Seguros Ejercicio
Derek Pacheco - A01412042
2024-08-19
Modelo Predictivo
Teoría
lm() es la función de R para ajustar modelos lineales. Es el modelo estadístico más básico que existe y más fácil de interpretar. Para interpretarlo se usa la medida R-cuadrada que significa que tan cerca estan los datos de la linea de regresion ajustada (Va de 0 a 1, donde 1 es que el modelo explica toda la variabilidad).
Importar base de datos
base_de_datos <- read.csv("C:\\Users\\Derek\\Documents\\seguros.csv")Entender la base de datos
resumen <- summary(base_de_datos)
resumen## ClaimID TotalPaid TotalReserves TotalRecovery
## Min. : 777632 Min. : 0 Min. : 0 Min. : 0.00
## 1st Qu.: 800748 1st Qu.: 83 1st Qu.: 0 1st Qu.: 0.00
## Median : 812128 Median : 271 Median : 0 Median : 0.00
## Mean : 1864676 Mean : 10404 Mean : 3368 Mean : 66.05
## 3rd Qu.: 824726 3rd Qu.: 1122 3rd Qu.: 0 3rd Qu.: 0.00
## Max. :62203364 Max. :4527291 Max. :1529053 Max. :100000.00
##
## IndemnityPaid OtherPaid TotalIncurredCost ClaimStatus
## Min. : 0 Min. : 0 Min. : -10400 Length:31619
## 1st Qu.: 0 1st Qu.: 80 1st Qu.: 80 Class :character
## Median : 0 Median : 265 Median : 266 Mode :character
## Mean : 4977 Mean : 5427 Mean : 13706
## 3rd Qu.: 0 3rd Qu.: 1023 3rd Qu.: 1098
## Max. :640732 Max. :4129915 Max. :4734750
##
## IncidentDate IncidentDescription ReturnToWorkDate ClaimantOpenedDate
## Length:31619 Length:31619 Length:31619 Length:31619
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## ClaimantClosedDate EmployerNotificationDate ReceivedDate
## Length:31619 Length:31619 Length:31619
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## IsDenied Transaction_Time Procesing_Time ClaimantAge_at_DOI
## Min. :0.00000 Min. : 0 Min. : 0.00 Min. :14.0
## 1st Qu.:0.00000 1st Qu.: 211 1st Qu.: 4.00 1st Qu.:33.0
## Median :0.00000 Median : 780 Median : 10.00 Median :42.0
## Mean :0.04463 Mean : 1004 Mean : 62.99 Mean :41.6
## 3rd Qu.:0.00000 3rd Qu.: 1440 3rd Qu.: 24.00 3rd Qu.:50.0
## Max. :1.00000 Max. :16428 Max. :11558.00 Max. :94.0
## NA's :614
## Gender ClaimantType InjuryNature BodyPartRegion
## Length:31619 Length:31619 Length:31619 Length:31619
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## BodyPart AverageWeeklyWage1 ClaimID1 BillReviewALE
## Length:31619 Min. : 100.0 Min. : 777632 Min. : -448.0
## Class :character 1st Qu.: 492.0 1st Qu.: 800748 1st Qu.: 16.0
## Mode :character Median : 492.0 Median : 812128 Median : 24.0
## Mean : 536.5 Mean : 1864676 Mean : 188.7
## 3rd Qu.: 492.0 3rd Qu.: 824726 3rd Qu.: 64.1
## Max. :8613.5 Max. :62203364 Max. :46055.3
## NA's :14912
## Hospital PhysicianOutpatient Rx
## Min. : -12570.4 Min. : -549.5 Min. : -160.7
## 1st Qu.: 210.5 1st Qu.: 105.8 1st Qu.: 22.9
## Median : 613.9 Median : 218.0 Median : 61.5
## Mean : 5113.2 Mean : 1813.2 Mean : 1695.2
## 3rd Qu.: 2349.1 3rd Qu.: 680.6 3rd Qu.: 189.0
## Max. :2759604.0 Max. :1219766.6 Max. :631635.5
## NA's :19655 NA's :2329 NA's :20730plot(base_de_datos$AverageWeeklyWage1, base_de_datos$TotalIncurredCost, main="Influencia del Salario Promedio Semanal sobre el Costo Total Incurrido", xlab="Salario Promedio Semanal", ylab="Costo Total Incurrido")
Generar regresion (modelo lineal)
regresion <- lm(TotalIncurredCost ~ AverageWeeklyWage1 + ClaimantAge_at_DOI + Transaction_Time + Gender + IsDenied, data=base_de_datos)
summary(regresion)##
## Call:
## lm(formula = TotalIncurredCost ~ AverageWeeklyWage1 + ClaimantAge_at_DOI +
## Transaction_Time + Gender + IsDenied, data = base_de_datos)
##
## Residuals:
## Min 1Q Median 3Q Max
## -90875 -6560 -3294 -351 1380909
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.117e+04 7.254e+02 -15.403 < 2e-16 ***
## AverageWeeklyWage1 1.080e+01 7.140e-01 15.127 < 2e-16 ***
## ClaimantAge_at_DOI 1.555e+02 1.386e+01 11.217 < 2e-16 ***
## Transaction_Time 4.505e+00 1.617e-01 27.868 < 2e-16 ***
## GenderMale -2.335e+01 3.128e+02 -0.075 0.94049
## GenderNot Available 1.639e+04 3.053e+03 5.370 7.95e-08 ***
## IsDenied -2.347e+03 7.538e+02 -3.114 0.00185 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 27390 on 30998 degrees of freedom
## (614 observations deleted due to missingness)
## Multiple R-squared: 0.03434, Adjusted R-squared: 0.03415
## F-statistic: 183.7 on 6 and 30998 DF, p-value: < 2.2e-16Evaluar, y en caso necesario, ajustar la regresion
regresion <- lm(TotalIncurredCost ~ AverageWeeklyWage1 + ClaimantAge_at_DOI + Transaction_Time + IsDenied, data=base_de_datos)
summary(regresion)##
## Call:
## lm(formula = TotalIncurredCost ~ AverageWeeklyWage1 + ClaimantAge_at_DOI +
## Transaction_Time + IsDenied, data = base_de_datos)
##
## Residuals:
## Min 1Q Median 3Q Max
## -90886 -6578 -3312 -366 1380848
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.116e+04 7.085e+02 -15.745 < 2e-16 ***
## AverageWeeklyWage1 1.080e+01 7.141e-01 15.128 < 2e-16 ***
## ClaimantAge_at_DOI 1.552e+02 1.385e+01 11.201 < 2e-16 ***
## Transaction_Time 4.531e+00 1.614e-01 28.074 < 2e-16 ***
## IsDenied -2.367e+03 7.538e+02 -3.141 0.00169 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 27400 on 31000 degrees of freedom
## (614 observations deleted due to missingness)
## Multiple R-squared: 0.03343, Adjusted R-squared: 0.03331
## F-statistic: 268.1 on 4 and 31000 DF, p-value: < 2.2e-16Construir un modelo de prediccion
datos_nuevos <- data.frame(AverageWeeklyWage1=500, ClaimantAge_at_DOI=40, Transaction_Time=1000, IsDenied=0)
predict(regresion, datos_nuevos)## 1
## 4984.57```
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