INTRODUCCIÓN

El aprendizaje supervisado es una técnica de aprendizaje automático (Machine Learning) que permite construir modelos capaces de realizar predicciones o clasificaciones a partir de datos históricos. Se denomina supervisado porque el algoritmo aprende utilizando ejemplos previamente etiquetados, es decir, observaciones en las que se conoce el resultado esperado.

Su funcionamiento consiste en identificar relaciones entre un conjunto de variables predictoras y una variable objetivo. Una vez el modelo ha aprendido estos patrones durante la etapa de entrenamiento, puede utilizarse para realizar predicciones sobre nuevos datos.

Las principales categorías del aprendizaje supervisado son:

CLASIFICACIÓN

Se utiliza cuando la variable objetivo corresponde a categorías o clases definidas. El propósito es determinar a qué categoría pertenece una observación. Algunos ejemplos son la detección de enfermedades, la clasificación de correos electrónicos como spam o no spam, y la predicción de supervivencia de pasajeros.

REGRESIÓN

Se utiliza cuando la variable objetivo es numérica y continua. Su objetivo es estimar valores como precios, ventas, demanda o consumo a partir de variables explicativas.

Debido a su capacidad para identificar patrones y generar predicciones, el aprendizaje supervisado es ampliamente utilizado en áreas como la medicina, las finanzas, la industria y la investigación.

OBJETIVOS

OBJETIVO GENERAL

  • Conocer el funcionamiento de la Regresión Logística Binaria y los Árboles de Decisión en el aprendizaje supervisado a través de ejemplos prácticos independientes con datos internacionales.

OBJETIVOS ESPECÍFICOS

  • Comprender el funcionamiento de los algoritmos de aprendizaje supervisado.
  • Desarrollar un ejemplo práctico para la Regresión Logística Binaria y mostrar sus resultados.
  • Desarrollar un ejemplo práctico para el Árbol de Decisión e ilustrar su estructura de reglas.
  • Analizar e interpretar los resultados obtenidos con cada modelo.

REGRESIÓN LOGÍSTICA BINARIA

La Regresión Logística Binaria es un algoritmo de aprendizaje supervisado perteneciente a la categoría de clasificación. Se utiliza cuando la variable objetivo presenta únicamente dos posibles resultados, por ejemplo, sí o no, verdadero o falso, aprobado o reprobado.

A diferencia de la Regresión Lineal, que predice valores numéricos, la Regresión Logística estima la probabilidad de que una observación pertenezca a una determinada categoría. Posteriormente, dicha probabilidad se transforma en una clasificación final.

FÓRMULA

La Regresión Logística utiliza la función sigmoide para transformar los resultados en probabilidades comprendidas entre 0 y 1. La probabilidad se calcula mediante la fórmula:

\[P(Y = 1 | X) = \frac{1}{1 + e^{-(\beta_0 + \beta_1 X_1 + \dots + \beta_n X_n)}}\]

Donde:

  • \(P(Y = 1|X)\) representa la probabilidad de que ocurra el evento de interés.
  • \(\beta_0\) corresponde al intercepto del modelo.
  • \(\beta_i\) representan los coeficientes asociados a cada variable predictora.
  • \(X_i\) representan las variables explicativas.

Si la probabilidad obtenida es mayor o igual a un umbral (generalmente 0.5), el registro se clasifica como 1; de lo contrario, se clasifica como 0.

APLICACIONES

La Regresión Logística Binaria es ampliamente utilizada en diferentes áreas debido a su capacidad para resolver problemas de clasificación. Algunas de sus aplicaciones más comunes son:

  • Diagnóstico de enfermedades.
  • Detección de fraude financiero.
  • Clasificación de clientes.
  • Predicción de abandono de usuarios.
  • Evaluación de riesgos.

HIPÓTESIS

Hipótesis nula (H₀)

Las variables predictoras no presentan una relación significativa con la variable objetivo.

Hipótesis alternativa (H₁)

Al menos una de las variables predictoras presenta una relación significativa con la variable objetivo.

CASO DE ESTUDIO: PREDICCIÓN DE DIABETES

Para la aplicación de la Regresión Logística Binaria se utilizará la base de datos Diabetes, la cual contiene información clínica de pacientes y permite determinar si una persona presenta o no diabetes a partir de diferentes características médicas.

La variable objetivo del conjunto de datos es Outcome, donde:

  • 0: Paciente sin diabetes.
  • 1: Paciente con diabetes.

JUSTIFICACIÓN DE LA BASE DE DATOS

Esta base de datos fue seleccionada porque representa un problema de clasificación binaria, característica fundamental para la aplicación de la Regresión Logística. Además, contiene variables clínicas relevantes como glucosa, presión arterial, índice de masa corporal y edad, las cuales pueden influir en la presencia de diabetes.

HIPÓTESIS DEL CASO DE ESTUDIO

Hipótesis nula (H₀)

Las características clínicas de los pacientes no permiten predecir la presencia de diabetes.

Hipótesis alternativa (H₁)

Las características clínicas de los pacientes permiten predecir la presencia de diabetes.

# Cargamos las librerias 
library(knitr)
## Warning: package 'knitr' was built under R version 4.5.3
library(kableExtra)
## Warning: package 'kableExtra' was built under R version 4.5.3
library(caret) # Libreria que permite automatizar y unificar todo el proceso de creacion de modelos predictivos
## Warning: package 'caret' was built under R version 4.5.3
## Cargando paquete requerido: ggplot2
## Warning: package 'ggplot2' was built under R version 4.5.3
## Cargando paquete requerido: lattice
# Cargamos la base de datos

diabetes<- read.csv("C:/Users/aleja/OneDrive/python/Bases de datos/diabetes.csv")

Primero cargamos la base de datos que contiene información clínica de pacientes. Esta información será utilizada para construir el modelo de Regresión Logística Binaria.

# Visualizamos los registros de la base de datos

kable(
  diabetes,
  caption = "Base de datos utilizada para el análisis"
) %>%
  kable_styling(
    bootstrap_options = c("striped","hover"),
    full_width = FALSE
  ) %>%
  scroll_box(
    width = "100%",
    height = "300px"
  )
Base de datos utilizada para el análisis
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome
6 148 72 35 0 33.6 0.627 50 1
1 85 66 29 0 26.6 0.351 31 0
8 183 64 0 0 23.3 0.672 32 1
1 89 66 23 94 28.1 0.167 21 0
0 137 40 35 168 43.1 2.288 33 1
5 116 74 0 0 25.6 0.201 30 0
3 78 50 32 88 31.0 0.248 26 1
10 115 0 0 0 35.3 0.134 29 0
2 197 70 45 543 30.5 0.158 53 1
8 125 96 0 0 0.0 0.232 54 1
4 110 92 0 0 37.6 0.191 30 0
10 168 74 0 0 38.0 0.537 34 1
10 139 80 0 0 27.1 1.441 57 0
1 189 60 23 846 30.1 0.398 59 1
5 166 72 19 175 25.8 0.587 51 1
7 100 0 0 0 30.0 0.484 32 1
0 118 84 47 230 45.8 0.551 31 1
7 107 74 0 0 29.6 0.254 31 1
1 103 30 38 83 43.3 0.183 33 0
1 115 70 30 96 34.6 0.529 32 1
3 126 88 41 235 39.3 0.704 27 0
8 99 84 0 0 35.4 0.388 50 0
7 196 90 0 0 39.8 0.451 41 1
9 119 80 35 0 29.0 0.263 29 1
11 143 94 33 146 36.6 0.254 51 1
10 125 70 26 115 31.1 0.205 41 1
7 147 76 0 0 39.4 0.257 43 1
1 97 66 15 140 23.2 0.487 22 0
13 145 82 19 110 22.2 0.245 57 0
5 117 92 0 0 34.1 0.337 38 0
5 109 75 26 0 36.0 0.546 60 0
3 158 76 36 245 31.6 0.851 28 1
3 88 58 11 54 24.8 0.267 22 0
6 92 92 0 0 19.9 0.188 28 0
10 122 78 31 0 27.6 0.512 45 0
4 103 60 33 192 24.0 0.966 33 0
11 138 76 0 0 33.2 0.420 35 0
9 102 76 37 0 32.9 0.665 46 1
2 90 68 42 0 38.2 0.503 27 1
4 111 72 47 207 37.1 1.390 56 1
3 180 64 25 70 34.0 0.271 26 0
7 133 84 0 0 40.2 0.696 37 0
7 106 92 18 0 22.7 0.235 48 0
9 171 110 24 240 45.4 0.721 54 1
7 159 64 0 0 27.4 0.294 40 0
0 180 66 39 0 42.0 1.893 25 1
1 146 56 0 0 29.7 0.564 29 0
2 71 70 27 0 28.0 0.586 22 0
7 103 66 32 0 39.1 0.344 31 1
7 105 0 0 0 0.0 0.305 24 0
1 103 80 11 82 19.4 0.491 22 0
1 101 50 15 36 24.2 0.526 26 0
5 88 66 21 23 24.4 0.342 30 0
8 176 90 34 300 33.7 0.467 58 1
7 150 66 42 342 34.7 0.718 42 0
1 73 50 10 0 23.0 0.248 21 0
7 187 68 39 304 37.7 0.254 41 1
0 100 88 60 110 46.8 0.962 31 0
0 146 82 0 0 40.5 1.781 44 0
0 105 64 41 142 41.5 0.173 22 0
2 84 0 0 0 0.0 0.304 21 0
8 133 72 0 0 32.9 0.270 39 1
5 44 62 0 0 25.0 0.587 36 0
2 141 58 34 128 25.4 0.699 24 0
7 114 66 0 0 32.8 0.258 42 1
5 99 74 27 0 29.0 0.203 32 0
0 109 88 30 0 32.5 0.855 38 1
2 109 92 0 0 42.7 0.845 54 0
1 95 66 13 38 19.6 0.334 25 0
4 146 85 27 100 28.9 0.189 27 0
2 100 66 20 90 32.9 0.867 28 1
5 139 64 35 140 28.6 0.411 26 0
13 126 90 0 0 43.4 0.583 42 1
4 129 86 20 270 35.1 0.231 23 0
1 79 75 30 0 32.0 0.396 22 0
1 0 48 20 0 24.7 0.140 22 0
7 62 78 0 0 32.6 0.391 41 0
5 95 72 33 0 37.7 0.370 27 0
0 131 0 0 0 43.2 0.270 26 1
2 112 66 22 0 25.0 0.307 24 0
3 113 44 13 0 22.4 0.140 22 0
2 74 0 0 0 0.0 0.102 22 0
7 83 78 26 71 29.3 0.767 36 0
0 101 65 28 0 24.6 0.237 22 0
5 137 108 0 0 48.8 0.227 37 1
2 110 74 29 125 32.4 0.698 27 0
13 106 72 54 0 36.6 0.178 45 0
2 100 68 25 71 38.5 0.324 26 0
15 136 70 32 110 37.1 0.153 43 1
1 107 68 19 0 26.5 0.165 24 0
1 80 55 0 0 19.1 0.258 21 0
4 123 80 15 176 32.0 0.443 34 0
7 81 78 40 48 46.7 0.261 42 0
4 134 72 0 0 23.8 0.277 60 1
2 142 82 18 64 24.7 0.761 21 0
6 144 72 27 228 33.9 0.255 40 0
2 92 62 28 0 31.6 0.130 24 0
1 71 48 18 76 20.4 0.323 22 0
6 93 50 30 64 28.7 0.356 23 0
1 122 90 51 220 49.7 0.325 31 1
1 163 72 0 0 39.0 1.222 33 1
1 151 60 0 0 26.1 0.179 22 0
0 125 96 0 0 22.5 0.262 21 0
1 81 72 18 40 26.6 0.283 24 0
2 85 65 0 0 39.6 0.930 27 0
1 126 56 29 152 28.7 0.801 21 0
1 96 122 0 0 22.4 0.207 27 0
4 144 58 28 140 29.5 0.287 37 0
3 83 58 31 18 34.3 0.336 25 0
0 95 85 25 36 37.4 0.247 24 1
3 171 72 33 135 33.3 0.199 24 1
8 155 62 26 495 34.0 0.543 46 1
1 89 76 34 37 31.2 0.192 23 0
4 76 62 0 0 34.0 0.391 25 0
7 160 54 32 175 30.5 0.588 39 1
4 146 92 0 0 31.2 0.539 61 1
5 124 74 0 0 34.0 0.220 38 1
5 78 48 0 0 33.7 0.654 25 0
4 97 60 23 0 28.2 0.443 22 0
4 99 76 15 51 23.2 0.223 21 0
0 162 76 56 100 53.2 0.759 25 1
6 111 64 39 0 34.2 0.260 24 0
2 107 74 30 100 33.6 0.404 23 0
5 132 80 0 0 26.8 0.186 69 0
0 113 76 0 0 33.3 0.278 23 1
1 88 30 42 99 55.0 0.496 26 1
3 120 70 30 135 42.9 0.452 30 0
1 118 58 36 94 33.3 0.261 23 0
1 117 88 24 145 34.5 0.403 40 1
0 105 84 0 0 27.9 0.741 62 1
4 173 70 14 168 29.7 0.361 33 1
9 122 56 0 0 33.3 1.114 33 1
3 170 64 37 225 34.5 0.356 30 1
8 84 74 31 0 38.3 0.457 39 0
2 96 68 13 49 21.1 0.647 26 0
2 125 60 20 140 33.8 0.088 31 0
0 100 70 26 50 30.8 0.597 21 0
0 93 60 25 92 28.7 0.532 22 0
0 129 80 0 0 31.2 0.703 29 0
5 105 72 29 325 36.9 0.159 28 0
3 128 78 0 0 21.1 0.268 55 0
5 106 82 30 0 39.5 0.286 38 0
2 108 52 26 63 32.5 0.318 22 0
10 108 66 0 0 32.4 0.272 42 1
4 154 62 31 284 32.8 0.237 23 0
0 102 75 23 0 0.0 0.572 21 0
9 57 80 37 0 32.8 0.096 41 0
2 106 64 35 119 30.5 1.400 34 0
5 147 78 0 0 33.7 0.218 65 0
2 90 70 17 0 27.3 0.085 22 0
1 136 74 50 204 37.4 0.399 24 0
4 114 65 0 0 21.9 0.432 37 0
9 156 86 28 155 34.3 1.189 42 1
1 153 82 42 485 40.6 0.687 23 0
8 188 78 0 0 47.9 0.137 43 1
7 152 88 44 0 50.0 0.337 36 1
2 99 52 15 94 24.6 0.637 21 0
1 109 56 21 135 25.2 0.833 23 0
2 88 74 19 53 29.0 0.229 22 0
17 163 72 41 114 40.9 0.817 47 1
4 151 90 38 0 29.7 0.294 36 0
7 102 74 40 105 37.2 0.204 45 0
0 114 80 34 285 44.2 0.167 27 0
2 100 64 23 0 29.7 0.368 21 0
0 131 88 0 0 31.6 0.743 32 1
6 104 74 18 156 29.9 0.722 41 1
3 148 66 25 0 32.5 0.256 22 0
4 120 68 0 0 29.6 0.709 34 0
4 110 66 0 0 31.9 0.471 29 0
3 111 90 12 78 28.4 0.495 29 0
6 102 82 0 0 30.8 0.180 36 1
6 134 70 23 130 35.4 0.542 29 1
2 87 0 23 0 28.9 0.773 25 0
1 79 60 42 48 43.5 0.678 23 0
2 75 64 24 55 29.7 0.370 33 0
8 179 72 42 130 32.7 0.719 36 1
6 85 78 0 0 31.2 0.382 42 0
0 129 110 46 130 67.1 0.319 26 1
5 143 78 0 0 45.0 0.190 47 0
5 130 82 0 0 39.1 0.956 37 1
6 87 80 0 0 23.2 0.084 32 0
0 119 64 18 92 34.9 0.725 23 0
1 0 74 20 23 27.7 0.299 21 0
5 73 60 0 0 26.8 0.268 27 0
4 141 74 0 0 27.6 0.244 40 0
7 194 68 28 0 35.9 0.745 41 1
8 181 68 36 495 30.1 0.615 60 1
1 128 98 41 58 32.0 1.321 33 1
8 109 76 39 114 27.9 0.640 31 1
5 139 80 35 160 31.6 0.361 25 1
3 111 62 0 0 22.6 0.142 21 0
9 123 70 44 94 33.1 0.374 40 0
7 159 66 0 0 30.4 0.383 36 1
11 135 0 0 0 52.3 0.578 40 1
8 85 55 20 0 24.4 0.136 42 0
5 158 84 41 210 39.4 0.395 29 1
1 105 58 0 0 24.3 0.187 21 0
3 107 62 13 48 22.9 0.678 23 1
4 109 64 44 99 34.8 0.905 26 1
4 148 60 27 318 30.9 0.150 29 1
0 113 80 16 0 31.0 0.874 21 0
1 138 82 0 0 40.1 0.236 28 0
0 108 68 20 0 27.3 0.787 32 0
2 99 70 16 44 20.4 0.235 27 0
6 103 72 32 190 37.7 0.324 55 0
5 111 72 28 0 23.9 0.407 27 0
8 196 76 29 280 37.5 0.605 57 1
5 162 104 0 0 37.7 0.151 52 1
1 96 64 27 87 33.2 0.289 21 0
7 184 84 33 0 35.5 0.355 41 1
2 81 60 22 0 27.7 0.290 25 0
0 147 85 54 0 42.8 0.375 24 0
7 179 95 31 0 34.2 0.164 60 0
0 140 65 26 130 42.6 0.431 24 1
9 112 82 32 175 34.2 0.260 36 1
12 151 70 40 271 41.8 0.742 38 1
5 109 62 41 129 35.8 0.514 25 1
6 125 68 30 120 30.0 0.464 32 0
5 85 74 22 0 29.0 1.224 32 1
5 112 66 0 0 37.8 0.261 41 1
0 177 60 29 478 34.6 1.072 21 1
2 158 90 0 0 31.6 0.805 66 1
7 119 0 0 0 25.2 0.209 37 0
7 142 60 33 190 28.8 0.687 61 0
1 100 66 15 56 23.6 0.666 26 0
1 87 78 27 32 34.6 0.101 22 0
0 101 76 0 0 35.7 0.198 26 0
3 162 52 38 0 37.2 0.652 24 1
4 197 70 39 744 36.7 2.329 31 0
0 117 80 31 53 45.2 0.089 24 0
4 142 86 0 0 44.0 0.645 22 1
6 134 80 37 370 46.2 0.238 46 1
1 79 80 25 37 25.4 0.583 22 0
4 122 68 0 0 35.0 0.394 29 0
3 74 68 28 45 29.7 0.293 23 0
4 171 72 0 0 43.6 0.479 26 1
7 181 84 21 192 35.9 0.586 51 1
0 179 90 27 0 44.1 0.686 23 1
9 164 84 21 0 30.8 0.831 32 1
0 104 76 0 0 18.4 0.582 27 0
1 91 64 24 0 29.2 0.192 21 0
4 91 70 32 88 33.1 0.446 22 0
3 139 54 0 0 25.6 0.402 22 1
6 119 50 22 176 27.1 1.318 33 1
2 146 76 35 194 38.2 0.329 29 0
9 184 85 15 0 30.0 1.213 49 1
10 122 68 0 0 31.2 0.258 41 0
0 165 90 33 680 52.3 0.427 23 0
9 124 70 33 402 35.4 0.282 34 0
1 111 86 19 0 30.1 0.143 23 0
9 106 52 0 0 31.2 0.380 42 0
2 129 84 0 0 28.0 0.284 27 0
2 90 80 14 55 24.4 0.249 24 0
0 86 68 32 0 35.8 0.238 25 0
12 92 62 7 258 27.6 0.926 44 1
1 113 64 35 0 33.6 0.543 21 1
3 111 56 39 0 30.1 0.557 30 0
2 114 68 22 0 28.7 0.092 25 0
1 193 50 16 375 25.9 0.655 24 0
11 155 76 28 150 33.3 1.353 51 1
3 191 68 15 130 30.9 0.299 34 0
3 141 0 0 0 30.0 0.761 27 1
4 95 70 32 0 32.1 0.612 24 0
3 142 80 15 0 32.4 0.200 63 0
4 123 62 0 0 32.0 0.226 35 1
5 96 74 18 67 33.6 0.997 43 0
0 138 0 0 0 36.3 0.933 25 1
2 128 64 42 0 40.0 1.101 24 0
0 102 52 0 0 25.1 0.078 21 0
2 146 0 0 0 27.5 0.240 28 1
10 101 86 37 0 45.6 1.136 38 1
2 108 62 32 56 25.2 0.128 21 0
3 122 78 0 0 23.0 0.254 40 0
1 71 78 50 45 33.2 0.422 21 0
13 106 70 0 0 34.2 0.251 52 0
2 100 70 52 57 40.5 0.677 25 0
7 106 60 24 0 26.5 0.296 29 1
0 104 64 23 116 27.8 0.454 23 0
5 114 74 0 0 24.9 0.744 57 0
2 108 62 10 278 25.3 0.881 22 0
0 146 70 0 0 37.9 0.334 28 1
10 129 76 28 122 35.9 0.280 39 0
7 133 88 15 155 32.4 0.262 37 0
7 161 86 0 0 30.4 0.165 47 1
2 108 80 0 0 27.0 0.259 52 1
7 136 74 26 135 26.0 0.647 51 0
5 155 84 44 545 38.7 0.619 34 0
1 119 86 39 220 45.6 0.808 29 1
4 96 56 17 49 20.8 0.340 26 0
5 108 72 43 75 36.1 0.263 33 0
0 78 88 29 40 36.9 0.434 21 0
0 107 62 30 74 36.6 0.757 25 1
2 128 78 37 182 43.3 1.224 31 1
1 128 48 45 194 40.5 0.613 24 1
0 161 50 0 0 21.9 0.254 65 0
6 151 62 31 120 35.5 0.692 28 0
2 146 70 38 360 28.0 0.337 29 1
0 126 84 29 215 30.7 0.520 24 0
14 100 78 25 184 36.6 0.412 46 1
8 112 72 0 0 23.6 0.840 58 0
0 167 0 0 0 32.3 0.839 30 1
2 144 58 33 135 31.6 0.422 25 1
5 77 82 41 42 35.8 0.156 35 0
5 115 98 0 0 52.9 0.209 28 1
3 150 76 0 0 21.0 0.207 37 0
2 120 76 37 105 39.7 0.215 29 0
10 161 68 23 132 25.5 0.326 47 1
0 137 68 14 148 24.8 0.143 21 0
0 128 68 19 180 30.5 1.391 25 1
2 124 68 28 205 32.9 0.875 30 1
6 80 66 30 0 26.2 0.313 41 0
0 106 70 37 148 39.4 0.605 22 0
2 155 74 17 96 26.6 0.433 27 1
3 113 50 10 85 29.5 0.626 25 0
7 109 80 31 0 35.9 1.127 43 1
2 112 68 22 94 34.1 0.315 26 0
3 99 80 11 64 19.3 0.284 30 0
3 182 74 0 0 30.5 0.345 29 1
3 115 66 39 140 38.1 0.150 28 0
6 194 78 0 0 23.5 0.129 59 1
4 129 60 12 231 27.5 0.527 31 0
3 112 74 30 0 31.6 0.197 25 1
0 124 70 20 0 27.4 0.254 36 1
13 152 90 33 29 26.8 0.731 43 1
2 112 75 32 0 35.7 0.148 21 0
1 157 72 21 168 25.6 0.123 24 0
1 122 64 32 156 35.1 0.692 30 1
10 179 70 0 0 35.1 0.200 37 0
2 102 86 36 120 45.5 0.127 23 1
6 105 70 32 68 30.8 0.122 37 0
8 118 72 19 0 23.1 1.476 46 0
2 87 58 16 52 32.7 0.166 25 0
1 180 0 0 0 43.3 0.282 41 1
12 106 80 0 0 23.6 0.137 44 0
1 95 60 18 58 23.9 0.260 22 0
0 165 76 43 255 47.9 0.259 26 0
0 117 0 0 0 33.8 0.932 44 0
5 115 76 0 0 31.2 0.343 44 1
9 152 78 34 171 34.2 0.893 33 1
7 178 84 0 0 39.9 0.331 41 1
1 130 70 13 105 25.9 0.472 22 0
1 95 74 21 73 25.9 0.673 36 0
1 0 68 35 0 32.0 0.389 22 0
5 122 86 0 0 34.7 0.290 33 0
8 95 72 0 0 36.8 0.485 57 0
8 126 88 36 108 38.5 0.349 49 0
1 139 46 19 83 28.7 0.654 22 0
3 116 0 0 0 23.5 0.187 23 0
3 99 62 19 74 21.8 0.279 26 0
5 0 80 32 0 41.0 0.346 37 1
4 92 80 0 0 42.2 0.237 29 0
4 137 84 0 0 31.2 0.252 30 0
3 61 82 28 0 34.4 0.243 46 0
1 90 62 12 43 27.2 0.580 24 0
3 90 78 0 0 42.7 0.559 21 0
9 165 88 0 0 30.4 0.302 49 1
1 125 50 40 167 33.3 0.962 28 1
13 129 0 30 0 39.9 0.569 44 1
12 88 74 40 54 35.3 0.378 48 0
1 196 76 36 249 36.5 0.875 29 1
5 189 64 33 325 31.2 0.583 29 1
5 158 70 0 0 29.8 0.207 63 0
5 103 108 37 0 39.2 0.305 65 0
4 146 78 0 0 38.5 0.520 67 1
4 147 74 25 293 34.9 0.385 30 0
5 99 54 28 83 34.0 0.499 30 0
6 124 72 0 0 27.6 0.368 29 1
0 101 64 17 0 21.0 0.252 21 0
3 81 86 16 66 27.5 0.306 22 0
1 133 102 28 140 32.8 0.234 45 1
3 173 82 48 465 38.4 2.137 25 1
0 118 64 23 89 0.0 1.731 21 0
0 84 64 22 66 35.8 0.545 21 0
2 105 58 40 94 34.9 0.225 25 0
2 122 52 43 158 36.2 0.816 28 0
12 140 82 43 325 39.2 0.528 58 1
0 98 82 15 84 25.2 0.299 22 0
1 87 60 37 75 37.2 0.509 22 0
4 156 75 0 0 48.3 0.238 32 1
0 93 100 39 72 43.4 1.021 35 0
1 107 72 30 82 30.8 0.821 24 0
0 105 68 22 0 20.0 0.236 22 0
1 109 60 8 182 25.4 0.947 21 0
1 90 62 18 59 25.1 1.268 25 0
1 125 70 24 110 24.3 0.221 25 0
1 119 54 13 50 22.3 0.205 24 0
5 116 74 29 0 32.3 0.660 35 1
8 105 100 36 0 43.3 0.239 45 1
5 144 82 26 285 32.0 0.452 58 1
3 100 68 23 81 31.6 0.949 28 0
1 100 66 29 196 32.0 0.444 42 0
5 166 76 0 0 45.7 0.340 27 1
1 131 64 14 415 23.7 0.389 21 0
4 116 72 12 87 22.1 0.463 37 0
4 158 78 0 0 32.9 0.803 31 1
2 127 58 24 275 27.7 1.600 25 0
3 96 56 34 115 24.7 0.944 39 0
0 131 66 40 0 34.3 0.196 22 1
3 82 70 0 0 21.1 0.389 25 0
3 193 70 31 0 34.9 0.241 25 1
4 95 64 0 0 32.0 0.161 31 1
6 137 61 0 0 24.2 0.151 55 0
5 136 84 41 88 35.0 0.286 35 1
9 72 78 25 0 31.6 0.280 38 0
5 168 64 0 0 32.9 0.135 41 1
2 123 48 32 165 42.1 0.520 26 0
4 115 72 0 0 28.9 0.376 46 1
0 101 62 0 0 21.9 0.336 25 0
8 197 74 0 0 25.9 1.191 39 1
1 172 68 49 579 42.4 0.702 28 1
6 102 90 39 0 35.7 0.674 28 0
1 112 72 30 176 34.4 0.528 25 0
1 143 84 23 310 42.4 1.076 22 0
1 143 74 22 61 26.2 0.256 21 0
0 138 60 35 167 34.6 0.534 21 1
3 173 84 33 474 35.7 0.258 22 1
1 97 68 21 0 27.2 1.095 22 0
4 144 82 32 0 38.5 0.554 37 1
1 83 68 0 0 18.2 0.624 27 0
3 129 64 29 115 26.4 0.219 28 1
1 119 88 41 170 45.3 0.507 26 0
2 94 68 18 76 26.0 0.561 21 0
0 102 64 46 78 40.6 0.496 21 0
2 115 64 22 0 30.8 0.421 21 0
8 151 78 32 210 42.9 0.516 36 1
4 184 78 39 277 37.0 0.264 31 1
0 94 0 0 0 0.0 0.256 25 0
1 181 64 30 180 34.1 0.328 38 1
0 135 94 46 145 40.6 0.284 26 0
1 95 82 25 180 35.0 0.233 43 1
2 99 0 0 0 22.2 0.108 23 0
3 89 74 16 85 30.4 0.551 38 0
1 80 74 11 60 30.0 0.527 22 0
2 139 75 0 0 25.6 0.167 29 0
1 90 68 8 0 24.5 1.138 36 0
0 141 0 0 0 42.4 0.205 29 1
12 140 85 33 0 37.4 0.244 41 0
5 147 75 0 0 29.9 0.434 28 0
1 97 70 15 0 18.2 0.147 21 0
6 107 88 0 0 36.8 0.727 31 0
0 189 104 25 0 34.3 0.435 41 1
2 83 66 23 50 32.2 0.497 22 0
4 117 64 27 120 33.2 0.230 24 0
8 108 70 0 0 30.5 0.955 33 1
4 117 62 12 0 29.7 0.380 30 1
0 180 78 63 14 59.4 2.420 25 1
1 100 72 12 70 25.3 0.658 28 0
0 95 80 45 92 36.5 0.330 26 0
0 104 64 37 64 33.6 0.510 22 1
0 120 74 18 63 30.5 0.285 26 0
1 82 64 13 95 21.2 0.415 23 0
2 134 70 0 0 28.9 0.542 23 1
0 91 68 32 210 39.9 0.381 25 0
2 119 0 0 0 19.6 0.832 72 0
2 100 54 28 105 37.8 0.498 24 0
14 175 62 30 0 33.6 0.212 38 1
1 135 54 0 0 26.7 0.687 62 0
5 86 68 28 71 30.2 0.364 24 0
10 148 84 48 237 37.6 1.001 51 1
9 134 74 33 60 25.9 0.460 81 0
9 120 72 22 56 20.8 0.733 48 0
1 71 62 0 0 21.8 0.416 26 0
8 74 70 40 49 35.3 0.705 39 0
5 88 78 30 0 27.6 0.258 37 0
10 115 98 0 0 24.0 1.022 34 0
0 124 56 13 105 21.8 0.452 21 0
0 74 52 10 36 27.8 0.269 22 0
0 97 64 36 100 36.8 0.600 25 0
8 120 0 0 0 30.0 0.183 38 1
6 154 78 41 140 46.1 0.571 27 0
1 144 82 40 0 41.3 0.607 28 0
0 137 70 38 0 33.2 0.170 22 0
0 119 66 27 0 38.8 0.259 22 0
7 136 90 0 0 29.9 0.210 50 0
4 114 64 0 0 28.9 0.126 24 0
0 137 84 27 0 27.3 0.231 59 0
2 105 80 45 191 33.7 0.711 29 1
7 114 76 17 110 23.8 0.466 31 0
8 126 74 38 75 25.9 0.162 39 0
4 132 86 31 0 28.0 0.419 63 0
3 158 70 30 328 35.5 0.344 35 1
0 123 88 37 0 35.2 0.197 29 0
4 85 58 22 49 27.8 0.306 28 0
0 84 82 31 125 38.2 0.233 23 0
0 145 0 0 0 44.2 0.630 31 1
0 135 68 42 250 42.3 0.365 24 1
1 139 62 41 480 40.7 0.536 21 0
0 173 78 32 265 46.5 1.159 58 0
4 99 72 17 0 25.6 0.294 28 0
8 194 80 0 0 26.1 0.551 67 0
2 83 65 28 66 36.8 0.629 24 0
2 89 90 30 0 33.5 0.292 42 0
4 99 68 38 0 32.8 0.145 33 0
4 125 70 18 122 28.9 1.144 45 1
3 80 0 0 0 0.0 0.174 22 0
6 166 74 0 0 26.6 0.304 66 0
5 110 68 0 0 26.0 0.292 30 0
2 81 72 15 76 30.1 0.547 25 0
7 195 70 33 145 25.1 0.163 55 1
6 154 74 32 193 29.3 0.839 39 0
2 117 90 19 71 25.2 0.313 21 0
3 84 72 32 0 37.2 0.267 28 0
6 0 68 41 0 39.0 0.727 41 1
7 94 64 25 79 33.3 0.738 41 0
3 96 78 39 0 37.3 0.238 40 0
10 75 82 0 0 33.3 0.263 38 0
0 180 90 26 90 36.5 0.314 35 1
1 130 60 23 170 28.6 0.692 21 0
2 84 50 23 76 30.4 0.968 21 0
8 120 78 0 0 25.0 0.409 64 0
12 84 72 31 0 29.7 0.297 46 1
0 139 62 17 210 22.1 0.207 21 0
9 91 68 0 0 24.2 0.200 58 0
2 91 62 0 0 27.3 0.525 22 0
3 99 54 19 86 25.6 0.154 24 0
3 163 70 18 105 31.6 0.268 28 1
9 145 88 34 165 30.3 0.771 53 1
7 125 86 0 0 37.6 0.304 51 0
13 76 60 0 0 32.8 0.180 41 0
6 129 90 7 326 19.6 0.582 60 0
2 68 70 32 66 25.0 0.187 25 0
3 124 80 33 130 33.2 0.305 26 0
6 114 0 0 0 0.0 0.189 26 0
9 130 70 0 0 34.2 0.652 45 1
3 125 58 0 0 31.6 0.151 24 0
3 87 60 18 0 21.8 0.444 21 0
1 97 64 19 82 18.2 0.299 21 0
3 116 74 15 105 26.3 0.107 24 0
0 117 66 31 188 30.8 0.493 22 0
0 111 65 0 0 24.6 0.660 31 0
2 122 60 18 106 29.8 0.717 22 0
0 107 76 0 0 45.3 0.686 24 0
1 86 66 52 65 41.3 0.917 29 0
6 91 0 0 0 29.8 0.501 31 0
1 77 56 30 56 33.3 1.251 24 0
4 132 0 0 0 32.9 0.302 23 1
0 105 90 0 0 29.6 0.197 46 0
0 57 60 0 0 21.7 0.735 67 0
0 127 80 37 210 36.3 0.804 23 0
3 129 92 49 155 36.4 0.968 32 1
8 100 74 40 215 39.4 0.661 43 1
3 128 72 25 190 32.4 0.549 27 1
10 90 85 32 0 34.9 0.825 56 1
4 84 90 23 56 39.5 0.159 25 0
1 88 78 29 76 32.0 0.365 29 0
8 186 90 35 225 34.5 0.423 37 1
5 187 76 27 207 43.6 1.034 53 1
4 131 68 21 166 33.1 0.160 28 0
1 164 82 43 67 32.8 0.341 50 0
4 189 110 31 0 28.5 0.680 37 0
1 116 70 28 0 27.4 0.204 21 0
3 84 68 30 106 31.9 0.591 25 0
6 114 88 0 0 27.8 0.247 66 0
1 88 62 24 44 29.9 0.422 23 0
1 84 64 23 115 36.9 0.471 28 0
7 124 70 33 215 25.5 0.161 37 0
1 97 70 40 0 38.1 0.218 30 0
8 110 76 0 0 27.8 0.237 58 0
11 103 68 40 0 46.2 0.126 42 0
11 85 74 0 0 30.1 0.300 35 0
6 125 76 0 0 33.8 0.121 54 1
0 198 66 32 274 41.3 0.502 28 1
1 87 68 34 77 37.6 0.401 24 0
6 99 60 19 54 26.9 0.497 32 0
0 91 80 0 0 32.4 0.601 27 0
2 95 54 14 88 26.1 0.748 22 0
1 99 72 30 18 38.6 0.412 21 0
6 92 62 32 126 32.0 0.085 46 0
4 154 72 29 126 31.3 0.338 37 0
0 121 66 30 165 34.3 0.203 33 1
3 78 70 0 0 32.5 0.270 39 0
2 130 96 0 0 22.6 0.268 21 0
3 111 58 31 44 29.5 0.430 22 0
2 98 60 17 120 34.7 0.198 22 0
1 143 86 30 330 30.1 0.892 23 0
1 119 44 47 63 35.5 0.280 25 0
6 108 44 20 130 24.0 0.813 35 0
2 118 80 0 0 42.9 0.693 21 1
10 133 68 0 0 27.0 0.245 36 0
2 197 70 99 0 34.7 0.575 62 1
0 151 90 46 0 42.1 0.371 21 1
6 109 60 27 0 25.0 0.206 27 0
12 121 78 17 0 26.5 0.259 62 0
8 100 76 0 0 38.7 0.190 42 0
8 124 76 24 600 28.7 0.687 52 1
1 93 56 11 0 22.5 0.417 22 0
8 143 66 0 0 34.9 0.129 41 1
6 103 66 0 0 24.3 0.249 29 0
3 176 86 27 156 33.3 1.154 52 1
0 73 0 0 0 21.1 0.342 25 0
11 111 84 40 0 46.8 0.925 45 1
2 112 78 50 140 39.4 0.175 24 0
3 132 80 0 0 34.4 0.402 44 1
2 82 52 22 115 28.5 1.699 25 0
6 123 72 45 230 33.6 0.733 34 0
0 188 82 14 185 32.0 0.682 22 1
0 67 76 0 0 45.3 0.194 46 0
1 89 24 19 25 27.8 0.559 21 0
1 173 74 0 0 36.8 0.088 38 1
1 109 38 18 120 23.1 0.407 26 0
1 108 88 19 0 27.1 0.400 24 0
6 96 0 0 0 23.7 0.190 28 0
1 124 74 36 0 27.8 0.100 30 0
7 150 78 29 126 35.2 0.692 54 1
4 183 0 0 0 28.4 0.212 36 1
1 124 60 32 0 35.8 0.514 21 0
1 181 78 42 293 40.0 1.258 22 1
1 92 62 25 41 19.5 0.482 25 0
0 152 82 39 272 41.5 0.270 27 0
1 111 62 13 182 24.0 0.138 23 0
3 106 54 21 158 30.9 0.292 24 0
3 174 58 22 194 32.9 0.593 36 1
7 168 88 42 321 38.2 0.787 40 1
6 105 80 28 0 32.5 0.878 26 0
11 138 74 26 144 36.1 0.557 50 1
3 106 72 0 0 25.8 0.207 27 0
6 117 96 0 0 28.7 0.157 30 0
2 68 62 13 15 20.1 0.257 23 0
9 112 82 24 0 28.2 1.282 50 1
0 119 0 0 0 32.4 0.141 24 1
2 112 86 42 160 38.4 0.246 28 0
2 92 76 20 0 24.2 1.698 28 0
6 183 94 0 0 40.8 1.461 45 0
0 94 70 27 115 43.5 0.347 21 0
2 108 64 0 0 30.8 0.158 21 0
4 90 88 47 54 37.7 0.362 29 0
0 125 68 0 0 24.7 0.206 21 0
0 132 78 0 0 32.4 0.393 21 0
5 128 80 0 0 34.6 0.144 45 0
4 94 65 22 0 24.7 0.148 21 0
7 114 64 0 0 27.4 0.732 34 1
0 102 78 40 90 34.5 0.238 24 0
2 111 60 0 0 26.2 0.343 23 0
1 128 82 17 183 27.5 0.115 22 0
10 92 62 0 0 25.9 0.167 31 0
13 104 72 0 0 31.2 0.465 38 1
5 104 74 0 0 28.8 0.153 48 0
2 94 76 18 66 31.6 0.649 23 0
7 97 76 32 91 40.9 0.871 32 1
1 100 74 12 46 19.5 0.149 28 0
0 102 86 17 105 29.3 0.695 27 0
4 128 70 0 0 34.3 0.303 24 0
6 147 80 0 0 29.5 0.178 50 1
4 90 0 0 0 28.0 0.610 31 0
3 103 72 30 152 27.6 0.730 27 0
2 157 74 35 440 39.4 0.134 30 0
1 167 74 17 144 23.4 0.447 33 1
0 179 50 36 159 37.8 0.455 22 1
11 136 84 35 130 28.3 0.260 42 1
0 107 60 25 0 26.4 0.133 23 0
1 91 54 25 100 25.2 0.234 23 0
1 117 60 23 106 33.8 0.466 27 0
5 123 74 40 77 34.1 0.269 28 0
2 120 54 0 0 26.8 0.455 27 0
1 106 70 28 135 34.2 0.142 22 0
2 155 52 27 540 38.7 0.240 25 1
2 101 58 35 90 21.8 0.155 22 0
1 120 80 48 200 38.9 1.162 41 0
11 127 106 0 0 39.0 0.190 51 0
3 80 82 31 70 34.2 1.292 27 1
10 162 84 0 0 27.7 0.182 54 0
1 199 76 43 0 42.9 1.394 22 1
8 167 106 46 231 37.6 0.165 43 1
9 145 80 46 130 37.9 0.637 40 1
6 115 60 39 0 33.7 0.245 40 1
1 112 80 45 132 34.8 0.217 24 0
4 145 82 18 0 32.5 0.235 70 1
10 111 70 27 0 27.5 0.141 40 1
6 98 58 33 190 34.0 0.430 43 0
9 154 78 30 100 30.9 0.164 45 0
6 165 68 26 168 33.6 0.631 49 0
1 99 58 10 0 25.4 0.551 21 0
10 68 106 23 49 35.5 0.285 47 0
3 123 100 35 240 57.3 0.880 22 0
8 91 82 0 0 35.6 0.587 68 0
6 195 70 0 0 30.9 0.328 31 1
9 156 86 0 0 24.8 0.230 53 1
0 93 60 0 0 35.3 0.263 25 0
3 121 52 0 0 36.0 0.127 25 1
2 101 58 17 265 24.2 0.614 23 0
2 56 56 28 45 24.2 0.332 22 0
0 162 76 36 0 49.6 0.364 26 1
0 95 64 39 105 44.6 0.366 22 0
4 125 80 0 0 32.3 0.536 27 1
5 136 82 0 0 0.0 0.640 69 0
2 129 74 26 205 33.2 0.591 25 0
3 130 64 0 0 23.1 0.314 22 0
1 107 50 19 0 28.3 0.181 29 0
1 140 74 26 180 24.1 0.828 23 0
1 144 82 46 180 46.1 0.335 46 1
8 107 80 0 0 24.6 0.856 34 0
13 158 114 0 0 42.3 0.257 44 1
2 121 70 32 95 39.1 0.886 23 0
7 129 68 49 125 38.5 0.439 43 1
2 90 60 0 0 23.5 0.191 25 0
7 142 90 24 480 30.4 0.128 43 1
3 169 74 19 125 29.9 0.268 31 1
0 99 0 0 0 25.0 0.253 22 0
4 127 88 11 155 34.5 0.598 28 0
4 118 70 0 0 44.5 0.904 26 0
2 122 76 27 200 35.9 0.483 26 0
6 125 78 31 0 27.6 0.565 49 1
1 168 88 29 0 35.0 0.905 52 1
2 129 0 0 0 38.5 0.304 41 0
4 110 76 20 100 28.4 0.118 27 0
6 80 80 36 0 39.8 0.177 28 0
10 115 0 0 0 0.0 0.261 30 1
2 127 46 21 335 34.4 0.176 22 0
9 164 78 0 0 32.8 0.148 45 1
2 93 64 32 160 38.0 0.674 23 1
3 158 64 13 387 31.2 0.295 24 0
5 126 78 27 22 29.6 0.439 40 0
10 129 62 36 0 41.2 0.441 38 1
0 134 58 20 291 26.4 0.352 21 0
3 102 74 0 0 29.5 0.121 32 0
7 187 50 33 392 33.9 0.826 34 1
3 173 78 39 185 33.8 0.970 31 1
10 94 72 18 0 23.1 0.595 56 0
1 108 60 46 178 35.5 0.415 24 0
5 97 76 27 0 35.6 0.378 52 1
4 83 86 19 0 29.3 0.317 34 0
1 114 66 36 200 38.1 0.289 21 0
1 149 68 29 127 29.3 0.349 42 1
5 117 86 30 105 39.1 0.251 42 0
1 111 94 0 0 32.8 0.265 45 0
4 112 78 40 0 39.4 0.236 38 0
1 116 78 29 180 36.1 0.496 25 0
0 141 84 26 0 32.4 0.433 22 0
2 175 88 0 0 22.9 0.326 22 0
2 92 52 0 0 30.1 0.141 22 0
3 130 78 23 79 28.4 0.323 34 1
8 120 86 0 0 28.4 0.259 22 1
2 174 88 37 120 44.5 0.646 24 1
2 106 56 27 165 29.0 0.426 22 0
2 105 75 0 0 23.3 0.560 53 0
4 95 60 32 0 35.4 0.284 28 0
0 126 86 27 120 27.4 0.515 21 0
8 65 72 23 0 32.0 0.600 42 0
2 99 60 17 160 36.6 0.453 21 0
1 102 74 0 0 39.5 0.293 42 1
11 120 80 37 150 42.3 0.785 48 1
3 102 44 20 94 30.8 0.400 26 0
1 109 58 18 116 28.5 0.219 22 0
9 140 94 0 0 32.7 0.734 45 1
13 153 88 37 140 40.6 1.174 39 0
12 100 84 33 105 30.0 0.488 46 0
1 147 94 41 0 49.3 0.358 27 1
1 81 74 41 57 46.3 1.096 32 0
3 187 70 22 200 36.4 0.408 36 1
6 162 62 0 0 24.3 0.178 50 1
4 136 70 0 0 31.2 1.182 22 1
1 121 78 39 74 39.0 0.261 28 0
3 108 62 24 0 26.0 0.223 25 0
0 181 88 44 510 43.3 0.222 26 1
8 154 78 32 0 32.4 0.443 45 1
1 128 88 39 110 36.5 1.057 37 1
7 137 90 41 0 32.0 0.391 39 0
0 123 72 0 0 36.3 0.258 52 1
1 106 76 0 0 37.5 0.197 26 0
6 190 92 0 0 35.5 0.278 66 1
2 88 58 26 16 28.4 0.766 22 0
9 170 74 31 0 44.0 0.403 43 1
9 89 62 0 0 22.5 0.142 33 0
10 101 76 48 180 32.9 0.171 63 0
2 122 70 27 0 36.8 0.340 27 0
5 121 72 23 112 26.2 0.245 30 0
1 126 60 0 0 30.1 0.349 47 1
1 93 70 31 0 30.4 0.315 23 0 
# Consultamos el numero de filas y columnas
dim(diabetes)
## [1] 768   9

La base de datos contiene 768 observaciones y 9 variables, estas serán utilizadas para construir el modelo.

# Descripción de las variables
descripcion_variables <- data.frame(
  Variable = c(
    "Pregnancies",
    "Glucose",
    "BloodPressure",
    "SkinThickness",
    "Insulin",
    "BMI",
    "DiabetesPedigreeFunction",
    "Age",
    "Outcome"
  ),
  Descripcion = c(
    "Número de embarazos",
    "Nivel de glucosa",
    "Presión arterial",
    "Grosor del pliegue cutáneo",
    "Nivel de insulina",
    "Índice de masa corporal",
    "Antecedentes familiares de diabetes",
    "Edad del paciente",
    "Diagnóstico de diabetes"
  )
)

kable(
  descripcion_variables,
  caption = "Descripción de las variables"
) %>%
  kable_styling(
    bootstrap_options = c("striped","hover"),
    full_width = FALSE
  )
Descripción de las variables
Variable Descripcion
Pregnancies Número de embarazos
Glucose Nivel de glucosa
BloodPressure Presión arterial
SkinThickness Grosor del pliegue cutáneo
Insulin Nivel de insulina
BMI Índice de masa corporal
DiabetesPedigreeFunction Antecedentes familiares de diabetes
Age Edad del paciente
Outcome Diagnóstico de diabetes 
# Calculamos medidas descriptivas
summary(diabetes)
##   Pregnancies        Glucose      BloodPressure    SkinThickness  
##  Min.   : 0.000   Min.   :  0.0   Min.   :  0.00   Min.   : 0.00  
##  1st Qu.: 1.000   1st Qu.: 99.0   1st Qu.: 62.00   1st Qu.: 0.00  
##  Median : 3.000   Median :117.0   Median : 72.00   Median :23.00  
##  Mean   : 3.845   Mean   :120.9   Mean   : 69.11   Mean   :20.54  
##  3rd Qu.: 6.000   3rd Qu.:140.2   3rd Qu.: 80.00   3rd Qu.:32.00  
##  Max.   :17.000   Max.   :199.0   Max.   :122.00   Max.   :99.00  
##     Insulin           BMI        DiabetesPedigreeFunction      Age       
##  Min.   :  0.0   Min.   : 0.00   Min.   :0.0780           Min.   :21.00  
##  1st Qu.:  0.0   1st Qu.:27.30   1st Qu.:0.2437           1st Qu.:24.00  
##  Median : 30.5   Median :32.00   Median :0.3725           Median :29.00  
##  Mean   : 79.8   Mean   :31.99   Mean   :0.4719           Mean   :33.24  
##  3rd Qu.:127.2   3rd Qu.:36.60   3rd Qu.:0.6262           3rd Qu.:41.00  
##  Max.   :846.0   Max.   :67.10   Max.   :2.4200           Max.   :81.00  
##     Outcome     
##  Min.   :0.000  
##  1st Qu.:0.000  
##  Median :0.000  
##  Mean   :0.349  
##  3rd Qu.:1.000  
##  Max.   :1.000

Se obtienen medidas descriptivas como valores mínimos, máximos, medianas y promedios, con el fin de conocer el comportamiento general de las variables.

# Distribución de la variable objetivo
table(diabetes$Outcome)
## 
##   0   1 
## 500 268

Esta tabla permite conocer cuántos pacientes presentan diabetes y cuántos no presentan la enfermedad.

# Representación gráfica de la variable objetivo

barplot(
  table(diabetes$Outcome),
  main = "Distribución de pacientes con y sin diabetes",
  xlab = "Diagnóstico",
  ylab = "Frecuencia"
)

CONSTRUCCIÓN DEL MODELO

DIVISIÓN DE LOS DATOS

La base de datos se divide en entrenamiento (70%) y prueba (30%)

# Se fija los 
set.seed(123)

indicedatos <- createDataPartition(
  diabetes$Outcome,
  p = 0.7,
  list = FALSE
)

datosentrena <- diabetes[indicedatos, ]
datosprueba <- diabetes[-indicedatos, ]

Verificamos:

dim(datosentrena)
## [1] 538   9
dim(datosprueba)
## [1] 230   9

El conjunto de entrenamiento se utilizará para construir el modelo, mientras que el conjunto de prueba permitirá evaluar su capacidad de predicción.

CREACIÓN DEL MODELO

# Construimos el modelo de regresión logística binaria

modelo_regresion <- glm(
  Outcome ~ .,
  data = datosentrena,
  family = "binomial"
)
# Resumen del modelo
# Revisamos los coeficientes obtenidos

summary(modelo_regresion)
## 
## Call:
## glm(formula = Outcome ~ ., family = "binomial", data = datosentrena)
## 
## Coefficients:
##                           Estimate Std. Error z value Pr(>|z|)    
## (Intercept)              -8.424356   0.841700 -10.009  < 2e-16 ***
## Pregnancies               0.103402   0.037988   2.722  0.00649 ** 
## Glucose                   0.035793   0.004565   7.841 4.46e-15 ***
## BloodPressure            -0.012665   0.006058  -2.091  0.03657 *  
## SkinThickness             0.003591   0.008093   0.444  0.65721    
## Insulin                  -0.001726   0.001060  -1.629  0.10335    
## BMI                       0.088810   0.017964   4.944 7.67e-07 ***
## DiabetesPedigreeFunction  0.699484   0.334854   2.089  0.03671 *  
## Age                       0.017113   0.011068   1.546  0.12206    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 695.03  on 537  degrees of freedom
## Residual deviance: 510.06  on 529  degrees of freedom
## AIC: 528.06
## 
## Number of Fisher Scoring iterations: 5

Este resultado permite identificar qué variables presentan una mayor influencia sobre la presencia o ausencia de diabetes.

# Predicciones
# Calculamos la probabilidad de diabetes para cada paciente
probabilidades <- predict(
  modelo_regresion,
  datosprueba,
  type = "response"
)
# Convertimos las probabilidades en categorías

predicciones <- ifelse(
  probabilidades > 0.5,
  1,
  0
)
# Convertimos a factores
predicciones <- as.factor(predicciones)

datosprueba$Outcome <- as.factor(
  datosprueba$Outcome
)
# Matriz de confusión 
# Comparamos los resultados reales con los resultados predichos

matriz <- confusionMatrix(
  predicciones,
  datosprueba$Outcome
)

matriz
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   0   1
##          0 137  36
##          1  12  45
##                                          
##                Accuracy : 0.7913         
##                  95% CI : (0.733, 0.8419)
##     No Information Rate : 0.6478         
##     P-Value [Acc > NIR] : 1.521e-06      
##                                          
##                   Kappa : 0.5095         
##                                          
##  Mcnemar's Test P-Value : 0.0009009      
##                                          
##             Sensitivity : 0.9195         
##             Specificity : 0.5556         
##          Pos Pred Value : 0.7919         
##          Neg Pred Value : 0.7895         
##              Prevalence : 0.6478         
##          Detection Rate : 0.5957         
##    Detection Prevalence : 0.7522         
##       Balanced Accuracy : 0.7375         
##                                          
##        'Positive' Class : 0              
##

La matriz de confusión permite comparar los valores reales con los valores predichos por el modelo. Se observa que el modelo clasificó correctamente a 137 pacientes sin diabetes y 45 pacientes con diabetes. Sin embargo, también se presentaron algunos errores de clasificación, ya que 36 pacientes con diabetes fueron identificados como sanos y 12 pacientes sanos fueron clasificados como diabéticos.

# Evaluamos la exactitud del modelo

matriz$overall["Accuracy"]
##  Accuracy 
## 0.7913043

La exactitud representa el porcentaje de pacientes que fueron clasificados correctamente por el modelo.

# Revisamos los coeficientes obtenidos por el modelo

summary(modelo_regresion)
## 
## Call:
## glm(formula = Outcome ~ ., family = "binomial", data = datosentrena)
## 
## Coefficients:
##                           Estimate Std. Error z value Pr(>|z|)    
## (Intercept)              -8.424356   0.841700 -10.009  < 2e-16 ***
## Pregnancies               0.103402   0.037988   2.722  0.00649 ** 
## Glucose                   0.035793   0.004565   7.841 4.46e-15 ***
## BloodPressure            -0.012665   0.006058  -2.091  0.03657 *  
## SkinThickness             0.003591   0.008093   0.444  0.65721    
## Insulin                  -0.001726   0.001060  -1.629  0.10335    
## BMI                       0.088810   0.017964   4.944 7.67e-07 ***
## DiabetesPedigreeFunction  0.699484   0.334854   2.089  0.03671 *  
## Age                       0.017113   0.011068   1.546  0.12206    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 695.03  on 537  degrees of freedom
## Residual deviance: 510.06  on 529  degrees of freedom
## AIC: 528.06
## 
## Number of Fisher Scoring iterations: 5

El resumen del modelo presenta información sobre la influencia de cada variable en la predicción de diabetes. Para interpretar los resultados se debe prestar especial atención a la columna Pr(>|z|), la cual corresponde al valor p asociado a cada variable.

Cuando el valor p es menor a 0.05, se considera que la variable tiene una relación significativa con la presencia de diabetes. Por el contrario, si el valor p es mayor a 0.05, la evidencia estadística no es suficiente para afirmar que dicha variable influya en la clasificación realizada por el modelo.

Adicionalmente, el signo del coeficiente estimado (Estimate) permite identificar la dirección de la relación. Un coeficiente positivo indica que al aumentar la variable también aumenta la probabilidad de presentar diabetes, mientras que un coeficiente negativo indica una disminución en dicha probabilidad.

# Extraemos los coeficientes del modelo

coeficientes <- summary(modelo_regresion)$coefficients

kable(
  round(coeficientes,4),
  caption = "Coeficientes obtenidos por el modelo de Regresión Logística"
) %>%
  kable_styling(
    bootstrap_options = c("striped","hover"),
    full_width = FALSE
  ) %>%
  scroll_box(
    width = "100%",
    height = "300px"
  )
Coeficientes obtenidos por el modelo de Regresión Logística
Estimate Std. Error z value Pr(>&#124;z&#124;)
(Intercept) -8.4244 0.8417 -10.0087 0.0000
Pregnancies 0.1034 0.0380 2.7219 0.0065
Glucose 0.0358 0.0046 7.8414 0.0000
BloodPressure -0.0127 0.0061 -2.0906 0.0366
SkinThickness 0.0036 0.0081 0.4438 0.6572
Insulin -0.0017 0.0011 -1.6288 0.1033
BMI 0.0888 0.0180 4.9437 0.0000
DiabetesPedigreeFunction 0.6995 0.3349 2.0889 0.0367
Age 0.0171 0.0111 1.5462 0.1221

En la tabla anterior se presentan los coeficientes estimados para cada variable. Aquellas variables con valores p inferiores a 0.05 se consideran estadísticamente significativas y aportan información relevante para la predicción de diabetes.

CONCLUSIÓN

La Regresión Logística Binaria permitió predecir la presencia de diabetes a partir de características clínicas de los pacientes. Los resultados mostraron que variables como la glucosa, el índice de masa corporal, el número de embarazos y los antecedentes familiares tienen una influencia significativa en la clasificación. Asimismo, el modelo alcanzó una exactitud del 79.13%, demostrando una capacidad adecuada para diferenciar entre pacientes con y sin diabetes.

ÁRBOLES DE DECISIÓN

Los Árboles de Decisión son algoritmos de aprendizaje supervisado utilizados para problemas de clasificación y regresión. Su funcionamiento consiste en dividir los datos en grupos cada vez más pequeños mediante reglas de decisión basadas en las características de las observaciones.

La estructura del modelo se asemeja a un árbol, donde cada nodo representa una condición, cada rama representa una posible respuesta y cada hoja contiene la clasificación final obtenida por el modelo.

Una de sus principales ventajas es que permiten interpretar fácilmente cómo se toman las decisiones dentro del proceso de clasificación.

CRITERIOS DE DIVISIÓN

Para construir el árbol, el algoritmo evalúa diferentes variables y selecciona aquellas que generan la mejor separación entre las clases.

Uno de los criterios más utilizados es el índice de Gini:

\[\text{Gini} = 1 - \sum (p_i)^2\]

Donde:

  • \(\text{Gini}\): Representa el coeficiente de impureza.
  • \((p_i)\) Representa la proporción de observaciones de cada clase.
  • Un valor cercano a 0 indica que el grupo es más homogéneo.

APLICACIONES

Los Árboles de Decisión son ampliamente utilizados en diferentes áreas:

  • Diagnóstico médico.
  • Clasificación de clientes.
  • Aprobación de créditos.
  • Predicción de fallas.
  • Análisis de riesgos.

CASO DE ESTUDIO: TITANIC

Para aplicar el modelo se utilizará la base de datos del Titanic, la cual contiene información de los pasajeros que viajaban a bordo del barco.

La variable objetivo será:

Survived

  • 0 = No sobrevivió.
  • 1 = Sobrevivió.

JUSTIFICACIÓN DE LA BASE DE DATOS

Esta base de datos fue seleccionada porque representa un problema clásico de clasificación binaria. A partir de variables como edad, sexo, tarifa pagada y clase del pasajero, el modelo intentará identificar qué factores influyeron en la supervivencia de los pasajeros.

HIPÓTESIS DEL CASO DE ESTUDIO

Hipótesis nula (H₀)

Las características de los pasajeros no permiten predecir su supervivencia.

Hipótesis alternativa (H₁)

Las características de los pasajeros permiten predecir su supervivencia.

# Cargamos la base de datos
datostitanic <- read.csv("C:/Users/aleja/OneDrive/python/Bases de datos/Titanic-Dataset.csv")

# Visualizamos los registros de la base de datos

kable(
  datostitanic,
  caption = "Base de datos utilizada para el modelo de Árbol de Decisión"
) %>%
  kable_styling(
    bootstrap_options = c("striped","hover"),
    full_width = FALSE
  ) %>%
  scroll_box(
    width = "100%",
    height = "300px"
  )
Base de datos utilizada para el modelo de Árbol de Decisión
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
1 0 3 Braund, Mr. Owen Harris male 22.00 1 0 A/5 21171 7.2500 S
2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38.00 1 0 PC 17599 71.2833 C85 C
3 1 3 Heikkinen, Miss. Laina female 26.00 0 0 STON/O2. 3101282 7.9250 S
4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.00 1 0 113803 53.1000 C123 S
5 0 3 Allen, Mr. William Henry male 35.00 0 0 373450 8.0500 S
6 0 3 Moran, Mr. James male NA 0 0 330877 8.4583 Q
7 0 1 McCarthy, Mr. Timothy J male 54.00 0 0 17463 51.8625 E46 S
8 0 3 Palsson, Master. Gosta Leonard male 2.00 3 1 349909 21.0750 S
9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.00 0 2 347742 11.1333 S
10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female 14.00 1 0 237736 30.0708 C
11 1 3 Sandstrom, Miss. Marguerite Rut female 4.00 1 1 PP 9549 16.7000 G6 S
12 1 1 Bonnell, Miss. Elizabeth female 58.00 0 0 113783 26.5500 C103 S
13 0 3 Saundercock, Mr. William Henry male 20.00 0 0 A/5. 2151 8.0500 S
14 0 3 Andersson, Mr. Anders Johan male 39.00 1 5 347082 31.2750 S
15 0 3 Vestrom, Miss. Hulda Amanda Adolfina female 14.00 0 0 350406 7.8542 S
16 1 2 Hewlett, Mrs. (Mary D Kingcome) female 55.00 0 0 248706 16.0000 S
17 0 3 Rice, Master. Eugene male 2.00 4 1 382652 29.1250 Q
18 1 2 Williams, Mr. Charles Eugene male NA 0 0 244373 13.0000 S
19 0 3 Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele) female 31.00 1 0 345763 18.0000 S
20 1 3 Masselmani, Mrs. Fatima female NA 0 0 2649 7.2250 C
21 0 2 Fynney, Mr. Joseph J male 35.00 0 0 239865 26.0000 S
22 1 2 Beesley, Mr. Lawrence male 34.00 0 0 248698 13.0000 D56 S
23 1 3 McGowan, Miss. Anna “Annie” female 15.00 0 0 330923 8.0292 Q
24 1 1 Sloper, Mr. William Thompson male 28.00 0 0 113788 35.5000 A6 S
25 0 3 Palsson, Miss. Torborg Danira female 8.00 3 1 349909 21.0750 S
26 1 3 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson) female 38.00 1 5 347077 31.3875 S
27 0 3 Emir, Mr. Farred Chehab male NA 0 0 2631 7.2250 C
28 0 1 Fortune, Mr. Charles Alexander male 19.00 3 2 19950 263.0000 C23 C25 C27 S
29 1 3 O’Dwyer, Miss. Ellen “Nellie” female NA 0 0 330959 7.8792 Q
30 0 3 Todoroff, Mr. Lalio male NA 0 0 349216 7.8958 S
31 0 1 Uruchurtu, Don. Manuel E male 40.00 0 0 PC 17601 27.7208 C
32 1 1 Spencer, Mrs. William Augustus (Marie Eugenie) female NA 1 0 PC 17569 146.5208 B78 C
33 1 3 Glynn, Miss. Mary Agatha female NA 0 0 335677 7.7500 Q
34 0 2 Wheadon, Mr. Edward H male 66.00 0 0 C.A. 24579 10.5000 S
35 0 1 Meyer, Mr. Edgar Joseph male 28.00 1 0 PC 17604 82.1708 C
36 0 1 Holverson, Mr. Alexander Oskar male 42.00 1 0 113789 52.0000 S
37 1 3 Mamee, Mr. Hanna male NA 0 0 2677 7.2292 C
38 0 3 Cann, Mr. Ernest Charles male 21.00 0 0 A./5. 2152 8.0500 S
39 0 3 Vander Planke, Miss. Augusta Maria female 18.00 2 0 345764 18.0000 S
40 1 3 Nicola-Yarred, Miss. Jamila female 14.00 1 0 2651 11.2417 C
41 0 3 Ahlin, Mrs. Johan (Johanna Persdotter Larsson) female 40.00 1 0 7546 9.4750 S
42 0 2 Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott) female 27.00 1 0 11668 21.0000 S
43 0 3 Kraeff, Mr. Theodor male NA 0 0 349253 7.8958 C
44 1 2 Laroche, Miss. Simonne Marie Anne Andree female 3.00 1 2 SC/Paris 2123 41.5792 C
45 1 3 Devaney, Miss. Margaret Delia female 19.00 0 0 330958 7.8792 Q
46 0 3 Rogers, Mr. William John male NA 0 0 S.C./A.4. 23567 8.0500 S
47 0 3 Lennon, Mr. Denis male NA 1 0 370371 15.5000 Q
48 1 3 O’Driscoll, Miss. Bridget female NA 0 0 14311 7.7500 Q
49 0 3 Samaan, Mr. Youssef male NA 2 0 2662 21.6792 C
50 0 3 Arnold-Franchi, Mrs. Josef (Josefine Franchi) female 18.00 1 0 349237 17.8000 S
51 0 3 Panula, Master. Juha Niilo male 7.00 4 1 3101295 39.6875 S
52 0 3 Nosworthy, Mr. Richard Cater male 21.00 0 0 A/4. 39886 7.8000 S
53 1 1 Harper, Mrs. Henry Sleeper (Myna Haxtun) female 49.00 1 0 PC 17572 76.7292 D33 C
54 1 2 Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson) female 29.00 1 0 2926 26.0000 S
55 0 1 Ostby, Mr. Engelhart Cornelius male 65.00 0 1 113509 61.9792 B30 C
56 1 1 Woolner, Mr. Hugh male NA 0 0 19947 35.5000 C52 S
57 1 2 Rugg, Miss. Emily female 21.00 0 0 C.A. 31026 10.5000 S
58 0 3 Novel, Mr. Mansouer male 28.50 0 0 2697 7.2292 C
59 1 2 West, Miss. Constance Mirium female 5.00 1 2 C.A. 34651 27.7500 S
60 0 3 Goodwin, Master. William Frederick male 11.00 5 2 CA 2144 46.9000 S
61 0 3 Sirayanian, Mr. Orsen male 22.00 0 0 2669 7.2292 C
62 1 1 Icard, Miss. Amelie female 38.00 0 0 113572 80.0000 B28
63 0 1 Harris, Mr. Henry Birkhardt male 45.00 1 0 36973 83.4750 C83 S
64 0 3 Skoog, Master. Harald male 4.00 3 2 347088 27.9000 S
65 0 1 Stewart, Mr. Albert A male NA 0 0 PC 17605 27.7208 C
66 1 3 Moubarek, Master. Gerios male NA 1 1 2661 15.2458 C
67 1 2 Nye, Mrs. (Elizabeth Ramell) female 29.00 0 0 C.A. 29395 10.5000 F33 S
68 0 3 Crease, Mr. Ernest James male 19.00 0 0 S.P. 3464 8.1583 S
69 1 3 Andersson, Miss. Erna Alexandra female 17.00 4 2 3101281 7.9250 S
70 0 3 Kink, Mr. Vincenz male 26.00 2 0 315151 8.6625 S
71 0 2 Jenkin, Mr. Stephen Curnow male 32.00 0 0 C.A. 33111 10.5000 S
72 0 3 Goodwin, Miss. Lillian Amy female 16.00 5 2 CA 2144 46.9000 S
73 0 2 Hood, Mr. Ambrose Jr male 21.00 0 0 S.O.C. 14879 73.5000 S
74 0 3 Chronopoulos, Mr. Apostolos male 26.00 1 0 2680 14.4542 C
75 1 3 Bing, Mr. Lee male 32.00 0 0 1601 56.4958 S
76 0 3 Moen, Mr. Sigurd Hansen male 25.00 0 0 348123 7.6500 F G73 S
77 0 3 Staneff, Mr. Ivan male NA 0 0 349208 7.8958 S
78 0 3 Moutal, Mr. Rahamin Haim male NA 0 0 374746 8.0500 S
79 1 2 Caldwell, Master. Alden Gates male 0.83 0 2 248738 29.0000 S
80 1 3 Dowdell, Miss. Elizabeth female 30.00 0 0 364516 12.4750 S
81 0 3 Waelens, Mr. Achille male 22.00 0 0 345767 9.0000 S
82 1 3 Sheerlinck, Mr. Jan Baptist male 29.00 0 0 345779 9.5000 S
83 1 3 McDermott, Miss. Brigdet Delia female NA 0 0 330932 7.7875 Q
84 0 1 Carrau, Mr. Francisco M male 28.00 0 0 113059 47.1000 S
85 1 2 Ilett, Miss. Bertha female 17.00 0 0 SO/C 14885 10.5000 S
86 1 3 Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson) female 33.00 3 0 3101278 15.8500 S
87 0 3 Ford, Mr. William Neal male 16.00 1 3 W./C. 6608 34.3750 S
88 0 3 Slocovski, Mr. Selman Francis male NA 0 0 SOTON/OQ 392086 8.0500 S
89 1 1 Fortune, Miss. Mabel Helen female 23.00 3 2 19950 263.0000 C23 C25 C27 S
90 0 3 Celotti, Mr. Francesco male 24.00 0 0 343275 8.0500 S
91 0 3 Christmann, Mr. Emil male 29.00 0 0 343276 8.0500 S
92 0 3 Andreasson, Mr. Paul Edvin male 20.00 0 0 347466 7.8542 S
93 0 1 Chaffee, Mr. Herbert Fuller male 46.00 1 0 W.E.P. 5734 61.1750 E31 S
94 0 3 Dean, Mr. Bertram Frank male 26.00 1 2 C.A. 2315 20.5750 S
95 0 3 Coxon, Mr. Daniel male 59.00 0 0 364500 7.2500 S
96 0 3 Shorney, Mr. Charles Joseph male NA 0 0 374910 8.0500 S
97 0 1 Goldschmidt, Mr. George B male 71.00 0 0 PC 17754 34.6542 A5 C
98 1 1 Greenfield, Mr. William Bertram male 23.00 0 1 PC 17759 63.3583 D10 D12 C
99 1 2 Doling, Mrs. John T (Ada Julia Bone) female 34.00 0 1 231919 23.0000 S
100 0 2 Kantor, Mr. Sinai male 34.00 1 0 244367 26.0000 S
101 0 3 Petranec, Miss. Matilda female 28.00 0 0 349245 7.8958 S
102 0 3 Petroff, Mr. Pastcho (“Pentcho”) male NA 0 0 349215 7.8958 S
103 0 1 White, Mr. Richard Frasar male 21.00 0 1 35281 77.2875 D26 S
104 0 3 Johansson, Mr. Gustaf Joel male 33.00 0 0 7540 8.6542 S
105 0 3 Gustafsson, Mr. Anders Vilhelm male 37.00 2 0 3101276 7.9250 S
106 0 3 Mionoff, Mr. Stoytcho male 28.00 0 0 349207 7.8958 S
107 1 3 Salkjelsvik, Miss. Anna Kristine female 21.00 0 0 343120 7.6500 S
108 1 3 Moss, Mr. Albert Johan male NA 0 0 312991 7.7750 S
109 0 3 Rekic, Mr. Tido male 38.00 0 0 349249 7.8958 S
110 1 3 Moran, Miss. Bertha female NA 1 0 371110 24.1500 Q
111 0 1 Porter, Mr. Walter Chamberlain male 47.00 0 0 110465 52.0000 C110 S
112 0 3 Zabour, Miss. Hileni female 14.50 1 0 2665 14.4542 C
113 0 3 Barton, Mr. David John male 22.00 0 0 324669 8.0500 S
114 0 3 Jussila, Miss. Katriina female 20.00 1 0 4136 9.8250 S
115 0 3 Attalah, Miss. Malake female 17.00 0 0 2627 14.4583 C
116 0 3 Pekoniemi, Mr. Edvard male 21.00 0 0 STON/O 2. 3101294 7.9250 S
117 0 3 Connors, Mr. Patrick male 70.50 0 0 370369 7.7500 Q
118 0 2 Turpin, Mr. William John Robert male 29.00 1 0 11668 21.0000 S
119 0 1 Baxter, Mr. Quigg Edmond male 24.00 0 1 PC 17558 247.5208 B58 B60 C
120 0 3 Andersson, Miss. Ellis Anna Maria female 2.00 4 2 347082 31.2750 S
121 0 2 Hickman, Mr. Stanley George male 21.00 2 0 S.O.C. 14879 73.5000 S
122 0 3 Moore, Mr. Leonard Charles male NA 0 0 A4. 54510 8.0500 S
123 0 2 Nasser, Mr. Nicholas male 32.50 1 0 237736 30.0708 C
124 1 2 Webber, Miss. Susan female 32.50 0 0 27267 13.0000 E101 S
125 0 1 White, Mr. Percival Wayland male 54.00 0 1 35281 77.2875 D26 S
126 1 3 Nicola-Yarred, Master. Elias male 12.00 1 0 2651 11.2417 C
127 0 3 McMahon, Mr. Martin male NA 0 0 370372 7.7500 Q
128 1 3 Madsen, Mr. Fridtjof Arne male 24.00 0 0 C 17369 7.1417 S
129 1 3 Peter, Miss. Anna female NA 1 1 2668 22.3583 F E69 C
130 0 3 Ekstrom, Mr. Johan male 45.00 0 0 347061 6.9750 S
131 0 3 Drazenoic, Mr. Jozef male 33.00 0 0 349241 7.8958 C
132 0 3 Coelho, Mr. Domingos Fernandeo male 20.00 0 0 SOTON/O.Q. 3101307 7.0500 S
133 0 3 Robins, Mrs. Alexander A (Grace Charity Laury) female 47.00 1 0 A/5. 3337 14.5000 S
134 1 2 Weisz, Mrs. Leopold (Mathilde Francoise Pede) female 29.00 1 0 228414 26.0000 S
135 0 2 Sobey, Mr. Samuel James Hayden male 25.00 0 0 C.A. 29178 13.0000 S
136 0 2 Richard, Mr. Emile male 23.00 0 0 SC/PARIS 2133 15.0458 C
137 1 1 Newsom, Miss. Helen Monypeny female 19.00 0 2 11752 26.2833 D47 S
138 0 1 Futrelle, Mr. Jacques Heath male 37.00 1 0 113803 53.1000 C123 S
139 0 3 Osen, Mr. Olaf Elon male 16.00 0 0 7534 9.2167 S
140 0 1 Giglio, Mr. Victor male 24.00 0 0 PC 17593 79.2000 B86 C
141 0 3 Boulos, Mrs. Joseph (Sultana) female NA 0 2 2678 15.2458 C
142 1 3 Nysten, Miss. Anna Sofia female 22.00 0 0 347081 7.7500 S
143 1 3 Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck) female 24.00 1 0 STON/O2. 3101279 15.8500 S
144 0 3 Burke, Mr. Jeremiah male 19.00 0 0 365222 6.7500 Q
145 0 2 Andrew, Mr. Edgardo Samuel male 18.00 0 0 231945 11.5000 S
146 0 2 Nicholls, Mr. Joseph Charles male 19.00 1 1 C.A. 33112 36.7500 S
147 1 3 Andersson, Mr. August Edvard (“Wennerstrom”) male 27.00 0 0 350043 7.7958 S
148 0 3 Ford, Miss. Robina Maggie “Ruby” female 9.00 2 2 W./C. 6608 34.3750 S
149 0 2 Navratil, Mr. Michel (“Louis M Hoffman”) male 36.50 0 2 230080 26.0000 F2 S
150 0 2 Byles, Rev. Thomas Roussel Davids male 42.00 0 0 244310 13.0000 S
151 0 2 Bateman, Rev. Robert James male 51.00 0 0 S.O.P. 1166 12.5250 S
152 1 1 Pears, Mrs. Thomas (Edith Wearne) female 22.00 1 0 113776 66.6000 C2 S
153 0 3 Meo, Mr. Alfonzo male 55.50 0 0 A.5. 11206 8.0500 S
154 0 3 van Billiard, Mr. Austin Blyler male 40.50 0 2 A/5. 851 14.5000 S
155 0 3 Olsen, Mr. Ole Martin male NA 0 0 Fa 265302 7.3125 S
156 0 1 Williams, Mr. Charles Duane male 51.00 0 1 PC 17597 61.3792 C
157 1 3 Gilnagh, Miss. Katherine “Katie” female 16.00 0 0 35851 7.7333 Q
158 0 3 Corn, Mr. Harry male 30.00 0 0 SOTON/OQ 392090 8.0500 S
159 0 3 Smiljanic, Mr. Mile male NA 0 0 315037 8.6625 S
160 0 3 Sage, Master. Thomas Henry male NA 8 2 CA. 2343 69.5500 S
161 0 3 Cribb, Mr. John Hatfield male 44.00 0 1 371362 16.1000 S
162 1 2 Watt, Mrs. James (Elizabeth “Bessie” Inglis Milne) female 40.00 0 0 C.A. 33595 15.7500 S
163 0 3 Bengtsson, Mr. John Viktor male 26.00 0 0 347068 7.7750 S
164 0 3 Calic, Mr. Jovo male 17.00 0 0 315093 8.6625 S
165 0 3 Panula, Master. Eino Viljami male 1.00 4 1 3101295 39.6875 S
166 1 3 Goldsmith, Master. Frank John William “Frankie” male 9.00 0 2 363291 20.5250 S
167 1 1 Chibnall, Mrs. (Edith Martha Bowerman) female NA 0 1 113505 55.0000 E33 S
168 0 3 Skoog, Mrs. William (Anna Bernhardina Karlsson) female 45.00 1 4 347088 27.9000 S
169 0 1 Baumann, Mr. John D male NA 0 0 PC 17318 25.9250 S
170 0 3 Ling, Mr. Lee male 28.00 0 0 1601 56.4958 S
171 0 1 Van der hoef, Mr. Wyckoff male 61.00 0 0 111240 33.5000 B19 S
172 0 3 Rice, Master. Arthur male 4.00 4 1 382652 29.1250 Q
173 1 3 Johnson, Miss. Eleanor Ileen female 1.00 1 1 347742 11.1333 S
174 0 3 Sivola, Mr. Antti Wilhelm male 21.00 0 0 STON/O 2. 3101280 7.9250 S
175 0 1 Smith, Mr. James Clinch male 56.00 0 0 17764 30.6958 A7 C
176 0 3 Klasen, Mr. Klas Albin male 18.00 1 1 350404 7.8542 S
177 0 3 Lefebre, Master. Henry Forbes male NA 3 1 4133 25.4667 S
178 0 1 Isham, Miss. Ann Elizabeth female 50.00 0 0 PC 17595 28.7125 C49 C
179 0 2 Hale, Mr. Reginald male 30.00 0 0 250653 13.0000 S
180 0 3 Leonard, Mr. Lionel male 36.00 0 0 LINE 0.0000 S
181 0 3 Sage, Miss. Constance Gladys female NA 8 2 CA. 2343 69.5500 S
182 0 2 Pernot, Mr. Rene male NA 0 0 SC/PARIS 2131 15.0500 C
183 0 3 Asplund, Master. Clarence Gustaf Hugo male 9.00 4 2 347077 31.3875 S
184 1 2 Becker, Master. Richard F male 1.00 2 1 230136 39.0000 F4 S
185 1 3 Kink-Heilmann, Miss. Luise Gretchen female 4.00 0 2 315153 22.0250 S
186 0 1 Rood, Mr. Hugh Roscoe male NA 0 0 113767 50.0000 A32 S
187 1 3 O’Brien, Mrs. Thomas (Johanna “Hannah” Godfrey) female NA 1 0 370365 15.5000 Q
188 1 1 Romaine, Mr. Charles Hallace (“Mr C Rolmane”) male 45.00 0 0 111428 26.5500 S
189 0 3 Bourke, Mr. John male 40.00 1 1 364849 15.5000 Q
190 0 3 Turcin, Mr. Stjepan male 36.00 0 0 349247 7.8958 S
191 1 2 Pinsky, Mrs. (Rosa) female 32.00 0 0 234604 13.0000 S
192 0 2 Carbines, Mr. William male 19.00 0 0 28424 13.0000 S
193 1 3 Andersen-Jensen, Miss. Carla Christine Nielsine female 19.00 1 0 350046 7.8542 S
194 1 2 Navratil, Master. Michel M male 3.00 1 1 230080 26.0000 F2 S
195 1 1 Brown, Mrs. James Joseph (Margaret Tobin) female 44.00 0 0 PC 17610 27.7208 B4 C
196 1 1 Lurette, Miss. Elise female 58.00 0 0 PC 17569 146.5208 B80 C
197 0 3 Mernagh, Mr. Robert male NA 0 0 368703 7.7500 Q
198 0 3 Olsen, Mr. Karl Siegwart Andreas male 42.00 0 1 4579 8.4042 S
199 1 3 Madigan, Miss. Margaret “Maggie” female NA 0 0 370370 7.7500 Q
200 0 2 Yrois, Miss. Henriette (“Mrs Harbeck”) female 24.00 0 0 248747 13.0000 S
201 0 3 Vande Walle, Mr. Nestor Cyriel male 28.00 0 0 345770 9.5000 S
202 0 3 Sage, Mr. Frederick male NA 8 2 CA. 2343 69.5500 S
203 0 3 Johanson, Mr. Jakob Alfred male 34.00 0 0 3101264 6.4958 S
204 0 3 Youseff, Mr. Gerious male 45.50 0 0 2628 7.2250 C
205 1 3 Cohen, Mr. Gurshon “Gus” male 18.00 0 0 A/5 3540 8.0500 S
206 0 3 Strom, Miss. Telma Matilda female 2.00 0 1 347054 10.4625 G6 S
207 0 3 Backstrom, Mr. Karl Alfred male 32.00 1 0 3101278 15.8500 S
208 1 3 Albimona, Mr. Nassef Cassem male 26.00 0 0 2699 18.7875 C
209 1 3 Carr, Miss. Helen “Ellen” female 16.00 0 0 367231 7.7500 Q
210 1 1 Blank, Mr. Henry male 40.00 0 0 112277 31.0000 A31 C
211 0 3 Ali, Mr. Ahmed male 24.00 0 0 SOTON/O.Q. 3101311 7.0500 S
212 1 2 Cameron, Miss. Clear Annie female 35.00 0 0 F.C.C. 13528 21.0000 S
213 0 3 Perkin, Mr. John Henry male 22.00 0 0 A/5 21174 7.2500 S
214 0 2 Givard, Mr. Hans Kristensen male 30.00 0 0 250646 13.0000 S
215 0 3 Kiernan, Mr. Philip male NA 1 0 367229 7.7500 Q
216 1 1 Newell, Miss. Madeleine female 31.00 1 0 35273 113.2750 D36 C
217 1 3 Honkanen, Miss. Eliina female 27.00 0 0 STON/O2. 3101283 7.9250 S
218 0 2 Jacobsohn, Mr. Sidney Samuel male 42.00 1 0 243847 27.0000 S
219 1 1 Bazzani, Miss. Albina female 32.00 0 0 11813 76.2917 D15 C
220 0 2 Harris, Mr. Walter male 30.00 0 0 W/C 14208 10.5000 S
221 1 3 Sunderland, Mr. Victor Francis male 16.00 0 0 SOTON/OQ 392089 8.0500 S
222 0 2 Bracken, Mr. James H male 27.00 0 0 220367 13.0000 S
223 0 3 Green, Mr. George Henry male 51.00 0 0 21440 8.0500 S
224 0 3 Nenkoff, Mr. Christo male NA 0 0 349234 7.8958 S
225 1 1 Hoyt, Mr. Frederick Maxfield male 38.00 1 0 19943 90.0000 C93 S
226 0 3 Berglund, Mr. Karl Ivar Sven male 22.00 0 0 PP 4348 9.3500 S
227 1 2 Mellors, Mr. William John male 19.00 0 0 SW/PP 751 10.5000 S
228 0 3 Lovell, Mr. John Hall (“Henry”) male 20.50 0 0 A/5 21173 7.2500 S
229 0 2 Fahlstrom, Mr. Arne Jonas male 18.00 0 0 236171 13.0000 S
230 0 3 Lefebre, Miss. Mathilde female NA 3 1 4133 25.4667 S
231 1 1 Harris, Mrs. Henry Birkhardt (Irene Wallach) female 35.00 1 0 36973 83.4750 C83 S
232 0 3 Larsson, Mr. Bengt Edvin male 29.00 0 0 347067 7.7750 S
233 0 2 Sjostedt, Mr. Ernst Adolf male 59.00 0 0 237442 13.5000 S
234 1 3 Asplund, Miss. Lillian Gertrud female 5.00 4 2 347077 31.3875 S
235 0 2 Leyson, Mr. Robert William Norman male 24.00 0 0 C.A. 29566 10.5000 S
236 0 3 Harknett, Miss. Alice Phoebe female NA 0 0 W./C. 6609 7.5500 S
237 0 2 Hold, Mr. Stephen male 44.00 1 0 26707 26.0000 S
238 1 2 Collyer, Miss. Marjorie “Lottie” female 8.00 0 2 C.A. 31921 26.2500 S
239 0 2 Pengelly, Mr. Frederick William male 19.00 0 0 28665 10.5000 S
240 0 2 Hunt, Mr. George Henry male 33.00 0 0 SCO/W 1585 12.2750 S
241 0 3 Zabour, Miss. Thamine female NA 1 0 2665 14.4542 C
242 1 3 Murphy, Miss. Katherine “Kate” female NA 1 0 367230 15.5000 Q
243 0 2 Coleridge, Mr. Reginald Charles male 29.00 0 0 W./C. 14263 10.5000 S
244 0 3 Maenpaa, Mr. Matti Alexanteri male 22.00 0 0 STON/O 2. 3101275 7.1250 S
245 0 3 Attalah, Mr. Sleiman male 30.00 0 0 2694 7.2250 C
246 0 1 Minahan, Dr. William Edward male 44.00 2 0 19928 90.0000 C78 Q
247 0 3 Lindahl, Miss. Agda Thorilda Viktoria female 25.00 0 0 347071 7.7750 S
248 1 2 Hamalainen, Mrs. William (Anna) female 24.00 0 2 250649 14.5000 S
249 1 1 Beckwith, Mr. Richard Leonard male 37.00 1 1 11751 52.5542 D35 S
250 0 2 Carter, Rev. Ernest Courtenay male 54.00 1 0 244252 26.0000 S
251 0 3 Reed, Mr. James George male NA 0 0 362316 7.2500 S
252 0 3 Strom, Mrs. Wilhelm (Elna Matilda Persson) female 29.00 1 1 347054 10.4625 G6 S
253 0 1 Stead, Mr. William Thomas male 62.00 0 0 113514 26.5500 C87 S
254 0 3 Lobb, Mr. William Arthur male 30.00 1 0 A/5. 3336 16.1000 S
255 0 3 Rosblom, Mrs. Viktor (Helena Wilhelmina) female 41.00 0 2 370129 20.2125 S
256 1 3 Touma, Mrs. Darwis (Hanne Youssef Razi) female 29.00 0 2 2650 15.2458 C
257 1 1 Thorne, Mrs. Gertrude Maybelle female NA 0 0 PC 17585 79.2000 C
258 1 1 Cherry, Miss. Gladys female 30.00 0 0 110152 86.5000 B77 S
259 1 1 Ward, Miss. Anna female 35.00 0 0 PC 17755 512.3292 C
260 1 2 Parrish, Mrs. (Lutie Davis) female 50.00 0 1 230433 26.0000 S
261 0 3 Smith, Mr. Thomas male NA 0 0 384461 7.7500 Q
262 1 3 Asplund, Master. Edvin Rojj Felix male 3.00 4 2 347077 31.3875 S
263 0 1 Taussig, Mr. Emil male 52.00 1 1 110413 79.6500 E67 S
264 0 1 Harrison, Mr. William male 40.00 0 0 112059 0.0000 B94 S
265 0 3 Henry, Miss. Delia female NA 0 0 382649 7.7500 Q
266 0 2 Reeves, Mr. David male 36.00 0 0 C.A. 17248 10.5000 S
267 0 3 Panula, Mr. Ernesti Arvid male 16.00 4 1 3101295 39.6875 S
268 1 3 Persson, Mr. Ernst Ulrik male 25.00 1 0 347083 7.7750 S
269 1 1 Graham, Mrs. William Thompson (Edith Junkins) female 58.00 0 1 PC 17582 153.4625 C125 S
270 1 1 Bissette, Miss. Amelia female 35.00 0 0 PC 17760 135.6333 C99 S
271 0 1 Cairns, Mr. Alexander male NA 0 0 113798 31.0000 S
272 1 3 Tornquist, Mr. William Henry male 25.00 0 0 LINE 0.0000 S
273 1 2 Mellinger, Mrs. (Elizabeth Anne Maidment) female 41.00 0 1 250644 19.5000 S
274 0 1 Natsch, Mr. Charles H male 37.00 0 1 PC 17596 29.7000 C118 C
275 1 3 Healy, Miss. Hanora “Nora” female NA 0 0 370375 7.7500 Q
276 1 1 Andrews, Miss. Kornelia Theodosia female 63.00 1 0 13502 77.9583 D7 S
277 0 3 Lindblom, Miss. Augusta Charlotta female 45.00 0 0 347073 7.7500 S
278 0 2 Parkes, Mr. Francis “Frank” male NA 0 0 239853 0.0000 S
279 0 3 Rice, Master. Eric male 7.00 4 1 382652 29.1250 Q
280 1 3 Abbott, Mrs. Stanton (Rosa Hunt) female 35.00 1 1 C.A. 2673 20.2500 S
281 0 3 Duane, Mr. Frank male 65.00 0 0 336439 7.7500 Q
282 0 3 Olsson, Mr. Nils Johan Goransson male 28.00 0 0 347464 7.8542 S
283 0 3 de Pelsmaeker, Mr. Alfons male 16.00 0 0 345778 9.5000 S
284 1 3 Dorking, Mr. Edward Arthur male 19.00 0 0 A/5. 10482 8.0500 S
285 0 1 Smith, Mr. Richard William male NA 0 0 113056 26.0000 A19 S
286 0 3 Stankovic, Mr. Ivan male 33.00 0 0 349239 8.6625 C
287 1 3 de Mulder, Mr. Theodore male 30.00 0 0 345774 9.5000 S
288 0 3 Naidenoff, Mr. Penko male 22.00 0 0 349206 7.8958 S
289 1 2 Hosono, Mr. Masabumi male 42.00 0 0 237798 13.0000 S
290 1 3 Connolly, Miss. Kate female 22.00 0 0 370373 7.7500 Q
291 1 1 Barber, Miss. Ellen “Nellie” female 26.00 0 0 19877 78.8500 S
292 1 1 Bishop, Mrs. Dickinson H (Helen Walton) female 19.00 1 0 11967 91.0792 B49 C
293 0 2 Levy, Mr. Rene Jacques male 36.00 0 0 SC/Paris 2163 12.8750 D C
294 0 3 Haas, Miss. Aloisia female 24.00 0 0 349236 8.8500 S
295 0 3 Mineff, Mr. Ivan male 24.00 0 0 349233 7.8958 S
296 0 1 Lewy, Mr. Ervin G male NA 0 0 PC 17612 27.7208 C
297 0 3 Hanna, Mr. Mansour male 23.50 0 0 2693 7.2292 C
298 0 1 Allison, Miss. Helen Loraine female 2.00 1 2 113781 151.5500 C22 C26 S
299 1 1 Saalfeld, Mr. Adolphe male NA 0 0 19988 30.5000 C106 S
300 1 1 Baxter, Mrs. James (Helene DeLaudeniere Chaput) female 50.00 0 1 PC 17558 247.5208 B58 B60 C
301 1 3 Kelly, Miss. Anna Katherine “Annie Kate” female NA 0 0 9234 7.7500 Q
302 1 3 McCoy, Mr. Bernard male NA 2 0 367226 23.2500 Q
303 0 3 Johnson, Mr. William Cahoone Jr male 19.00 0 0 LINE 0.0000 S
304 1 2 Keane, Miss. Nora A female NA 0 0 226593 12.3500 E101 Q
305 0 3 Williams, Mr. Howard Hugh “Harry” male NA 0 0 A/5 2466 8.0500 S
306 1 1 Allison, Master. Hudson Trevor male 0.92 1 2 113781 151.5500 C22 C26 S
307 1 1 Fleming, Miss. Margaret female NA 0 0 17421 110.8833 C
308 1 1 Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo) female 17.00 1 0 PC 17758 108.9000 C65 C
309 0 2 Abelson, Mr. Samuel male 30.00 1 0 P/PP 3381 24.0000 C
310 1 1 Francatelli, Miss. Laura Mabel female 30.00 0 0 PC 17485 56.9292 E36 C
311 1 1 Hays, Miss. Margaret Bechstein female 24.00 0 0 11767 83.1583 C54 C
312 1 1 Ryerson, Miss. Emily Borie female 18.00 2 2 PC 17608 262.3750 B57 B59 B63 B66 C
313 0 2 Lahtinen, Mrs. William (Anna Sylfven) female 26.00 1 1 250651 26.0000 S
314 0 3 Hendekovic, Mr. Ignjac male 28.00 0 0 349243 7.8958 S
315 0 2 Hart, Mr. Benjamin male 43.00 1 1 F.C.C. 13529 26.2500 S
316 1 3 Nilsson, Miss. Helmina Josefina female 26.00 0 0 347470 7.8542 S
317 1 2 Kantor, Mrs. Sinai (Miriam Sternin) female 24.00 1 0 244367 26.0000 S
318 0 2 Moraweck, Dr. Ernest male 54.00 0 0 29011 14.0000 S
319 1 1 Wick, Miss. Mary Natalie female 31.00 0 2 36928 164.8667 C7 S
320 1 1 Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone) female 40.00 1 1 16966 134.5000 E34 C
321 0 3 Dennis, Mr. Samuel male 22.00 0 0 A/5 21172 7.2500 S
322 0 3 Danoff, Mr. Yoto male 27.00 0 0 349219 7.8958 S
323 1 2 Slayter, Miss. Hilda Mary female 30.00 0 0 234818 12.3500 Q
324 1 2 Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh) female 22.00 1 1 248738 29.0000 S
325 0 3 Sage, Mr. George John Jr male NA 8 2 CA. 2343 69.5500 S
326 1 1 Young, Miss. Marie Grice female 36.00 0 0 PC 17760 135.6333 C32 C
327 0 3 Nysveen, Mr. Johan Hansen male 61.00 0 0 345364 6.2375 S
328 1 2 Ball, Mrs. (Ada E Hall) female 36.00 0 0 28551 13.0000 D S
329 1 3 Goldsmith, Mrs. Frank John (Emily Alice Brown) female 31.00 1 1 363291 20.5250 S
330 1 1 Hippach, Miss. Jean Gertrude female 16.00 0 1 111361 57.9792 B18 C
331 1 3 McCoy, Miss. Agnes female NA 2 0 367226 23.2500 Q
332 0 1 Partner, Mr. Austen male 45.50 0 0 113043 28.5000 C124 S
333 0 1 Graham, Mr. George Edward male 38.00 0 1 PC 17582 153.4625 C91 S
334 0 3 Vander Planke, Mr. Leo Edmondus male 16.00 2 0 345764 18.0000 S
335 1 1 Frauenthal, Mrs. Henry William (Clara Heinsheimer) female NA 1 0 PC 17611 133.6500 S
336 0 3 Denkoff, Mr. Mitto male NA 0 0 349225 7.8958 S
337 0 1 Pears, Mr. Thomas Clinton male 29.00 1 0 113776 66.6000 C2 S
338 1 1 Burns, Miss. Elizabeth Margaret female 41.00 0 0 16966 134.5000 E40 C
339 1 3 Dahl, Mr. Karl Edwart male 45.00 0 0 7598 8.0500 S
340 0 1 Blackwell, Mr. Stephen Weart male 45.00 0 0 113784 35.5000 T S
341 1 2 Navratil, Master. Edmond Roger male 2.00 1 1 230080 26.0000 F2 S
342 1 1 Fortune, Miss. Alice Elizabeth female 24.00 3 2 19950 263.0000 C23 C25 C27 S
343 0 2 Collander, Mr. Erik Gustaf male 28.00 0 0 248740 13.0000 S
344 0 2 Sedgwick, Mr. Charles Frederick Waddington male 25.00 0 0 244361 13.0000 S
345 0 2 Fox, Mr. Stanley Hubert male 36.00 0 0 229236 13.0000 S
346 1 2 Brown, Miss. Amelia “Mildred” female 24.00 0 0 248733 13.0000 F33 S
347 1 2 Smith, Miss. Marion Elsie female 40.00 0 0 31418 13.0000 S
348 1 3 Davison, Mrs. Thomas Henry (Mary E Finck) female NA 1 0 386525 16.1000 S
349 1 3 Coutts, Master. William Loch “William” male 3.00 1 1 C.A. 37671 15.9000 S
350 0 3 Dimic, Mr. Jovan male 42.00 0 0 315088 8.6625 S
351 0 3 Odahl, Mr. Nils Martin male 23.00 0 0 7267 9.2250 S
352 0 1 Williams-Lambert, Mr. Fletcher Fellows male NA 0 0 113510 35.0000 C128 S
353 0 3 Elias, Mr. Tannous male 15.00 1 1 2695 7.2292 C
354 0 3 Arnold-Franchi, Mr. Josef male 25.00 1 0 349237 17.8000 S
355 0 3 Yousif, Mr. Wazli male NA 0 0 2647 7.2250 C
356 0 3 Vanden Steen, Mr. Leo Peter male 28.00 0 0 345783 9.5000 S
357 1 1 Bowerman, Miss. Elsie Edith female 22.00 0 1 113505 55.0000 E33 S
358 0 2 Funk, Miss. Annie Clemmer female 38.00 0 0 237671 13.0000 S
359 1 3 McGovern, Miss. Mary female NA 0 0 330931 7.8792 Q
360 1 3 Mockler, Miss. Helen Mary “Ellie” female NA 0 0 330980 7.8792 Q
361 0 3 Skoog, Mr. Wilhelm male 40.00 1 4 347088 27.9000 S
362 0 2 del Carlo, Mr. Sebastiano male 29.00 1 0 SC/PARIS 2167 27.7208 C
363 0 3 Barbara, Mrs. (Catherine David) female 45.00 0 1 2691 14.4542 C
364 0 3 Asim, Mr. Adola male 35.00 0 0 SOTON/O.Q. 3101310 7.0500 S
365 0 3 O’Brien, Mr. Thomas male NA 1 0 370365 15.5000 Q
366 0 3 Adahl, Mr. Mauritz Nils Martin male 30.00 0 0 C 7076 7.2500 S
367 1 1 Warren, Mrs. Frank Manley (Anna Sophia Atkinson) female 60.00 1 0 110813 75.2500 D37 C
368 1 3 Moussa, Mrs. (Mantoura Boulos) female NA 0 0 2626 7.2292 C
369 1 3 Jermyn, Miss. Annie female NA 0 0 14313 7.7500 Q
370 1 1 Aubart, Mme. Leontine Pauline female 24.00 0 0 PC 17477 69.3000 B35 C
371 1 1 Harder, Mr. George Achilles male 25.00 1 0 11765 55.4417 E50 C
372 0 3 Wiklund, Mr. Jakob Alfred male 18.00 1 0 3101267 6.4958 S
373 0 3 Beavan, Mr. William Thomas male 19.00 0 0 323951 8.0500 S
374 0 1 Ringhini, Mr. Sante male 22.00 0 0 PC 17760 135.6333 C
375 0 3 Palsson, Miss. Stina Viola female 3.00 3 1 349909 21.0750 S
376 1 1 Meyer, Mrs. Edgar Joseph (Leila Saks) female NA 1 0 PC 17604 82.1708 C
377 1 3 Landergren, Miss. Aurora Adelia female 22.00 0 0 C 7077 7.2500 S
378 0 1 Widener, Mr. Harry Elkins male 27.00 0 2 113503 211.5000 C82 C
379 0 3 Betros, Mr. Tannous male 20.00 0 0 2648 4.0125 C
380 0 3 Gustafsson, Mr. Karl Gideon male 19.00 0 0 347069 7.7750 S
381 1 1 Bidois, Miss. Rosalie female 42.00 0 0 PC 17757 227.5250 C
382 1 3 Nakid, Miss. Maria (“Mary”) female 1.00 0 2 2653 15.7417 C
383 0 3 Tikkanen, Mr. Juho male 32.00 0 0 STON/O 2. 3101293 7.9250 S
384 1 1 Holverson, Mrs. Alexander Oskar (Mary Aline Towner) female 35.00 1 0 113789 52.0000 S
385 0 3 Plotcharsky, Mr. Vasil male NA 0 0 349227 7.8958 S
386 0 2 Davies, Mr. Charles Henry male 18.00 0 0 S.O.C. 14879 73.5000 S
387 0 3 Goodwin, Master. Sidney Leonard male 1.00 5 2 CA 2144 46.9000 S
388 1 2 Buss, Miss. Kate female 36.00 0 0 27849 13.0000 S
389 0 3 Sadlier, Mr. Matthew male NA 0 0 367655 7.7292 Q
390 1 2 Lehmann, Miss. Bertha female 17.00 0 0 SC 1748 12.0000 C
391 1 1 Carter, Mr. William Ernest male 36.00 1 2 113760 120.0000 B96 B98 S
392 1 3 Jansson, Mr. Carl Olof male 21.00 0 0 350034 7.7958 S
393 0 3 Gustafsson, Mr. Johan Birger male 28.00 2 0 3101277 7.9250 S
394 1 1 Newell, Miss. Marjorie female 23.00 1 0 35273 113.2750 D36 C
395 1 3 Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson) female 24.00 0 2 PP 9549 16.7000 G6 S
396 0 3 Johansson, Mr. Erik male 22.00 0 0 350052 7.7958 S
397 0 3 Olsson, Miss. Elina female 31.00 0 0 350407 7.8542 S
398 0 2 McKane, Mr. Peter David male 46.00 0 0 28403 26.0000 S
399 0 2 Pain, Dr. Alfred male 23.00 0 0 244278 10.5000 S
400 1 2 Trout, Mrs. William H (Jessie L) female 28.00 0 0 240929 12.6500 S
401 1 3 Niskanen, Mr. Juha male 39.00 0 0 STON/O 2. 3101289 7.9250 S
402 0 3 Adams, Mr. John male 26.00 0 0 341826 8.0500 S
403 0 3 Jussila, Miss. Mari Aina female 21.00 1 0 4137 9.8250 S
404 0 3 Hakkarainen, Mr. Pekka Pietari male 28.00 1 0 STON/O2. 3101279 15.8500 S
405 0 3 Oreskovic, Miss. Marija female 20.00 0 0 315096 8.6625 S
406 0 2 Gale, Mr. Shadrach male 34.00 1 0 28664 21.0000 S
407 0 3 Widegren, Mr. Carl/Charles Peter male 51.00 0 0 347064 7.7500 S
408 1 2 Richards, Master. William Rowe male 3.00 1 1 29106 18.7500 S
409 0 3 Birkeland, Mr. Hans Martin Monsen male 21.00 0 0 312992 7.7750 S
410 0 3 Lefebre, Miss. Ida female NA 3 1 4133 25.4667 S
411 0 3 Sdycoff, Mr. Todor male NA 0 0 349222 7.8958 S
412 0 3 Hart, Mr. Henry male NA 0 0 394140 6.8583 Q
413 1 1 Minahan, Miss. Daisy E female 33.00 1 0 19928 90.0000 C78 Q
414 0 2 Cunningham, Mr. Alfred Fleming male NA 0 0 239853 0.0000 S
415 1 3 Sundman, Mr. Johan Julian male 44.00 0 0 STON/O 2. 3101269 7.9250 S
416 0 3 Meek, Mrs. Thomas (Annie Louise Rowley) female NA 0 0 343095 8.0500 S
417 1 2 Drew, Mrs. James Vivian (Lulu Thorne Christian) female 34.00 1 1 28220 32.5000 S
418 1 2 Silven, Miss. Lyyli Karoliina female 18.00 0 2 250652 13.0000 S
419 0 2 Matthews, Mr. William John male 30.00 0 0 28228 13.0000 S
420 0 3 Van Impe, Miss. Catharina female 10.00 0 2 345773 24.1500 S
421 0 3 Gheorgheff, Mr. Stanio male NA 0 0 349254 7.8958 C
422 0 3 Charters, Mr. David male 21.00 0 0 A/5. 13032 7.7333 Q
423 0 3 Zimmerman, Mr. Leo male 29.00 0 0 315082 7.8750 S
424 0 3 Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren) female 28.00 1 1 347080 14.4000 S
425 0 3 Rosblom, Mr. Viktor Richard male 18.00 1 1 370129 20.2125 S
426 0 3 Wiseman, Mr. Phillippe male NA 0 0 A/4. 34244 7.2500 S
427 1 2 Clarke, Mrs. Charles V (Ada Maria Winfield) female 28.00 1 0 2003 26.0000 S
428 1 2 Phillips, Miss. Kate Florence (“Mrs Kate Louise Phillips Marshall”) female 19.00 0 0 250655 26.0000 S
429 0 3 Flynn, Mr. James male NA 0 0 364851 7.7500 Q
430 1 3 Pickard, Mr. Berk (Berk Trembisky) male 32.00 0 0 SOTON/O.Q. 392078 8.0500 E10 S
431 1 1 Bjornstrom-Steffansson, Mr. Mauritz Hakan male 28.00 0 0 110564 26.5500 C52 S
432 1 3 Thorneycroft, Mrs. Percival (Florence Kate White) female NA 1 0 376564 16.1000 S
433 1 2 Louch, Mrs. Charles Alexander (Alice Adelaide Slow) female 42.00 1 0 SC/AH 3085 26.0000 S
434 0 3 Kallio, Mr. Nikolai Erland male 17.00 0 0 STON/O 2. 3101274 7.1250 S
435 0 1 Silvey, Mr. William Baird male 50.00 1 0 13507 55.9000 E44 S
436 1 1 Carter, Miss. Lucile Polk female 14.00 1 2 113760 120.0000 B96 B98 S
437 0 3 Ford, Miss. Doolina Margaret “Daisy” female 21.00 2 2 W./C. 6608 34.3750 S
438 1 2 Richards, Mrs. Sidney (Emily Hocking) female 24.00 2 3 29106 18.7500 S
439 0 1 Fortune, Mr. Mark male 64.00 1 4 19950 263.0000 C23 C25 C27 S
440 0 2 Kvillner, Mr. Johan Henrik Johannesson male 31.00 0 0 C.A. 18723 10.5000 S
441 1 2 Hart, Mrs. Benjamin (Esther Ada Bloomfield) female 45.00 1 1 F.C.C. 13529 26.2500 S
442 0 3 Hampe, Mr. Leon male 20.00 0 0 345769 9.5000 S
443 0 3 Petterson, Mr. Johan Emil male 25.00 1 0 347076 7.7750 S
444 1 2 Reynaldo, Ms. Encarnacion female 28.00 0 0 230434 13.0000 S
445 1 3 Johannesen-Bratthammer, Mr. Bernt male NA 0 0 65306 8.1125 S
446 1 1 Dodge, Master. Washington male 4.00 0 2 33638 81.8583 A34 S
447 1 2 Mellinger, Miss. Madeleine Violet female 13.00 0 1 250644 19.5000 S
448 1 1 Seward, Mr. Frederic Kimber male 34.00 0 0 113794 26.5500 S
449 1 3 Baclini, Miss. Marie Catherine female 5.00 2 1 2666 19.2583 C
450 1 1 Peuchen, Major. Arthur Godfrey male 52.00 0 0 113786 30.5000 C104 S
451 0 2 West, Mr. Edwy Arthur male 36.00 1 2 C.A. 34651 27.7500 S
452 0 3 Hagland, Mr. Ingvald Olai Olsen male NA 1 0 65303 19.9667 S
453 0 1 Foreman, Mr. Benjamin Laventall male 30.00 0 0 113051 27.7500 C111 C
454 1 1 Goldenberg, Mr. Samuel L male 49.00 1 0 17453 89.1042 C92 C
455 0 3 Peduzzi, Mr. Joseph male NA 0 0 A/5 2817 8.0500 S
456 1 3 Jalsevac, Mr. Ivan male 29.00 0 0 349240 7.8958 C
457 0 1 Millet, Mr. Francis Davis male 65.00 0 0 13509 26.5500 E38 S
458 1 1 Kenyon, Mrs. Frederick R (Marion) female NA 1 0 17464 51.8625 D21 S
459 1 2 Toomey, Miss. Ellen female 50.00 0 0 F.C.C. 13531 10.5000 S
460 0 3 O’Connor, Mr. Maurice male NA 0 0 371060 7.7500 Q
461 1 1 Anderson, Mr. Harry male 48.00 0 0 19952 26.5500 E12 S
462 0 3 Morley, Mr. William male 34.00 0 0 364506 8.0500 S
463 0 1 Gee, Mr. Arthur H male 47.00 0 0 111320 38.5000 E63 S
464 0 2 Milling, Mr. Jacob Christian male 48.00 0 0 234360 13.0000 S
465 0 3 Maisner, Mr. Simon male NA 0 0 A/S 2816 8.0500 S
466 0 3 Goncalves, Mr. Manuel Estanslas male 38.00 0 0 SOTON/O.Q. 3101306 7.0500 S
467 0 2 Campbell, Mr. William male NA 0 0 239853 0.0000 S
468 0 1 Smart, Mr. John Montgomery male 56.00 0 0 113792 26.5500 S
469 0 3 Scanlan, Mr. James male NA 0 0 36209 7.7250 Q
470 1 3 Baclini, Miss. Helene Barbara female 0.75 2 1 2666 19.2583 C
471 0 3 Keefe, Mr. Arthur male NA 0 0 323592 7.2500 S
472 0 3 Cacic, Mr. Luka male 38.00 0 0 315089 8.6625 S
473 1 2 West, Mrs. Edwy Arthur (Ada Mary Worth) female 33.00 1 2 C.A. 34651 27.7500 S
474 1 2 Jerwan, Mrs. Amin S (Marie Marthe Thuillard) female 23.00 0 0 SC/AH Basle 541 13.7917 D C
475 0 3 Strandberg, Miss. Ida Sofia female 22.00 0 0 7553 9.8375 S
476 0 1 Clifford, Mr. George Quincy male NA 0 0 110465 52.0000 A14 S
477 0 2 Renouf, Mr. Peter Henry male 34.00 1 0 31027 21.0000 S
478 0 3 Braund, Mr. Lewis Richard male 29.00 1 0 3460 7.0458 S
479 0 3 Karlsson, Mr. Nils August male 22.00 0 0 350060 7.5208 S
480 1 3 Hirvonen, Miss. Hildur E female 2.00 0 1 3101298 12.2875 S
481 0 3 Goodwin, Master. Harold Victor male 9.00 5 2 CA 2144 46.9000 S
482 0 2 Frost, Mr. Anthony Wood “Archie” male NA 0 0 239854 0.0000 S
483 0 3 Rouse, Mr. Richard Henry male 50.00 0 0 A/5 3594 8.0500 S
484 1 3 Turkula, Mrs. (Hedwig) female 63.00 0 0 4134 9.5875 S
485 1 1 Bishop, Mr. Dickinson H male 25.00 1 0 11967 91.0792 B49 C
486 0 3 Lefebre, Miss. Jeannie female NA 3 1 4133 25.4667 S
487 1 1 Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby) female 35.00 1 0 19943 90.0000 C93 S
488 0 1 Kent, Mr. Edward Austin male 58.00 0 0 11771 29.7000 B37 C
489 0 3 Somerton, Mr. Francis William male 30.00 0 0 A.5. 18509 8.0500 S
490 1 3 Coutts, Master. Eden Leslie “Neville” male 9.00 1 1 C.A. 37671 15.9000 S
491 0 3 Hagland, Mr. Konrad Mathias Reiersen male NA 1 0 65304 19.9667 S
492 0 3 Windelov, Mr. Einar male 21.00 0 0 SOTON/OQ 3101317 7.2500 S
493 0 1 Molson, Mr. Harry Markland male 55.00 0 0 113787 30.5000 C30 S
494 0 1 Artagaveytia, Mr. Ramon male 71.00 0 0 PC 17609 49.5042 C
495 0 3 Stanley, Mr. Edward Roland male 21.00 0 0 A/4 45380 8.0500 S
496 0 3 Yousseff, Mr. Gerious male NA 0 0 2627 14.4583 C
497 1 1 Eustis, Miss. Elizabeth Mussey female 54.00 1 0 36947 78.2667 D20 C
498 0 3 Shellard, Mr. Frederick William male NA 0 0 C.A. 6212 15.1000 S
499 0 1 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female 25.00 1 2 113781 151.5500 C22 C26 S
500 0 3 Svensson, Mr. Olof male 24.00 0 0 350035 7.7958 S
501 0 3 Calic, Mr. Petar male 17.00 0 0 315086 8.6625 S
502 0 3 Canavan, Miss. Mary female 21.00 0 0 364846 7.7500 Q
503 0 3 O’Sullivan, Miss. Bridget Mary female NA 0 0 330909 7.6292 Q
504 0 3 Laitinen, Miss. Kristina Sofia female 37.00 0 0 4135 9.5875 S
505 1 1 Maioni, Miss. Roberta female 16.00 0 0 110152 86.5000 B79 S
506 0 1 Penasco y Castellana, Mr. Victor de Satode male 18.00 1 0 PC 17758 108.9000 C65 C
507 1 2 Quick, Mrs. Frederick Charles (Jane Richards) female 33.00 0 2 26360 26.0000 S
508 1 1 Bradley, Mr. George (“George Arthur Brayton”) male NA 0 0 111427 26.5500 S
509 0 3 Olsen, Mr. Henry Margido male 28.00 0 0 C 4001 22.5250 S
510 1 3 Lang, Mr. Fang male 26.00 0 0 1601 56.4958 S
511 1 3 Daly, Mr. Eugene Patrick male 29.00 0 0 382651 7.7500 Q
512 0 3 Webber, Mr. James male NA 0 0 SOTON/OQ 3101316 8.0500 S
513 1 1 McGough, Mr. James Robert male 36.00 0 0 PC 17473 26.2875 E25 S
514 1 1 Rothschild, Mrs. Martin (Elizabeth L. Barrett) female 54.00 1 0 PC 17603 59.4000 C
515 0 3 Coleff, Mr. Satio male 24.00 0 0 349209 7.4958 S
516 0 1 Walker, Mr. William Anderson male 47.00 0 0 36967 34.0208 D46 S
517 1 2 Lemore, Mrs. (Amelia Milley) female 34.00 0 0 C.A. 34260 10.5000 F33 S
518 0 3 Ryan, Mr. Patrick male NA 0 0 371110 24.1500 Q
519 1 2 Angle, Mrs. William A (Florence “Mary” Agnes Hughes) female 36.00 1 0 226875 26.0000 S
520 0 3 Pavlovic, Mr. Stefo male 32.00 0 0 349242 7.8958 S
521 1 1 Perreault, Miss. Anne female 30.00 0 0 12749 93.5000 B73 S
522 0 3 Vovk, Mr. Janko male 22.00 0 0 349252 7.8958 S
523 0 3 Lahoud, Mr. Sarkis male NA 0 0 2624 7.2250 C
524 1 1 Hippach, Mrs. Louis Albert (Ida Sophia Fischer) female 44.00 0 1 111361 57.9792 B18 C
525 0 3 Kassem, Mr. Fared male NA 0 0 2700 7.2292 C
526 0 3 Farrell, Mr. James male 40.50 0 0 367232 7.7500 Q
527 1 2 Ridsdale, Miss. Lucy female 50.00 0 0 W./C. 14258 10.5000 S
528 0 1 Farthing, Mr. John male NA 0 0 PC 17483 221.7792 C95 S
529 0 3 Salonen, Mr. Johan Werner male 39.00 0 0 3101296 7.9250 S
530 0 2 Hocking, Mr. Richard George male 23.00 2 1 29104 11.5000 S
531 1 2 Quick, Miss. Phyllis May female 2.00 1 1 26360 26.0000 S
532 0 3 Toufik, Mr. Nakli male NA 0 0 2641 7.2292 C
533 0 3 Elias, Mr. Joseph Jr male 17.00 1 1 2690 7.2292 C
534 1 3 Peter, Mrs. Catherine (Catherine Rizk) female NA 0 2 2668 22.3583 C
535 0 3 Cacic, Miss. Marija female 30.00 0 0 315084 8.6625 S
536 1 2 Hart, Miss. Eva Miriam female 7.00 0 2 F.C.C. 13529 26.2500 S
537 0 1 Butt, Major. Archibald Willingham male 45.00 0 0 113050 26.5500 B38 S
538 1 1 LeRoy, Miss. Bertha female 30.00 0 0 PC 17761 106.4250 C
539 0 3 Risien, Mr. Samuel Beard male NA 0 0 364498 14.5000 S
540 1 1 Frolicher, Miss. Hedwig Margaritha female 22.00 0 2 13568 49.5000 B39 C
541 1 1 Crosby, Miss. Harriet R female 36.00 0 2 WE/P 5735 71.0000 B22 S
542 0 3 Andersson, Miss. Ingeborg Constanzia female 9.00 4 2 347082 31.2750 S
543 0 3 Andersson, Miss. Sigrid Elisabeth female 11.00 4 2 347082 31.2750 S
544 1 2 Beane, Mr. Edward male 32.00 1 0 2908 26.0000 S
545 0 1 Douglas, Mr. Walter Donald male 50.00 1 0 PC 17761 106.4250 C86 C
546 0 1 Nicholson, Mr. Arthur Ernest male 64.00 0 0 693 26.0000 S
547 1 2 Beane, Mrs. Edward (Ethel Clarke) female 19.00 1 0 2908 26.0000 S
548 1 2 Padro y Manent, Mr. Julian male NA 0 0 SC/PARIS 2146 13.8625 C
549 0 3 Goldsmith, Mr. Frank John male 33.00 1 1 363291 20.5250 S
550 1 2 Davies, Master. John Morgan Jr male 8.00 1 1 C.A. 33112 36.7500 S
551 1 1 Thayer, Mr. John Borland Jr male 17.00 0 2 17421 110.8833 C70 C
552 0 2 Sharp, Mr. Percival James R male 27.00 0 0 244358 26.0000 S
553 0 3 O’Brien, Mr. Timothy male NA 0 0 330979 7.8292 Q
554 1 3 Leeni, Mr. Fahim (“Philip Zenni”) male 22.00 0 0 2620 7.2250 C
555 1 3 Ohman, Miss. Velin female 22.00 0 0 347085 7.7750 S
556 0 1 Wright, Mr. George male 62.00 0 0 113807 26.5500 S
557 1 1 Duff Gordon, Lady. (Lucille Christiana Sutherland) (“Mrs Morgan”) female 48.00 1 0 11755 39.6000 A16 C
558 0 1 Robbins, Mr. Victor male NA 0 0 PC 17757 227.5250 C
559 1 1 Taussig, Mrs. Emil (Tillie Mandelbaum) female 39.00 1 1 110413 79.6500 E67 S
560 1 3 de Messemaeker, Mrs. Guillaume Joseph (Emma) female 36.00 1 0 345572 17.4000 S
561 0 3 Morrow, Mr. Thomas Rowan male NA 0 0 372622 7.7500 Q
562 0 3 Sivic, Mr. Husein male 40.00 0 0 349251 7.8958 S
563 0 2 Norman, Mr. Robert Douglas male 28.00 0 0 218629 13.5000 S
564 0 3 Simmons, Mr. John male NA 0 0 SOTON/OQ 392082 8.0500 S
565 0 3 Meanwell, Miss. (Marion Ogden) female NA 0 0 SOTON/O.Q. 392087 8.0500 S
566 0 3 Davies, Mr. Alfred J male 24.00 2 0 A/4 48871 24.1500 S
567 0 3 Stoytcheff, Mr. Ilia male 19.00 0 0 349205 7.8958 S
568 0 3 Palsson, Mrs. Nils (Alma Cornelia Berglund) female 29.00 0 4 349909 21.0750 S
569 0 3 Doharr, Mr. Tannous male NA 0 0 2686 7.2292 C
570 1 3 Jonsson, Mr. Carl male 32.00 0 0 350417 7.8542 S
571 1 2 Harris, Mr. George male 62.00 0 0 S.W./PP 752 10.5000 S
572 1 1 Appleton, Mrs. Edward Dale (Charlotte Lamson) female 53.00 2 0 11769 51.4792 C101 S
573 1 1 Flynn, Mr. John Irwin (“Irving”) male 36.00 0 0 PC 17474 26.3875 E25 S
574 1 3 Kelly, Miss. Mary female NA 0 0 14312 7.7500 Q
575 0 3 Rush, Mr. Alfred George John male 16.00 0 0 A/4. 20589 8.0500 S
576 0 3 Patchett, Mr. George male 19.00 0 0 358585 14.5000 S
577 1 2 Garside, Miss. Ethel female 34.00 0 0 243880 13.0000 S
578 1 1 Silvey, Mrs. William Baird (Alice Munger) female 39.00 1 0 13507 55.9000 E44 S
579 0 3 Caram, Mrs. Joseph (Maria Elias) female NA 1 0 2689 14.4583 C
580 1 3 Jussila, Mr. Eiriik male 32.00 0 0 STON/O 2. 3101286 7.9250 S
581 1 2 Christy, Miss. Julie Rachel female 25.00 1 1 237789 30.0000 S
582 1 1 Thayer, Mrs. John Borland (Marian Longstreth Morris) female 39.00 1 1 17421 110.8833 C68 C
583 0 2 Downton, Mr. William James male 54.00 0 0 28403 26.0000 S
584 0 1 Ross, Mr. John Hugo male 36.00 0 0 13049 40.1250 A10 C
585 0 3 Paulner, Mr. Uscher male NA 0 0 3411 8.7125 C
586 1 1 Taussig, Miss. Ruth female 18.00 0 2 110413 79.6500 E68 S
587 0 2 Jarvis, Mr. John Denzil male 47.00 0 0 237565 15.0000 S
588 1 1 Frolicher-Stehli, Mr. Maxmillian male 60.00 1 1 13567 79.2000 B41 C
589 0 3 Gilinski, Mr. Eliezer male 22.00 0 0 14973 8.0500 S
590 0 3 Murdlin, Mr. Joseph male NA 0 0 A./5. 3235 8.0500 S
591 0 3 Rintamaki, Mr. Matti male 35.00 0 0 STON/O 2. 3101273 7.1250 S
592 1 1 Stephenson, Mrs. Walter Bertram (Martha Eustis) female 52.00 1 0 36947 78.2667 D20 C
593 0 3 Elsbury, Mr. William James male 47.00 0 0 A/5 3902 7.2500 S
594 0 3 Bourke, Miss. Mary female NA 0 2 364848 7.7500 Q
595 0 2 Chapman, Mr. John Henry male 37.00 1 0 SC/AH 29037 26.0000 S
596 0 3 Van Impe, Mr. Jean Baptiste male 36.00 1 1 345773 24.1500 S
597 1 2 Leitch, Miss. Jessie Wills female NA 0 0 248727 33.0000 S
598 0 3 Johnson, Mr. Alfred male 49.00 0 0 LINE 0.0000 S
599 0 3 Boulos, Mr. Hanna male NA 0 0 2664 7.2250 C
600 1 1 Duff Gordon, Sir. Cosmo Edmund (“Mr Morgan”) male 49.00 1 0 PC 17485 56.9292 A20 C
601 1 2 Jacobsohn, Mrs. Sidney Samuel (Amy Frances Christy) female 24.00 2 1 243847 27.0000 S
602 0 3 Slabenoff, Mr. Petco male NA 0 0 349214 7.8958 S
603 0 1 Harrington, Mr. Charles H male NA 0 0 113796 42.4000 S
604 0 3 Torber, Mr. Ernst William male 44.00 0 0 364511 8.0500 S
605 1 1 Homer, Mr. Harry (“Mr E Haven”) male 35.00 0 0 111426 26.5500 C
606 0 3 Lindell, Mr. Edvard Bengtsson male 36.00 1 0 349910 15.5500 S
607 0 3 Karaic, Mr. Milan male 30.00 0 0 349246 7.8958 S
608 1 1 Daniel, Mr. Robert Williams male 27.00 0 0 113804 30.5000 S
609 1 2 Laroche, Mrs. Joseph (Juliette Marie Louise Lafargue) female 22.00 1 2 SC/Paris 2123 41.5792 C
610 1 1 Shutes, Miss. Elizabeth W female 40.00 0 0 PC 17582 153.4625 C125 S
611 0 3 Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren) female 39.00 1 5 347082 31.2750 S
612 0 3 Jardin, Mr. Jose Neto male NA 0 0 SOTON/O.Q. 3101305 7.0500 S
613 1 3 Murphy, Miss. Margaret Jane female NA 1 0 367230 15.5000 Q
614 0 3 Horgan, Mr. John male NA 0 0 370377 7.7500 Q
615 0 3 Brocklebank, Mr. William Alfred male 35.00 0 0 364512 8.0500 S
616 1 2 Herman, Miss. Alice female 24.00 1 2 220845 65.0000 S
617 0 3 Danbom, Mr. Ernst Gilbert male 34.00 1 1 347080 14.4000 S
618 0 3 Lobb, Mrs. William Arthur (Cordelia K Stanlick) female 26.00 1 0 A/5. 3336 16.1000 S
619 1 2 Becker, Miss. Marion Louise female 4.00 2 1 230136 39.0000 F4 S
620 0 2 Gavey, Mr. Lawrence male 26.00 0 0 31028 10.5000 S
621 0 3 Yasbeck, Mr. Antoni male 27.00 1 0 2659 14.4542 C
622 1 1 Kimball, Mr. Edwin Nelson Jr male 42.00 1 0 11753 52.5542 D19 S
623 1 3 Nakid, Mr. Sahid male 20.00 1 1 2653 15.7417 C
624 0 3 Hansen, Mr. Henry Damsgaard male 21.00 0 0 350029 7.8542 S
625 0 3 Bowen, Mr. David John “Dai” male 21.00 0 0 54636 16.1000 S
626 0 1 Sutton, Mr. Frederick male 61.00 0 0 36963 32.3208 D50 S
627 0 2 Kirkland, Rev. Charles Leonard male 57.00 0 0 219533 12.3500 Q
628 1 1 Longley, Miss. Gretchen Fiske female 21.00 0 0 13502 77.9583 D9 S
629 0 3 Bostandyeff, Mr. Guentcho male 26.00 0 0 349224 7.8958 S
630 0 3 O’Connell, Mr. Patrick D male NA 0 0 334912 7.7333 Q
631 1 1 Barkworth, Mr. Algernon Henry Wilson male 80.00 0 0 27042 30.0000 A23 S
632 0 3 Lundahl, Mr. Johan Svensson male 51.00 0 0 347743 7.0542 S
633 1 1 Stahelin-Maeglin, Dr. Max male 32.00 0 0 13214 30.5000 B50 C
634 0 1 Parr, Mr. William Henry Marsh male NA 0 0 112052 0.0000 S
635 0 3 Skoog, Miss. Mabel female 9.00 3 2 347088 27.9000 S
636 1 2 Davis, Miss. Mary female 28.00 0 0 237668 13.0000 S
637 0 3 Leinonen, Mr. Antti Gustaf male 32.00 0 0 STON/O 2. 3101292 7.9250 S
638 0 2 Collyer, Mr. Harvey male 31.00 1 1 C.A. 31921 26.2500 S
639 0 3 Panula, Mrs. Juha (Maria Emilia Ojala) female 41.00 0 5 3101295 39.6875 S
640 0 3 Thorneycroft, Mr. Percival male NA 1 0 376564 16.1000 S
641 0 3 Jensen, Mr. Hans Peder male 20.00 0 0 350050 7.8542 S
642 1 1 Sagesser, Mlle. Emma female 24.00 0 0 PC 17477 69.3000 B35 C
643 0 3 Skoog, Miss. Margit Elizabeth female 2.00 3 2 347088 27.9000 S
644 1 3 Foo, Mr. Choong male NA 0 0 1601 56.4958 S
645 1 3 Baclini, Miss. Eugenie female 0.75 2 1 2666 19.2583 C
646 1 1 Harper, Mr. Henry Sleeper male 48.00 1 0 PC 17572 76.7292 D33 C
647 0 3 Cor, Mr. Liudevit male 19.00 0 0 349231 7.8958 S
648 1 1 Simonius-Blumer, Col. Oberst Alfons male 56.00 0 0 13213 35.5000 A26 C
649 0 3 Willey, Mr. Edward male NA 0 0 S.O./P.P. 751 7.5500 S
650 1 3 Stanley, Miss. Amy Zillah Elsie female 23.00 0 0 CA. 2314 7.5500 S
651 0 3 Mitkoff, Mr. Mito male NA 0 0 349221 7.8958 S
652 1 2 Doling, Miss. Elsie female 18.00 0 1 231919 23.0000 S
653 0 3 Kalvik, Mr. Johannes Halvorsen male 21.00 0 0 8475 8.4333 S
654 1 3 O’Leary, Miss. Hanora “Norah” female NA 0 0 330919 7.8292 Q
655 0 3 Hegarty, Miss. Hanora “Nora” female 18.00 0 0 365226 6.7500 Q
656 0 2 Hickman, Mr. Leonard Mark male 24.00 2 0 S.O.C. 14879 73.5000 S
657 0 3 Radeff, Mr. Alexander male NA 0 0 349223 7.8958 S
658 0 3 Bourke, Mrs. John (Catherine) female 32.00 1 1 364849 15.5000 Q
659 0 2 Eitemiller, Mr. George Floyd male 23.00 0 0 29751 13.0000 S
660 0 1 Newell, Mr. Arthur Webster male 58.00 0 2 35273 113.2750 D48 C
661 1 1 Frauenthal, Dr. Henry William male 50.00 2 0 PC 17611 133.6500 S
662 0 3 Badt, Mr. Mohamed male 40.00 0 0 2623 7.2250 C
663 0 1 Colley, Mr. Edward Pomeroy male 47.00 0 0 5727 25.5875 E58 S
664 0 3 Coleff, Mr. Peju male 36.00 0 0 349210 7.4958 S
665 1 3 Lindqvist, Mr. Eino William male 20.00 1 0 STON/O 2. 3101285 7.9250 S
666 0 2 Hickman, Mr. Lewis male 32.00 2 0 S.O.C. 14879 73.5000 S
667 0 2 Butler, Mr. Reginald Fenton male 25.00 0 0 234686 13.0000 S
668 0 3 Rommetvedt, Mr. Knud Paust male NA 0 0 312993 7.7750 S
669 0 3 Cook, Mr. Jacob male 43.00 0 0 A/5 3536 8.0500 S
670 1 1 Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright) female NA 1 0 19996 52.0000 C126 S
671 1 2 Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford) female 40.00 1 1 29750 39.0000 S
672 0 1 Davidson, Mr. Thornton male 31.00 1 0 F.C. 12750 52.0000 B71 S
673 0 2 Mitchell, Mr. Henry Michael male 70.00 0 0 C.A. 24580 10.5000 S
674 1 2 Wilhelms, Mr. Charles male 31.00 0 0 244270 13.0000 S
675 0 2 Watson, Mr. Ennis Hastings male NA 0 0 239856 0.0000 S
676 0 3 Edvardsson, Mr. Gustaf Hjalmar male 18.00 0 0 349912 7.7750 S
677 0 3 Sawyer, Mr. Frederick Charles male 24.50 0 0 342826 8.0500 S
678 1 3 Turja, Miss. Anna Sofia female 18.00 0 0 4138 9.8417 S
679 0 3 Goodwin, Mrs. Frederick (Augusta Tyler) female 43.00 1 6 CA 2144 46.9000 S
680 1 1 Cardeza, Mr. Thomas Drake Martinez male 36.00 0 1 PC 17755 512.3292 B51 B53 B55 C
681 0 3 Peters, Miss. Katie female NA 0 0 330935 8.1375 Q
682 1 1 Hassab, Mr. Hammad male 27.00 0 0 PC 17572 76.7292 D49 C
683 0 3 Olsvigen, Mr. Thor Anderson male 20.00 0 0 6563 9.2250 S
684 0 3 Goodwin, Mr. Charles Edward male 14.00 5 2 CA 2144 46.9000 S
685 0 2 Brown, Mr. Thomas William Solomon male 60.00 1 1 29750 39.0000 S
686 0 2 Laroche, Mr. Joseph Philippe Lemercier male 25.00 1 2 SC/Paris 2123 41.5792 C
687 0 3 Panula, Mr. Jaako Arnold male 14.00 4 1 3101295 39.6875 S
688 0 3 Dakic, Mr. Branko male 19.00 0 0 349228 10.1708 S
689 0 3 Fischer, Mr. Eberhard Thelander male 18.00 0 0 350036 7.7958 S
690 1 1 Madill, Miss. Georgette Alexandra female 15.00 0 1 24160 211.3375 B5 S
691 1 1 Dick, Mr. Albert Adrian male 31.00 1 0 17474 57.0000 B20 S
692 1 3 Karun, Miss. Manca female 4.00 0 1 349256 13.4167 C
693 1 3 Lam, Mr. Ali male NA 0 0 1601 56.4958 S
694 0 3 Saad, Mr. Khalil male 25.00 0 0 2672 7.2250 C
695 0 1 Weir, Col. John male 60.00 0 0 113800 26.5500 S
696 0 2 Chapman, Mr. Charles Henry male 52.00 0 0 248731 13.5000 S
697 0 3 Kelly, Mr. James male 44.00 0 0 363592 8.0500 S
698 1 3 Mullens, Miss. Katherine “Katie” female NA 0 0 35852 7.7333 Q
699 0 1 Thayer, Mr. John Borland male 49.00 1 1 17421 110.8833 C68 C
700 0 3 Humblen, Mr. Adolf Mathias Nicolai Olsen male 42.00 0 0 348121 7.6500 F G63 S
701 1 1 Astor, Mrs. John Jacob (Madeleine Talmadge Force) female 18.00 1 0 PC 17757 227.5250 C62 C64 C
702 1 1 Silverthorne, Mr. Spencer Victor male 35.00 0 0 PC 17475 26.2875 E24 S
703 0 3 Barbara, Miss. Saiide female 18.00 0 1 2691 14.4542 C
704 0 3 Gallagher, Mr. Martin male 25.00 0 0 36864 7.7417 Q
705 0 3 Hansen, Mr. Henrik Juul male 26.00 1 0 350025 7.8542 S
706 0 2 Morley, Mr. Henry Samuel (“Mr Henry Marshall”) male 39.00 0 0 250655 26.0000 S
707 1 2 Kelly, Mrs. Florence “Fannie” female 45.00 0 0 223596 13.5000 S
708 1 1 Calderhead, Mr. Edward Pennington male 42.00 0 0 PC 17476 26.2875 E24 S
709 1 1 Cleaver, Miss. Alice female 22.00 0 0 113781 151.5500 S
710 1 3 Moubarek, Master. Halim Gonios (“William George”) male NA 1 1 2661 15.2458 C
711 1 1 Mayne, Mlle. Berthe Antonine (“Mrs de Villiers”) female 24.00 0 0 PC 17482 49.5042 C90 C
712 0 1 Klaber, Mr. Herman male NA 0 0 113028 26.5500 C124 S
713 1 1 Taylor, Mr. Elmer Zebley male 48.00 1 0 19996 52.0000 C126 S
714 0 3 Larsson, Mr. August Viktor male 29.00 0 0 7545 9.4833 S
715 0 2 Greenberg, Mr. Samuel male 52.00 0 0 250647 13.0000 S
716 0 3 Soholt, Mr. Peter Andreas Lauritz Andersen male 19.00 0 0 348124 7.6500 F G73 S
717 1 1 Endres, Miss. Caroline Louise female 38.00 0 0 PC 17757 227.5250 C45 C
718 1 2 Troutt, Miss. Edwina Celia “Winnie” female 27.00 0 0 34218 10.5000 E101 S
719 0 3 McEvoy, Mr. Michael male NA 0 0 36568 15.5000 Q
720 0 3 Johnson, Mr. Malkolm Joackim male 33.00 0 0 347062 7.7750 S
721 1 2 Harper, Miss. Annie Jessie “Nina” female 6.00 0 1 248727 33.0000 S
722 0 3 Jensen, Mr. Svend Lauritz male 17.00 1 0 350048 7.0542 S
723 0 2 Gillespie, Mr. William Henry male 34.00 0 0 12233 13.0000 S
724 0 2 Hodges, Mr. Henry Price male 50.00 0 0 250643 13.0000 S
725 1 1 Chambers, Mr. Norman Campbell male 27.00 1 0 113806 53.1000 E8 S
726 0 3 Oreskovic, Mr. Luka male 20.00 0 0 315094 8.6625 S
727 1 2 Renouf, Mrs. Peter Henry (Lillian Jefferys) female 30.00 3 0 31027 21.0000 S
728 1 3 Mannion, Miss. Margareth female NA 0 0 36866 7.7375 Q
729 0 2 Bryhl, Mr. Kurt Arnold Gottfrid male 25.00 1 0 236853 26.0000 S
730 0 3 Ilmakangas, Miss. Pieta Sofia female 25.00 1 0 STON/O2. 3101271 7.9250 S
731 1 1 Allen, Miss. Elisabeth Walton female 29.00 0 0 24160 211.3375 B5 S
732 0 3 Hassan, Mr. Houssein G N male 11.00 0 0 2699 18.7875 C
733 0 2 Knight, Mr. Robert J male NA 0 0 239855 0.0000 S
734 0 2 Berriman, Mr. William John male 23.00 0 0 28425 13.0000 S
735 0 2 Troupiansky, Mr. Moses Aaron male 23.00 0 0 233639 13.0000 S
736 0 3 Williams, Mr. Leslie male 28.50 0 0 54636 16.1000 S
737 0 3 Ford, Mrs. Edward (Margaret Ann Watson) female 48.00 1 3 W./C. 6608 34.3750 S
738 1 1 Lesurer, Mr. Gustave J male 35.00 0 0 PC 17755 512.3292 B101 C
739 0 3 Ivanoff, Mr. Kanio male NA 0 0 349201 7.8958 S
740 0 3 Nankoff, Mr. Minko male NA 0 0 349218 7.8958 S
741 1 1 Hawksford, Mr. Walter James male NA 0 0 16988 30.0000 D45 S
742 0 1 Cavendish, Mr. Tyrell William male 36.00 1 0 19877 78.8500 C46 S
743 1 1 Ryerson, Miss. Susan Parker “Suzette” female 21.00 2 2 PC 17608 262.3750 B57 B59 B63 B66 C
744 0 3 McNamee, Mr. Neal male 24.00 1 0 376566 16.1000 S
745 1 3 Stranden, Mr. Juho male 31.00 0 0 STON/O 2. 3101288 7.9250 S
746 0 1 Crosby, Capt. Edward Gifford male 70.00 1 1 WE/P 5735 71.0000 B22 S
747 0 3 Abbott, Mr. Rossmore Edward male 16.00 1 1 C.A. 2673 20.2500 S
748 1 2 Sinkkonen, Miss. Anna female 30.00 0 0 250648 13.0000 S
749 0 1 Marvin, Mr. Daniel Warner male 19.00 1 0 113773 53.1000 D30 S
750 0 3 Connaghton, Mr. Michael male 31.00 0 0 335097 7.7500 Q
751 1 2 Wells, Miss. Joan female 4.00 1 1 29103 23.0000 S
752 1 3 Moor, Master. Meier male 6.00 0 1 392096 12.4750 E121 S
753 0 3 Vande Velde, Mr. Johannes Joseph male 33.00 0 0 345780 9.5000 S
754 0 3 Jonkoff, Mr. Lalio male 23.00 0 0 349204 7.8958 S
755 1 2 Herman, Mrs. Samuel (Jane Laver) female 48.00 1 2 220845 65.0000 S
756 1 2 Hamalainen, Master. Viljo male 0.67 1 1 250649 14.5000 S
757 0 3 Carlsson, Mr. August Sigfrid male 28.00 0 0 350042 7.7958 S
758 0 2 Bailey, Mr. Percy Andrew male 18.00 0 0 29108 11.5000 S
759 0 3 Theobald, Mr. Thomas Leonard male 34.00 0 0 363294 8.0500 S
760 1 1 Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards) female 33.00 0 0 110152 86.5000 B77 S
761 0 3 Garfirth, Mr. John male NA 0 0 358585 14.5000 S
762 0 3 Nirva, Mr. Iisakki Antino Aijo male 41.00 0 0 SOTON/O2 3101272 7.1250 S
763 1 3 Barah, Mr. Hanna Assi male 20.00 0 0 2663 7.2292 C
764 1 1 Carter, Mrs. William Ernest (Lucile Polk) female 36.00 1 2 113760 120.0000 B96 B98 S
765 0 3 Eklund, Mr. Hans Linus male 16.00 0 0 347074 7.7750 S
766 1 1 Hogeboom, Mrs. John C (Anna Andrews) female 51.00 1 0 13502 77.9583 D11 S
767 0 1 Brewe, Dr. Arthur Jackson male NA 0 0 112379 39.6000 C
768 0 3 Mangan, Miss. Mary female 30.50 0 0 364850 7.7500 Q
769 0 3 Moran, Mr. Daniel J male NA 1 0 371110 24.1500 Q
770 0 3 Gronnestad, Mr. Daniel Danielsen male 32.00 0 0 8471 8.3625 S
771 0 3 Lievens, Mr. Rene Aime male 24.00 0 0 345781 9.5000 S
772 0 3 Jensen, Mr. Niels Peder male 48.00 0 0 350047 7.8542 S
773 0 2 Mack, Mrs. (Mary) female 57.00 0 0 S.O./P.P. 3 10.5000 E77 S
774 0 3 Elias, Mr. Dibo male NA 0 0 2674 7.2250 C
775 1 2 Hocking, Mrs. Elizabeth (Eliza Needs) female 54.00 1 3 29105 23.0000 S
776 0 3 Myhrman, Mr. Pehr Fabian Oliver Malkolm male 18.00 0 0 347078 7.7500 S
777 0 3 Tobin, Mr. Roger male NA 0 0 383121 7.7500 F38 Q
778 1 3 Emanuel, Miss. Virginia Ethel female 5.00 0 0 364516 12.4750 S
779 0 3 Kilgannon, Mr. Thomas J male NA 0 0 36865 7.7375 Q
780 1 1 Robert, Mrs. Edward Scott (Elisabeth Walton McMillan) female 43.00 0 1 24160 211.3375 B3 S
781 1 3 Ayoub, Miss. Banoura female 13.00 0 0 2687 7.2292 C
782 1 1 Dick, Mrs. Albert Adrian (Vera Gillespie) female 17.00 1 0 17474 57.0000 B20 S
783 0 1 Long, Mr. Milton Clyde male 29.00 0 0 113501 30.0000 D6 S
784 0 3 Johnston, Mr. Andrew G male NA 1 2 W./C. 6607 23.4500 S
785 0 3 Ali, Mr. William male 25.00 0 0 SOTON/O.Q. 3101312 7.0500 S
786 0 3 Harmer, Mr. Abraham (David Lishin) male 25.00 0 0 374887 7.2500 S
787 1 3 Sjoblom, Miss. Anna Sofia female 18.00 0 0 3101265 7.4958 S
788 0 3 Rice, Master. George Hugh male 8.00 4 1 382652 29.1250 Q
789 1 3 Dean, Master. Bertram Vere male 1.00 1 2 C.A. 2315 20.5750 S
790 0 1 Guggenheim, Mr. Benjamin male 46.00 0 0 PC 17593 79.2000 B82 B84 C
791 0 3 Keane, Mr. Andrew “Andy” male NA 0 0 12460 7.7500 Q
792 0 2 Gaskell, Mr. Alfred male 16.00 0 0 239865 26.0000 S
793 0 3 Sage, Miss. Stella Anna female NA 8 2 CA. 2343 69.5500 S
794 0 1 Hoyt, Mr. William Fisher male NA 0 0 PC 17600 30.6958 C
795 0 3 Dantcheff, Mr. Ristiu male 25.00 0 0 349203 7.8958 S
796 0 2 Otter, Mr. Richard male 39.00 0 0 28213 13.0000 S
797 1 1 Leader, Dr. Alice (Farnham) female 49.00 0 0 17465 25.9292 D17 S
798 1 3 Osman, Mrs. Mara female 31.00 0 0 349244 8.6833 S
799 0 3 Ibrahim Shawah, Mr. Yousseff male 30.00 0 0 2685 7.2292 C
800 0 3 Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert) female 30.00 1 1 345773 24.1500 S
801 0 2 Ponesell, Mr. Martin male 34.00 0 0 250647 13.0000 S
802 1 2 Collyer, Mrs. Harvey (Charlotte Annie Tate) female 31.00 1 1 C.A. 31921 26.2500 S
803 1 1 Carter, Master. William Thornton II male 11.00 1 2 113760 120.0000 B96 B98 S
804 1 3 Thomas, Master. Assad Alexander male 0.42 0 1 2625 8.5167 C
805 1 3 Hedman, Mr. Oskar Arvid male 27.00 0 0 347089 6.9750 S
806 0 3 Johansson, Mr. Karl Johan male 31.00 0 0 347063 7.7750 S
807 0 1 Andrews, Mr. Thomas Jr male 39.00 0 0 112050 0.0000 A36 S
808 0 3 Pettersson, Miss. Ellen Natalia female 18.00 0 0 347087 7.7750 S
809 0 2 Meyer, Mr. August male 39.00 0 0 248723 13.0000 S
810 1 1 Chambers, Mrs. Norman Campbell (Bertha Griggs) female 33.00 1 0 113806 53.1000 E8 S
811 0 3 Alexander, Mr. William male 26.00 0 0 3474 7.8875 S
812 0 3 Lester, Mr. James male 39.00 0 0 A/4 48871 24.1500 S
813 0 2 Slemen, Mr. Richard James male 35.00 0 0 28206 10.5000 S
814 0 3 Andersson, Miss. Ebba Iris Alfrida female 6.00 4 2 347082 31.2750 S
815 0 3 Tomlin, Mr. Ernest Portage male 30.50 0 0 364499 8.0500 S
816 0 1 Fry, Mr. Richard male NA 0 0 112058 0.0000 B102 S
817 0 3 Heininen, Miss. Wendla Maria female 23.00 0 0 STON/O2. 3101290 7.9250 S
818 0 2 Mallet, Mr. Albert male 31.00 1 1 S.C./PARIS 2079 37.0042 C
819 0 3 Holm, Mr. John Fredrik Alexander male 43.00 0 0 C 7075 6.4500 S
820 0 3 Skoog, Master. Karl Thorsten male 10.00 3 2 347088 27.9000 S
821 1 1 Hays, Mrs. Charles Melville (Clara Jennings Gregg) female 52.00 1 1 12749 93.5000 B69 S
822 1 3 Lulic, Mr. Nikola male 27.00 0 0 315098 8.6625 S
823 0 1 Reuchlin, Jonkheer. John George male 38.00 0 0 19972 0.0000 S
824 1 3 Moor, Mrs. (Beila) female 27.00 0 1 392096 12.4750 E121 S
825 0 3 Panula, Master. Urho Abraham male 2.00 4 1 3101295 39.6875 S
826 0 3 Flynn, Mr. John male NA 0 0 368323 6.9500 Q
827 0 3 Lam, Mr. Len male NA 0 0 1601 56.4958 S
828 1 2 Mallet, Master. Andre male 1.00 0 2 S.C./PARIS 2079 37.0042 C
829 1 3 McCormack, Mr. Thomas Joseph male NA 0 0 367228 7.7500 Q
830 1 1 Stone, Mrs. George Nelson (Martha Evelyn) female 62.00 0 0 113572 80.0000 B28
831 1 3 Yasbeck, Mrs. Antoni (Selini Alexander) female 15.00 1 0 2659 14.4542 C
832 1 2 Richards, Master. George Sibley male 0.83 1 1 29106 18.7500 S
833 0 3 Saad, Mr. Amin male NA 0 0 2671 7.2292 C
834 0 3 Augustsson, Mr. Albert male 23.00 0 0 347468 7.8542 S
835 0 3 Allum, Mr. Owen George male 18.00 0 0 2223 8.3000 S
836 1 1 Compton, Miss. Sara Rebecca female 39.00 1 1 PC 17756 83.1583 E49 C
837 0 3 Pasic, Mr. Jakob male 21.00 0 0 315097 8.6625 S
838 0 3 Sirota, Mr. Maurice male NA 0 0 392092 8.0500 S
839 1 3 Chip, Mr. Chang male 32.00 0 0 1601 56.4958 S
840 1 1 Marechal, Mr. Pierre male NA 0 0 11774 29.7000 C47 C
841 0 3 Alhomaki, Mr. Ilmari Rudolf male 20.00 0 0 SOTON/O2 3101287 7.9250 S
842 0 2 Mudd, Mr. Thomas Charles male 16.00 0 0 S.O./P.P. 3 10.5000 S
843 1 1 Serepeca, Miss. Augusta female 30.00 0 0 113798 31.0000 C
844 0 3 Lemberopolous, Mr. Peter L male 34.50 0 0 2683 6.4375 C
845 0 3 Culumovic, Mr. Jeso male 17.00 0 0 315090 8.6625 S
846 0 3 Abbing, Mr. Anthony male 42.00 0 0 C.A. 5547 7.5500 S
847 0 3 Sage, Mr. Douglas Bullen male NA 8 2 CA. 2343 69.5500 S
848 0 3 Markoff, Mr. Marin male 35.00 0 0 349213 7.8958 C
849 0 2 Harper, Rev. John male 28.00 0 1 248727 33.0000 S
850 1 1 Goldenberg, Mrs. Samuel L (Edwiga Grabowska) female NA 1 0 17453 89.1042 C92 C
851 0 3 Andersson, Master. Sigvard Harald Elias male 4.00 4 2 347082 31.2750 S
852 0 3 Svensson, Mr. Johan male 74.00 0 0 347060 7.7750 S
853 0 3 Boulos, Miss. Nourelain female 9.00 1 1 2678 15.2458 C
854 1 1 Lines, Miss. Mary Conover female 16.00 0 1 PC 17592 39.4000 D28 S
855 0 2 Carter, Mrs. Ernest Courtenay (Lilian Hughes) female 44.00 1 0 244252 26.0000 S
856 1 3 Aks, Mrs. Sam (Leah Rosen) female 18.00 0 1 392091 9.3500 S
857 1 1 Wick, Mrs. George Dennick (Mary Hitchcock) female 45.00 1 1 36928 164.8667 S
858 1 1 Daly, Mr. Peter Denis male 51.00 0 0 113055 26.5500 E17 S
859 1 3 Baclini, Mrs. Solomon (Latifa Qurban) female 24.00 0 3 2666 19.2583 C
860 0 3 Razi, Mr. Raihed male NA 0 0 2629 7.2292 C
861 0 3 Hansen, Mr. Claus Peter male 41.00 2 0 350026 14.1083 S
862 0 2 Giles, Mr. Frederick Edward male 21.00 1 0 28134 11.5000 S
863 1 1 Swift, Mrs. Frederick Joel (Margaret Welles Barron) female 48.00 0 0 17466 25.9292 D17 S
864 0 3 Sage, Miss. Dorothy Edith “Dolly” female NA 8 2 CA. 2343 69.5500 S
865 0 2 Gill, Mr. John William male 24.00 0 0 233866 13.0000 S
866 1 2 Bystrom, Mrs. (Karolina) female 42.00 0 0 236852 13.0000 S
867 1 2 Duran y More, Miss. Asuncion female 27.00 1 0 SC/PARIS 2149 13.8583 C
868 0 1 Roebling, Mr. Washington Augustus II male 31.00 0 0 PC 17590 50.4958 A24 S
869 0 3 van Melkebeke, Mr. Philemon male NA 0 0 345777 9.5000 S
870 1 3 Johnson, Master. Harold Theodor male 4.00 1 1 347742 11.1333 S
871 0 3 Balkic, Mr. Cerin male 26.00 0 0 349248 7.8958 S
872 1 1 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47.00 1 1 11751 52.5542 D35 S
873 0 1 Carlsson, Mr. Frans Olof male 33.00 0 0 695 5.0000 B51 B53 B55 S
874 0 3 Vander Cruyssen, Mr. Victor male 47.00 0 0 345765 9.0000 S
875 1 2 Abelson, Mrs. Samuel (Hannah Wizosky) female 28.00 1 0 P/PP 3381 24.0000 C
876 1 3 Najib, Miss. Adele Kiamie “Jane” female 15.00 0 0 2667 7.2250 C
877 0 3 Gustafsson, Mr. Alfred Ossian male 20.00 0 0 7534 9.8458 S
878 0 3 Petroff, Mr. Nedelio male 19.00 0 0 349212 7.8958 S
879 0 3 Laleff, Mr. Kristo male NA 0 0 349217 7.8958 S
880 1 1 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56.00 0 1 11767 83.1583 C50 C
881 1 2 Shelley, Mrs. William (Imanita Parrish Hall) female 25.00 0 1 230433 26.0000 S
882 0 3 Markun, Mr. Johann male 33.00 0 0 349257 7.8958 S
883 0 3 Dahlberg, Miss. Gerda Ulrika female 22.00 0 0 7552 10.5167 S
884 0 2 Banfield, Mr. Frederick James male 28.00 0 0 C.A./SOTON 34068 10.5000 S
885 0 3 Sutehall, Mr. Henry Jr male 25.00 0 0 SOTON/OQ 392076 7.0500 S
886 0 3 Rice, Mrs. William (Margaret Norton) female 39.00 0 5 382652 29.1250 Q
887 0 2 Montvila, Rev. Juozas male 27.00 0 0 211536 13.0000 S
888 1 1 Graham, Miss. Margaret Edith female 19.00 0 0 112053 30.0000 B42 S
889 0 3 Johnston, Miss. Catherine Helen “Carrie” female NA 1 2 W./C. 6607 23.4500 S
890 1 1 Behr, Mr. Karl Howell male 26.00 0 0 111369 30.0000 C148 C
891 0 3 Dooley, Mr. Patrick male 32.00 0 0 370376 7.7500 Q 
# Consultamos el número de filas y columnas

dim(datostitanic)
## [1] 891  12

La base de datos contiene información de los pasajeros del Titanic, incluyendo variables relacionadas con sus características personales y condiciones de viaje.

# Descripción de las variables
descripcion_variables <- data.frame(
  Variable = c(
    "Survived",
    "Pclass",
    "Sex",
    "Age",
    "Fare",
    "Embarked"
  ),
  Descripcion = c(
    "Supervivencia del pasajero",
    "Clase del pasajero",
    "Sexo",
    "Edad",
    "Tarifa pagada",
    "Puerto de embarque"
  )
)

kable(
  descripcion_variables,
  caption = "Variables utilizadas en el modelo"
) %>%
  kable_styling(
    bootstrap_options = c("striped","hover"),
    full_width = FALSE
  )
Variables utilizadas en el modelo
Variable Descripcion
Survived Supervivencia del pasajero
Pclass Clase del pasajero
Sex Sexo
Age Edad
Fare Tarifa pagada
Embarked Puerto de embarque

DISTRIBUCIÓN DE LA VARIABLE OBJETIVO

# Cantidad de pasajeros que sobrevivieron y no sobrevivieron

table(datostitanic$Survived)
## 
##   0   1 
## 549 342
# Representación gráfica de la variable objetivo

barplot(
  table(datostitanic$Survived),
  main = "Distribución de supervivencia de pasajeros",
  xlab = "Supervivencia",
  ylab = "Frecuencia"
)

Esta gráfica permite observar la distribución de pasajeros que sobrevivieron y que no sobrevivieron al accidente, información que será utilizada para entrenar el modelo de clasificación.

SELECCIÓN DE VARIABLES

# Seleccionamos las variables más relevantes

datosarbol <- datostitanic[,c(
  "Survived",
  "Pclass",
  "Sex",
  "Age",
  "Fare",
  "Embarked"
)]

Como no nesceitamos todas las columnas, seleccionamos solo las que son mas relevantes.

TRATAMIENTO DE VALORES FALTANTES

La variable Age (Años), suele tener datos vacios

# Eliminamos registros con valores faltantes

datosarbol <- na.omit(datosarbol)

Se eliminaron los registros con información incompleta para evitar errores durante la construcción del modelo.

# Fijamos para obtener siempre los mismos resultados

set.seed(123)

indicedatos <- createDataPartition(
  datosarbol$Survived,
  p = 0.7,
  list = FALSE
)

datosentrena <- datosarbol[indicedatos, ]

datosprueba <- datosarbol[-indicedatos, ]

CONSTRUCCIÓN DEL ÁRBOL DE DECISIÓN

Primero cargamos las librerias

library(rpart) #libreria que ayuda a predecir un valor continuo
library(rpart.plot) #libreria que dibuja el arbol de decisión
## Warning: package 'rpart.plot' was built under R version 4.5.3
# Construimos el árbol de decisión

modeloarbol <- rpart(
  Survived ~ .,
  data = datosentrena,
  method = "class"
)

VISUALIZACIÓN DEL ÁRBOL

rpart.plot(modeloarbol)

El árbol de decisión muestra las reglas utilizadas para clasificar a los pasajeros según su probabilidad de supervivencia. Cada división representa una condición basada en las características de los pasajeros.

Los resultados sugieren que las mujeres presentaron mayores probabilidades de supervivencia que los hombres. Además, la clase en la que viajaba el pasajero también influyó en el resultado, observándose una mayor probabilidad de supervivencia en pasajeros de clases superiores. La edad y la tarifa pagada complementan las decisiones del modelo, permitiendo generar clasificaciones más precisas.

PREDICCIONES DEL MODELO

# Generamos las predicciones sobre los datos de prueba

predicciones <- predict(
  modeloarbol,
  datosprueba,
  type = "class"
)

MATRIZ DE CONFUSIÓN

# Convertimos la variable real a factor

datosprueba$Survived <- as.factor(
  datosprueba$Survived
)

# Calculamos la matriz de confusión

matrizconfusion <- confusionMatrix(
  predicciones,
  datosprueba$Survived
)

matrizconfusion
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   0   1
##          0 110  32
##          1  11  61
##                                          
##                Accuracy : 0.7991         
##                  95% CI : (0.739, 0.8506)
##     No Information Rate : 0.5654         
##     P-Value [Acc > NIR] : 5.244e-13      
##                                          
##                   Kappa : 0.5802         
##                                          
##  Mcnemar's Test P-Value : 0.002289       
##                                          
##             Sensitivity : 0.9091         
##             Specificity : 0.6559         
##          Pos Pred Value : 0.7746         
##          Neg Pred Value : 0.8472         
##              Prevalence : 0.5654         
##          Detection Rate : 0.5140         
##    Detection Prevalence : 0.6636         
##       Balanced Accuracy : 0.7825         
##                                          
##        'Positive' Class : 0              
##

La matriz de confusión permite comparar los valores reales con las predicciones realizadas por el árbol de decisión. Se observa que el modelo clasificó correctamente 110 pasajeros que no sobrevivieron y 61 pasajeros que sí sobrevivieron. Sin embargo, también se presentaron algunos errores de clasificación, ya que 32 pasajeros que sobrevivieron fueron clasificados como fallecidos y 11 pasajeros que fallecieron fueron clasificados como sobrevivientes.

EXACTITUD DEL MODELO

matrizconfusion$overall["Accuracy"]
##  Accuracy 
## 0.7990654

VARIABLES IMPORTANTES IDENTIFICADAS POR EL MODELO

# Variables identificadas por el Árbol de Decisión

variables_importantes <- data.frame(
  Variable = c(
    "Sex",
    "Pclass",
    "Age",
    "Fare"
  ),
  Importancia = c(
    "Muy alta",
    "Alta",
    "Media",
    "Media"
  ),
  Interpretacion = c(
    "Fue la variable más importante para predecir la supervivencia",
    "La clase del pasajero influyó en la probabilidad de sobrevivir",
    "La edad ayudó a realizar divisiones dentro del árbol",
    "La tarifa pagada complementó las decisiones del modelo"
  )
)

kable(
  variables_importantes,
  caption = "Variables relevantes identificadas por el Árbol de Decisión"
) %>%
  kable_styling(
    bootstrap_options = c("striped","hover"),
    full_width = FALSE
  )
Variables relevantes identificadas por el Árbol de Decisión
Variable Importancia Interpretacion
Sex Muy alta Fue la variable más importante para predecir la supervivencia
Pclass Alta La clase del pasajero influyó en la probabilidad de sobrevivir
Age Media La edad ayudó a realizar divisiones dentro del árbol
Fare Media La tarifa pagada complementó las decisiones del modelo

La tabla anterior resume las variables que tuvieron mayor participación en las decisiones del modelo. Se observa que el sexo fue el factor más importante para clasificar a los pasajeros, seguido por la clase en la que viajaban, la edad y la tarifa pagada

DESEMPEÑO DEL MODELO

# Resumen del desempeño del modelo

resultado_arbol <- data.frame(
  Indicador = c(
    "Exactitud",
    "Sensibilidad",
    "Especificidad",
    "Precisión"
  ),
  Valor = c(
    "79.91%",
    "90.91%",
    "65.59%",
    "77.46%"
  )
)

kable(
  resultado_arbol,
  caption = "Indicadores de desempeño del Árbol de Decisión"
) %>%
  kable_styling(
    bootstrap_options = c("striped","hover"),
    full_width = FALSE
  )
Indicadores de desempeño del Árbol de Decisión
Indicador Valor
Exactitud 79.91%
Sensibilidad 90.91%
Especificidad 65.59%
Precisión 77.46%

CONCLUSIÓN

El Árbol de Decisión obtuvo una exactitud del 79.91%, lo que indica que clasificó correctamente aproximadamente 80 de cada 100 pasajeros evaluados. Estos resultados muestran que el modelo tiene una buena capacidad para predecir la supervivencia de los pasajeros utilizando las variables seleccionadas.

CONCLUSIONES DEL TRABAJO

REFERENCIAS