Modelo Predectivo
Im() 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
están los datos de la linea de regresión ajustada (Va de 0 a 1, donde 1
es que el modelo explica toda la variabilidad).
base_de_datos <- read.csv("C:/Users/eleyva1/Downloads/seguros.csv")
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
##
## 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
##
##
##
##
## AverageWeeklyWage1 ClaimID1 BillReviewALE Hospital
## Min. : 100.0 Min. : 777632 Min. : -448.0 Min. : -12570.4
## 1st Qu.: 492.0 1st Qu.: 800748 1st Qu.: 16.0 1st Qu.: 210.5
## Median : 492.0 Median : 812128 Median : 24.0 Median : 613.9
## Mean : 536.5 Mean : 1864676 Mean : 188.7 Mean : 5113.2
## 3rd Qu.: 492.0 3rd Qu.: 824726 3rd Qu.: 64.1 3rd Qu.: 2349.1
## Max. :8613.5 Max. :62203364 Max. :46055.3 Max. :2759604.0
## NA's :14912 NA's :19655
## PhysicianOutpatient Rx
## Min. : -549.5 Min. : -160.7
## 1st Qu.: 105.8 1st Qu.: 22.9
## Median : 218.0 Median : 61.5
## Mean : 1813.2 Mean : 1695.2
## 3rd Qu.: 680.6 3rd Qu.: 189.0
## Max. :1219766.6 Max. :631635.5
## NA's :2329 NA's :20730
regresion <- lm(TotalIncurredCost ~TotalPaid+ IndemnityPaid, data=base_de_datos)
summary(regresion)
##
## Call:
## lm(formula = TotalIncurredCost ~ TotalPaid + IndemnityPaid, data = base_de_datos)
##
## Residuals:
## Min 1Q Median 3Q Max
## -886159 727 899 937 1304420
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.520e+02 1.748e+02 -5.448 5.14e-08 ***
## TotalPaid 1.204e+00 5.195e-03 231.822 < 2e-16 ***
## IndemnityPaid 4.278e-01 1.128e-02 37.919 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 30500 on 31616 degrees of freedom
## Multiple R-squared: 0.869, Adjusted R-squared: 0.869
## F-statistic: 1.048e+05 on 2 and 31616 DF, p-value: < 2.2e-16
datos_nuevos <- data.frame(TotalPaid=8000, IndemnityPaid=3000)
predict(regresion,datos_nuevos)
## 1
## 9965.171
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