lm() es la función de R para ajustar modelos lineales. Es el modelo estadistico más basico que existe y más facil de interpretar. Para interpretarlo se usa la medida de R-cuadrada, que signifcia 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)
#Modelo Predictivo
# Importar base de datos
base_de_datos <- read.csv("Desktop/seguros.csv")
#View(base_de_datos)
# 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 :20730# Generar regresion (modelo lineal)
regresion <- lm(TotalIncurredCost ~ TotalPaid +TotalReserves + TotalRecovery + IndemnityPaid + OtherPaid + Transaction_Time + Procesing_Time + AverageWeeklyWage1, data=base_de_datos)
summary(regresion)##
## Call:
## lm(formula = TotalIncurredCost ~ TotalPaid + TotalReserves +
## TotalRecovery + IndemnityPaid + OtherPaid + Transaction_Time +
## Procesing_Time + AverageWeeklyWage1, data = base_de_datos)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.974e-07 -3.000e-11 -1.000e-11 1.000e-11 9.110e-09
##
## Coefficients: (2 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.281e-10 4.723e-11 -1.118e+01 <2e-16 ***
## TotalPaid 1.000e+00 1.248e-15 8.014e+14 <2e-16 ***
## TotalReserves NA NA NA NA
## TotalRecovery -1.000e+00 1.547e-14 -6.464e+13 <2e-16 ***
## IndemnityPaid 1.336e-15 2.254e-15 5.930e-01 0.553
## OtherPaid NA NA NA NA
## Transaction_Time 2.212e-14 1.678e-14 1.318e+00 0.188
## Procesing_Time -1.098e-12 8.847e-14 -1.241e+01 <2e-16 ***
## AverageWeeklyWage1 -4.563e-14 7.368e-14 -6.190e-01 0.536
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.834e-09 on 30998 degrees of freedom
## (614 observations deleted due to missingness)
## Multiple R-squared: 1, Adjusted R-squared: 1
## F-statistic: 4.999e+29 on 6 and 30998 DF, p-value: < 2.2e-16# Evaluar, y en caso necesario, ajustar la regresion
regresion <- lm(TotalIncurredCost ~ TotalPaid + TotalRecovery + IndemnityPaid + AverageWeeklyWage1, data=base_de_datos)
summary(regresion)##
## Call:
## lm(formula = TotalIncurredCost ~ TotalPaid + TotalRecovery +
## IndemnityPaid + AverageWeeklyWage1, data = base_de_datos)
##
## Residuals:
## Min 1Q Median 3Q Max
## -888407 597 800 851 1303964
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 230.420508 449.635356 0.512 0.60833
## TotalPaid 1.204439 0.005196 231.805 < 2e-16 ***
## TotalRecovery -0.250959 0.131256 -1.912 0.05589 .
## IndemnityPaid 0.430447 0.011306 38.073 < 2e-16 ***
## AverageWeeklyWage1 -2.201917 0.778035 -2.830 0.00466 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 30500 on 31614 degrees of freedom
## Multiple R-squared: 0.869, Adjusted R-squared: 0.869
## F-statistic: 5.244e+04 on 4 and 31614 DF, p-value: < 2.2e-16# Construir un modelo de prediccion
datos_nuevos <- data.frame(TotalPaid= 10404, TotalRecovery= 66.05, IndemnityPaid= 4977, AverageWeeklyWage1= 536.5)
predict(regresion,datos_nuevos)## 1
## 13705.84El modelo de regresión lineal explica el 39% del comportamiento de las rentas de bicicletas. Este modelo. se puede mejorar ageregando otras variables que impacten el negocio.