanic_noNA[-1])## T5.embarked T5.age T5.sex T5.sibsp T5.parch T5.fare
## [1,] -0.2022575 -0.05741486 -0.5363321 -0.01140343 0.115436 0.2478576titanic_noNA.cor = cor(titanic_noNA)
corrplot(titanic_noNA.cor)
corrplot(titanic_noNA.cor, method = 'number')
corrplot(titanic_noNA.cor, order = 'hclust', addrect = 3)
#9) Plot survival with other features to see if any correlation exists
ggplot(titanic_noNA, aes(x=T5.survived, y=T5.fare))+geom_point()
ggplot(titanic_noNA, aes(x=T5.survived, y=T5.sex))+geom_point()
ggplot(data=titanic_noNA, mapping=aes(x=T5.survived,y=T5.fare)) + geom_point(mapping = aes(color=T5.sex)) 
ggplot(titanic_noNA, aes(x=T5.survived, y=T5.sex, color = "T5.sex"))+ geom_smooth(method = "loess") ## `geom_smooth()` using formula = 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.995## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 1.005## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 2.3403e-29## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 1.01## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.995## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 1.005## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 2.3403e-29## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 1.01
ggplot(titanic_noNA, aes(x=T5.survived, y=T5.fare,color = "T5.fare"))+geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.995## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 1.005## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 2.3403e-29## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 1.01## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.995## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 1.005## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 2.3403e-29## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 1.01
ggplot(titanic_noNA, aes(x=T5.survived, y=T5.age,color = "T5.age"))+geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.995## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 1.005## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 2.3403e-29## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 1.01## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.995## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 1.005## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 2.3403e-29## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 1.01
ggplot(titanic_noNA, aes(x=T5.survived, y=T5.sibsp,color = "T5.sibsp"))+geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.995## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 1.005## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 2.3403e-29## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 1.01## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.995## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 1.005## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 2.3403e-29## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 1.01
##Using ggpairs
ggpairs(titanic_noNA)
ggpairs(titanic,columns = 1:7,aes(color =T5.sex , alpha = 0.5))## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 263 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removing 1 row that contained a missing value## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 2 rows containing missing values (`stat_boxplot()`).## Warning: Removed 263 rows containing non-finite values (`stat_boxplot()`).## Warning: Removed 2 rows containing non-finite values (`stat_g_gally_count()`).## Warning: Removed 2 rows containing missing values (`stat_boxplot()`).
## Removed 2 rows containing missing values (`stat_boxplot()`).
## Removed 2 rows containing missing values (`stat_boxplot()`).## Warning: Removed 1 rows containing non-finite values (`stat_boxplot()`).## Warning: Removed 263 rows containing missing values (`geom_point()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 263 rows containing non-finite values (`stat_bin()`).## Warning: Removed 263 rows containing non-finite values (`stat_density()`).## Warning: Removed 263 rows containing non-finite values (`stat_boxplot()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 263 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 263 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 264 rows containing missing values## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 263 rows containing non-finite values (`stat_bin()`).## Warning: Removed 1 rows containing non-finite values (`stat_boxplot()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 263 rows containing missing values (`geom_point()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removing 1 row that contained a missing value## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 263 rows containing missing values (`geom_point()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removing 1 row that contained a missing value## Warning: Removed 1 rows containing missing values (`geom_point()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1 rows containing non-finite values (`stat_bin()`).## Warning: Removed 264 rows containing missing values (`geom_point()`).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1 rows containing non-finite values (`stat_bin()`).## Warning: Removed 1 rows containing missing values (`geom_point()`).
## Removed 1 rows containing missing values (`geom_point()`).## Warning: Removed 1 rows containing non-finite values (`stat_density()`).
#####10 and 11) Spliting dataset to training and test in ration 80:20
set.seed(1000)
sample <- sample.split(titanic_noNA$T5.survived, SplitRatio = 0.8)
training_dataset2 <- subset(titanic_noNA, sample == TRUE)
count(training_dataset2) ###834 ## n
## 1 834testing_dataset2 <- subset(titanic_noNA, sample == FALSE)
count (testing_dataset2) ### 209## n
## 1 209#####12 and 13) Using RPART
fit <- rpart(T5.survived ~ T5.sex + T5.age + T5.sibsp + T5.parch + T5.fare + T5.embarked, data=training_dataset2, method="class")
fit## n= 834
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 834 340 1 (0.59232614 0.40767386)
## 2) T5.sex>=1.5 521 108 1 (0.79270633 0.20729367)
## 4) T5.age>=9.5 489 90 1 (0.81595092 0.18404908) *
## 5) T5.age< 9.5 32 14 2 (0.43750000 0.56250000)
## 10) T5.sibsp>=2.5 12 1 1 (0.91666667 0.08333333) *
## 11) T5.sibsp< 2.5 20 3 2 (0.15000000 0.85000000) *
## 3) T5.sex< 1.5 313 81 2 (0.25878594 0.74121406)
## 6) T5.fare< 48.2 219 78 2 (0.35616438 0.64383562)
## 12) T5.fare< 10.48125 58 26 1 (0.55172414 0.44827586)
## 24) T5.fare>=7.72915 43 14 1 (0.67441860 0.32558140) *
## 25) T5.fare< 7.72915 15 3 2 (0.20000000 0.80000000) *
## 13) T5.fare>=10.48125 161 46 2 (0.28571429 0.71428571)
## 26) T5.sibsp>=2.5 9 1 1 (0.88888889 0.11111111) *
## 27) T5.sibsp< 2.5 152 38 2 (0.25000000 0.75000000)
## 54) T5.parch>=3.5 7 1 1 (0.85714286 0.14285714) *
## 55) T5.parch< 3.5 145 32 2 (0.22068966 0.77931034) *
## 7) T5.fare>=48.2 94 3 2 (0.03191489 0.96808511) *14, 15 and 16) Ploting and saving the fit treee
png("C:/GGTUAN/DREAMS/Yankee/TSU/MSc_TSU/Spring_2024/CS-583 Data Minning/rpart_tree.png")
rpart.plot(fit, extra = 106)
png("C:/GGTUAN/DREAMS/Yankee/TSU/MSc_TSU/Spring_2024/CS-583 Data Minning/fancy_rpart_tree.png")
fancyRpartPlot(fit, caption=NULL)
dev.off() ## png
## 217) Examine the tree you obtained. What is the most “important” feature over which the tree first split? Do you agree with the Titanic slogan “ Women and children first!”
Yes very obvious Sex and age were predominant factors
fit$variable.importance## T5.sex T5.fare T5.parch T5.sibsp T5.age T5.embarked
## 111.480529 36.749245 19.300397 18.165960 12.420656 3.247549names(fit)## [1] "frame" "where" "call"
## [4] "terms" "cptable" "method"
## [7] "parms" "control" "functions"
## [10] "numresp" "splits" "variable.importance"
## [13] "y" "ordered"printcp(fit)##
## Classification tree:
## rpart(formula = T5.survived ~ T5.sex + T5.age + T5.sibsp + T5.parch +
## T5.fare + T5.embarked, data = training_dataset2, method = "class")
##
## Variables actually used in tree construction:
## [1] T5.age T5.fare T5.parch T5.sex T5.sibsp
##
## Root node error: 340/834 = 0.40767
##
## n= 834
##
## CP nsplit rel error xerror xstd
## 1 0.444118 0 1.00000 1.00000 0.041739
## 2 0.020588 1 0.55588 0.55588 0.035559
## 3 0.014706 3 0.51471 0.55588 0.035559
## 4 0.010000 8 0.43529 0.54412 0.035290###18) Use the predict function with your model fit to make predictions on the test dataset and save it in a variable Prediction
Prediction <- predict(fit, testing_dataset2, type = "class")
Prediction## 10 23 33 40 44 54 55 61 66 73 82 94 100 103 105 116
## 1 1 2 1 2 1 1 1 2 2 1 1 2 2 2 1
## 120 132 145 146 151 173 178 184 190 194 198 213 214 215 217 239
## 1 2 2 1 1 1 1 1 1 2 1 1 2 2 2 2
## 245 246 260 264 267 273 277 281 282 286 290 293 295 296 297 301
## 1 2 1 2 1 2 1 1 2 1 2 1 1 1 2 1
## 302 307 312 317 320 323 324 325 330 335 336 346 348 355 359 362
## 1 1 2 1 2 2 1 2 2 1 1 1 1 1 2 2
## 381 384 387 392 403 405 407 410 415 416 417 423 425 428 441 442
## 2 1 1 1 2 1 1 1 1 2 1 1 1 2 2 2
## 445 451 464 468 473 476 480 483 491 495 498 500 502 516 517 528
## 1 1 1 2 1 2 2 2 2 2 1 1 2 2 1 1
## 531 544 545 546 550 556 557 559 568 569 579 581 584 588 593 597
## 1 1 1 1 2 1 1 2 1 1 1 2 2 2 1 1
## 603 604 607 610 619 624 628 630 641 644 646 655 656 666 670 678
## 1 2 1 1 1 1 1 1 1 1 1 1 1 2 1 1
## 680 685 688 695 700 703 704 708 710 711 715 716 725 726 728 733
## 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 734 737 738 743 749 751 760 766 767 773 775 785 792 815 822 831
## 2 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1
## 863 868 882 892 896 897 906 911 914 918 920 940 968 975 981 987
## 1 2 1 1 2 1 1 1 1 1 1 2 1 1 1 1
## 1008 1022 1025 1059 1061 1062 1067 1068 1069 1083 1090 1095 1097 1104 1106 1118
## 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1
## 1145 1148 1158 1161 1183 1208 1223 1230 1257 1258 1272 1276 1277 1279 1290 1296
## 1 2 1 1 2 1 1 1 2 2 1 1 2 1 1 1
## 1297
## 1
## Levels: 1 2###19) Save the results in a data frame Results that will have two columns 1: PassengerSex =test$Sex, and 2: Survived = Prediction
PassengerSex <- testing_dataset2$T5.survived
PassengerSex## [1] 1 2 2 1 2 1 2 1 2 2 1 2 2 2 2 1 2 2 2 2 1 1 2 2 1 2 1 1 2 2 2 2 1 2 2 2 1
## [38] 2 1 2 2 1 2 2 1 2 2 1 1 1 2 1 2 2 1 2 2 1 1 1 1 1 2 2 2 1 1 1 2 1 1 1 1 2
## [75] 1 1 1 2 2 2 1 1 1 2 1 1 2 2 1 2 1 1 2 2 1 1 1 1 1 2 2 1 1 2 1 1 1 2 2 2 1
## [112] 2 1 2 2 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 1 1 2 1 1 2 1 1 2
## [149] 1 1 2 2 1 1 1 1 1 1 1 1 2 2 2 1 2 1 2 1 2 2 1 1 1 1 2 1 2 1 1 1 2 2 1 2 1
## [186] 2 1 2 1 1 1 1 1 1 1 1 2 1 1 1 2 2 1 1 1 1 1 1 1Survived <- Prediction
Survived## 10 23 33 40 44 54 55 61 66 73 82 94 100 103 105 116
## 1 1 2 1 2 1 1 1 2 2 1 1 2 2 2 1
## 120 132 145 146 151 173 178 184 190 194 198 213 214 215 217 239
## 1 2 2 1 1 1 1 1 1 2 1 1 2 2 2 2
## 245 246 260 264 267 273 277 281 282 286 290 293 295 296 297 301
## 1 2 1 2 1 2 1 1 2 1 2 1 1 1 2 1
## 302 307 312 317 320 323 324 325 330 335 336 346 348 355 359 362
## 1 1 2 1 2 2 1 2 2 1 1 1 1 1 2 2
## 381 384 387 392 403 405 407 410 415 416 417 423 425 428 441 442
## 2 1 1 1 2 1 1 1 1 2 1 1 1 2 2 2
## 445 451 464 468 473 476 480 483 491 495 498 500 502 516 517 528
## 1 1 1 2 1 2 2 2 2 2 1 1 2 2 1 1
## 531 544 545 546 550 556 557 559 568 569 579 581 584 588 593 597
## 1 1 1 1 2 1 1 2 1 1 1 2 2 2 1 1
## 603 604 607 610 619 624 628 630 641 644 646 655 656 666 670 678
## 1 2 1 1 1 1 1 1 1 1 1 1 1 2 1 1
## 680 685 688 695 700 703 704 708 710 711 715 716 725 726 728 733
## 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 734 737 738 743 749 751 760 766 767 773 775 785 792 815 822 831
## 2 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1
## 863 868 882 892 896 897 906 911 914 918 920 940 968 975 981 987
## 1 2 1 1 2 1 1 1 1 1 1 2 1 1 1 1
## 1008 1022 1025 1059 1061 1062 1067 1068 1069 1083 1090 1095 1097 1104 1106 1118
## 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1
## 1145 1148 1158 1161 1183 1208 1223 1230 1257 1258 1272 1276 1277 1279 1290 1296
## 1 2 1 1 2 1 1 1 2 2 1 1 2 1 1 1
## 1297
## 1
## Levels: 1 2test_df <- data.frame(PassengerSex, Survived)
test_df## PassengerSex Survived
## 10 1 1
## 23 2 1
## 33 2 2
## 40 1 1
## 44 2 2
## 54 1 1
## 55 2 1
## 61 1 1
## 66 2 2
## 73 2 2
## 82 1 1
## 94 2 1
## 100 2 2
## 103 2 2
## 105 2 2
## 116 1 1
## 120 2 1
## 132 2 2
## 145 2 2
## 146 2 1
## 151 1 1
## 173 1 1
## 178 2 1
## 184 2 1
## 190 1 1
## 194 2 2
## 198 1 1
## 213 1 1
## 214 2 2
## 215 2 2
## 217 2 2
## 239 2 2
## 245 1 1
## 246 2 2
## 260 2 1
## 264 2 2
## 267 1 1
## 273 2 2
## 277 1 1
## 281 2 1
## 282 2 2
## 286 1 1
## 290 2 2
## 293 2 1
## 295 1 1
## 296 2 1
## 297 2 2
## 301 1 1
## 302 1 1
## 307 1 1
## 312 2 2
## 317 1 1
## 320 2 2
## 323 2 2
## 324 1 1
## 325 2 2
## 330 2 2
## 335 1 1
## 336 1 1
## 346 1 1
## 348 1 1
## 355 1 1
## 359 2 2
## 362 2 2
## 381 2 2
## 384 1 1
## 387 1 1
## 392 1 1
## 403 2 2
## 405 1 1
## 407 1 1
## 410 1 1
## 415 1 1
## 416 2 2
## 417 1 1
## 423 1 1
## 425 1 1
## 428 2 2
## 441 2 2
## 442 2 2
## 445 1 1
## 451 1 1
## 464 1 1
## 468 2 2
## 473 1 1
## 476 1 2
## 480 2 2
## 483 2 2
## 491 1 2
## 495 2 2
## 498 1 1
## 500 1 1
## 502 2 2
## 516 2 2
## 517 1 1
## 528 1 1
## 531 1 1
## 544 1 1
## 545 1 1
## 546 2 1
## 550 2 2
## 556 1 1
## 557 1 1
## 559 2 2
## 568 1 1
## 569 1 1
## 579 1 1
## 581 2 2
## 584 2 2
## 588 2 2
## 593 1 1
## 597 2 1
## 603 1 1
## 604 2 2
## 607 2 1
## 610 1 1
## 619 1 1
## 624 1 1
## 628 1 1
## 630 1 1
## 641 1 1
## 644 2 1
## 646 2 1
## 655 1 1
## 656 1 1
## 666 1 2
## 670 1 1
## 678 1 1
## 680 1 2
## 685 1 2
## 688 1 1
## 695 1 1
## 700 1 1
## 703 1 1
## 704 1 1
## 708 1 1
## 710 2 1
## 711 1 1
## 715 2 1
## 716 1 1
## 725 1 1
## 726 2 1
## 728 1 1
## 733 1 1
## 734 2 2
## 737 1 1
## 738 1 1
## 743 2 1
## 749 1 1
## 751 1 1
## 760 2 2
## 766 2 2
## 767 1 1
## 773 1 1
## 775 1 1
## 785 1 1
## 792 1 1
## 815 1 1
## 822 1 1
## 831 1 1
## 863 2 1
## 868 2 2
## 882 2 1
## 892 1 1
## 896 2 2
## 897 1 1
## 906 2 1
## 911 1 1
## 914 2 1
## 918 2 1
## 920 1 1
## 940 1 2
## 968 1 1
## 975 1 1
## 981 2 1
## 987 1 1
## 1008 2 1
## 1022 1 1
## 1025 1 1
## 1059 1 1
## 1061 2 1
## 1062 2 1
## 1067 1 1
## 1068 2 1
## 1069 1 1
## 1083 2 2
## 1090 1 1
## 1095 2 1
## 1097 1 1
## 1104 1 1
## 1106 1 1
## 1118 1 1
## 1145 1 1
## 1148 1 2
## 1158 1 1
## 1161 1 1
## 1183 2 2
## 1208 1 1
## 1223 1 1
## 1230 1 1
## 1257 2 2
## 1258 2 2
## 1272 1 1
## 1276 1 1
## 1277 1 2
## 1279 1 1
## 1290 1 1
## 1296 1 1
## 1297 1 1####Making sense of the prediction and actual comparison
#Confusion Matric or CrossTable view
Conf_Matrix <- table(testing_dataset2$T5.survived, Prediction)
Conf_Matrix## Prediction
## 1 2
## 1 116 8
## 2 31 54#Percentage Accuracy
accuracy_Test <- sum(diag(Conf_Matrix)) / sum(Conf_Matrix)
accuracy_Test ## [1] 0.8133971###20) Save your data frame in a .csv file by using
write.csv(test_df, file = "Titanicdtree.csv", row.names = FALSE)