#INTEGRANTES DEL EQUIPO
#BenjamÃn Reyes Sánchez A00830513
#Emilio Amarante Portilla A01236198
#Santiago MacÃas Villa A00830653
#Santiago Guerra Leija A00571467
#Lesly Gómez Hernández A00830701
bank <- read.csv("/Users/benjaminreyessanchez/Downloads/bank2.csv")
library(tidyverse)
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## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(dplyr)
library(ggplot2)
glimpse(bank)
## Rows: 4,521
## Columns: 17
## $ age <int> 30, 33, 35, 30, 59, 35, 36, 39, 41, 43, 39, 43, 36, 20, 31, …
## $ job <chr> "unemployed", "services", "management", "management", "blue-…
## $ marital <chr> "married", "married", "single", "married", "married", "singl…
## $ education <chr> "primary", "secondary", "tertiary", "tertiary", "secondary",…
## $ default <chr> "no", "no", "no", "no", "no", "no", "no", "no", "no", "no", …
## $ balance <int> 1787, 4789, 1350, 1476, 0, 747, 307, 147, 221, -88, 9374, 26…
## $ housing <chr> "no", "yes", "yes", "yes", "yes", "no", "yes", "yes", "yes",…
## $ loan <chr> "no", "yes", "no", "yes", "no", "no", "no", "no", "no", "yes…
## $ contact <chr> "cellular", "cellular", "cellular", "unknown", "unknown", "c…
## $ day <int> 19, 11, 16, 3, 5, 23, 14, 6, 14, 17, 20, 17, 13, 30, 29, 29,…
## $ month <chr> "oct", "may", "apr", "jun", "may", "feb", "may", "may", "may…
## $ duration <int> 79, 220, 185, 199, 226, 141, 341, 151, 57, 313, 273, 113, 32…
## $ campaign <int> 1, 1, 1, 4, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 1, 2, 5, 1, 1, 1, …
## $ pdays <int> -1, 339, 330, -1, -1, 176, 330, -1, -1, 147, -1, -1, -1, -1,…
## $ previous <int> 0, 4, 1, 0, 0, 3, 2, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 2, 0, 1, …
## $ poutcome <chr> "unknown", "failure", "failure", "unknown", "unknown", "fail…
## $ y <chr> "no", "no", "no", "no", "no", "no", "no", "no", "no", "no", …
summary(bank)
## age job marital education
## Min. :19.00 Length:4521 Length:4521 Length:4521
## 1st Qu.:33.00 Class :character Class :character Class :character
## Median :39.00 Mode :character Mode :character Mode :character
## Mean :41.17
## 3rd Qu.:49.00
## Max. :87.00
## default balance housing loan
## Length:4521 Min. :-3313 Length:4521 Length:4521
## Class :character 1st Qu.: 69 Class :character Class :character
## Mode :character Median : 444 Mode :character Mode :character
## Mean : 1423
## 3rd Qu.: 1480
## Max. :71188
## contact day month duration
## Length:4521 Min. : 1.00 Length:4521 Min. : 4
## Class :character 1st Qu.: 9.00 Class :character 1st Qu.: 104
## Mode :character Median :16.00 Mode :character Median : 185
## Mean :15.92 Mean : 264
## 3rd Qu.:21.00 3rd Qu.: 329
## Max. :31.00 Max. :3025
## campaign pdays previous poutcome
## Min. : 1.000 Min. : -1.00 Min. : 0.0000 Length:4521
## 1st Qu.: 1.000 1st Qu.: -1.00 1st Qu.: 0.0000 Class :character
## Median : 2.000 Median : -1.00 Median : 0.0000 Mode :character
## Mean : 2.794 Mean : 39.77 Mean : 0.5426
## 3rd Qu.: 3.000 3rd Qu.: -1.00 3rd Qu.: 0.0000
## Max. :50.000 Max. :871.00 Max. :25.0000
## y
## Length:4521
## Class :character
## Mode :character
##
##
##
Prestamo de vivienda según ocupación
#Cantidad de personas con un prestamo de vivienda, según su ocupación.
tab <- table(bank$housing, bank$job)
tab
##
## admin. blue-collar entrepreneur housemaid management retired
## no 176 251 74 73 466 180
## yes 302 695 94 39 503 50
##
## self-employed services student technician unemployed unknown
## no 95 132 64 344 70 37
## yes 88 285 20 424 58 1
barplot(tab, main = "Prestamo de vivienda según ocupación")
legend(x="topright", legend=c("No", "Yes"), fill = c("Black","Grey"), title = "Housing")
prop.table(tab, margin = 2)
##
## admin. blue-collar entrepreneur housemaid management retired
## no 0.36820084 0.26532770 0.44047619 0.65178571 0.48090815 0.78260870
## yes 0.63179916 0.73467230 0.55952381 0.34821429 0.51909185 0.21739130
##
## self-employed services student technician unemployed unknown
## no 0.51912568 0.31654676 0.76190476 0.44791667 0.54687500 0.97368421
## yes 0.48087432 0.68345324 0.23809524 0.55208333 0.45312500 0.02631579
#Proporciones
Resultados de la Campaña - ¿Lograron o no que el cliente hiciera un
depósito en la institución?
#Diagrama de Dispersión tomando en cuenta la edad del cliente y la duración del último contacto que se tuvo. Y estableciendo por colores si se obtuvo un resultado positivo o negativo de realizar un depósito a largo plazo en la institución bancaria.
plot(x = bank$age, y = bank$duration, col = factor(bank$y), main = "Resultados de la Campaña", xlab = "Edad", ylab = "Duración del último contacto")
legend(x = "topleft", legend = c("No", "SÃ"), fill = c("Black", "Red"), title = "Objetivo Completado")
