Regresion Logistca Titanic
Fernando Palmer A00839550
2026-02-19
Teoría
La regresión logística es un modelo estadístico de clasificación binaria, que estima la probabilidad de que ocurra un evento (valor 1) frente a que no ocurra (valor 0), en función de variables independientes. # Instalar paquetes y llamar librerías
library(titanic)
library(caret)## Loading required package: ggplot2## Loading required package: latticelibrary(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.2.0 ✔ readr 2.1.6
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ lubridate 1.9.5 ✔ tibble 3.3.1
## ✔ purrr 1.2.1 ✔ tidyr 1.3.2## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ purrr::lift() masks caret::lift()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsCrear la base de datos
df <- read.csv("C:\\Users\\ferpa\\OneDrive\\Documentos\\TEC\\Semestre 6\\m3 cantu\\titanic.csv")Entender la base de datos
summary(df)## pclass survived name sex
## Min. :1.000 Min. :0.000 Length:1310 Length:1310
## 1st Qu.:2.000 1st Qu.:0.000 Class :character Class :character
## Median :3.000 Median :0.000 Mode :character Mode :character
## Mean :2.295 Mean :0.382
## 3rd Qu.:3.000 3rd Qu.:1.000
## Max. :3.000 Max. :1.000
## NA's :1 NA's :1
## age sibsp parch ticket
## Min. : 0.1667 Min. :0.0000 Min. :0.000 Length:1310
## 1st Qu.:21.0000 1st Qu.:0.0000 1st Qu.:0.000 Class :character
## Median :28.0000 Median :0.0000 Median :0.000 Mode :character
## Mean :29.8811 Mean :0.4989 Mean :0.385
## 3rd Qu.:39.0000 3rd Qu.:1.0000 3rd Qu.:0.000
## Max. :80.0000 Max. :8.0000 Max. :9.000
## NA's :264 NA's :1 NA's :1
## fare cabin embarked boat
## Min. : 0.000 Length:1310 Length:1310 Length:1310
## 1st Qu.: 7.896 Class :character Class :character Class :character
## Median : 14.454 Mode :character Mode :character Mode :character
## Mean : 33.295
## 3rd Qu.: 31.275
## Max. :512.329
## NA's :2
## body home.dest
## Min. : 1.0 Length:1310
## 1st Qu.: 72.0 Class :character
## Median :155.0 Mode :character
## Mean :160.8
## 3rd Qu.:256.0
## Max. :328.0
## NA's :1189str(df)## 'data.frame': 1310 obs. of 14 variables:
## $ pclass : int 1 1 1 1 1 1 1 1 1 1 ...
## $ survived : int 1 1 0 0 0 1 1 0 1 0 ...
## $ name : chr "Allen, Miss. Elisabeth Walton" "Allison, Master. Hudson Trevor" "Allison, Miss. Helen Loraine" "Allison, Mr. Hudson Joshua Creighton" ...
## $ sex : chr "female" "male" "female" "male" ...
## $ age : num 29 0.917 2 30 25 ...
## $ sibsp : int 0 1 1 1 1 0 1 0 2 0 ...
## $ parch : int 0 2 2 2 2 0 0 0 0 0 ...
## $ ticket : chr "24160" "113781" "113781" "113781" ...
## $ fare : num 211 152 152 152 152 ...
## $ cabin : chr "B5" "C22 C26" "C22 C26" "C22 C26" ...
## $ embarked : chr "S" "S" "S" "S" ...
## $ boat : chr "2" "11" "" "" ...
## $ body : int NA NA NA 135 NA NA NA NA NA 22 ...
## $ home.dest: chr "St Louis, MO" "Montreal, PQ / Chesterville, ON" "Montreal, PQ / Chesterville, ON" "Montreal, PQ / Chesterville, ON" ...df <- df[, c("survived", "pclass","sex","age")]
df <- na.omit(df)
df$survived <- as.factor(df$survived)
df$pclass <- as.factor(df$pclass)
df$sex <- as.factor(df$sex)Crear el modelo
modelo <- glm(survived ~ ., data=df, family=binomial)
summary(modelo)##
## Call:
## glm(formula = survived ~ ., family = binomial, data = df)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.522074 0.326702 10.781 < 2e-16 ***
## pclass2 -1.280570 0.225538 -5.678 1.36e-08 ***
## pclass3 -2.289661 0.225802 -10.140 < 2e-16 ***
## sexmale -2.497845 0.166037 -15.044 < 2e-16 ***
## age -0.034393 0.006331 -5.433 5.56e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1414.62 on 1045 degrees of freedom
## Residual deviance: 982.45 on 1041 degrees of freedom
## AIC: 992.45
##
## Number of Fisher Scoring iterations: 4Probar el modelo
prueba <- data.frame(pclass=as.factor(c(1,3)),
sex=as.factor(c("female","male")),age=c(25,40))
probabilidad <- predict(modelo, newdata=prueba, type="response")
cbind(prueba, Probabilidad_Sobrevive=probabilidad)## pclass sex age Probabilidad_Sobrevive
## 1 1 female 25 0.93476160
## 2 3 male 40 0.06653593Conclusiones
En conclusion la regresion logistica nos dio algo muy parecido a que concluyo el arbol de decisiones, que al ser hombre uno tiene muchisimas mas probabilidades de no sobrevivir que una mujer. la mujer de 25 años teneindo una probabilidad del 93% o tambien se podria decir que una de casi 10 mujeres de 25 no sobrevivio. y del lado de los hombres de 40 años en promedio, solo sobrevivio uno de 16 personas (tasa de supervivencia de 6.7%)