23/10, 2018
Selección de modelos
Que variables explican la distribución del guanaco?
- Selecciona el mejor modelo en regresión para distribución del guanaco
| presence | TempMedia | TempMesCalido | TempMesFrio | TempRangoAnual | PPAnual | PPMesSeco | PPMesHum |
|---|---|---|---|---|---|---|---|
| 1 | 16.4 | 26.0 | 7.6 | 18.4 | 37 | 12 | 0 |
| 1 | 15.2 | 25.1 | 5.8 | 19.3 | 31 | 10 | 0 |
| 1 | 19.8 | 28.0 | 11.8 | 16.2 | 17 | 4 | 0 |
| 1 | 17.4 | 26.7 | 9.1 | 17.6 | 38 | 12 | 0 |
| 1 | 1.5 | 10.0 | -5.7 | 15.7 | 1474 | 144 | 101 |
| 1 | 15.0 | 23.8 | 7.1 | 16.7 | 66 | 18 | 0 |
| 1 | 7.7 | 17.4 | -0.8 | 18.2 | 696 | 91 | 32 |
| 1 | 6.9 | 16.8 | -2.0 | 18.8 | 578 | 73 | 27 |
| 1 | 7.7 | 17.4 | -0.8 | 18.2 | 696 | 91 | 32 |
| 1 | 1.5 | 10.0 | -5.7 | 15.7 | 1474 | 144 | 101 |
| 1 | 7.7 | 17.4 | -0.8 | 18.2 | 696 | 91 | 32 |
| 1 | 7.1 | 16.9 | -1.7 | 18.6 | 604 | 77 | 28 |
| 1 | 7.3 | 17.4 | -1.7 | 19.1 | 495 | 64 | 22 |
| 1 | 5.7 | 15.8 | -2.9 | 18.7 | 791 | 87 | 49 |
| 1 | 8.1 | 22.0 | -1.8 | 23.8 | 783 | 137 | 22 |
| 1 | 8.2 | 22.2 | -1.7 | 23.9 | 760 | 139 | 21 |
| 1 | 5.0 | 13.0 | -1.8 | 14.8 | 677 | 66 | 42 |
| 1 | 5.7 | 15.8 | -2.9 | 18.7 | 791 | 87 | 49 |
| 1 | 5.7 | 15.8 | -2.9 | 18.7 | 791 | 87 | 49 |
| 1 | 5.7 | 15.8 | -2.9 | 18.7 | 791 | 87 | 49 |
Preparamos para ver los modelos
library(tidyverse)
library(MuMIn)
library(broom)
sp2 <- read_rds("sp2.rds")
SA <- read_rds("SA.rds")
Generar modelo global y lidiar con multicolinearidad
- Modelo general
FitGlob <- glm(presence ~ ., family = binomial,
data = sp2)
- Multicolinearidad
smat <- abs(cor(sp2[, -1])) <=
0.7
smat[!lower.tri(smat)] <- NA
- Número de variables máxima
K = floor(nrow(sp2)/10)
Selección de modelos
options(na.action = "na.fail")
Selected <- dredge(FitGlob, subset = smat,
m.lim = c(0, K))
| (Intercept) | PPAnual | PPMesHum | PPMesSeco | TempMedia | TempMesCalido | TempMesFrio | TempRangoAnual | df | logLik | AICc | delta | weight | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 76 | 7.010 | -0.004 | 0.056 | NA | -0.257 | NA | NA | -0.192 | 5 | -169.152 | 348.356 | 0.000 | 1 |
| 87 | 6.547 | NA | 0.025 | -0.024 | NA | -0.232 | NA | -0.053 | 5 | -177.723 | 365.498 | 17.142 | 0 |
| 23 | 5.589 | NA | 0.027 | -0.024 | NA | -0.243 | NA | NA | 4 | -178.758 | 365.550 | 17.194 | 0 |
| 84 | 6.790 | -0.005 | 0.062 | NA | NA | -0.229 | NA | -0.073 | 5 | -178.775 | 367.602 | 19.246 | 0 |
| 20 | 5.452 | -0.005 | 0.064 | NA | NA | -0.243 | NA | NA | 4 | -180.696 | 369.426 | 21.070 | 0 |
| 74 | 8.048 | -0.002 | NA | NA | -0.295 | NA | NA | -0.231 | 4 | -183.025 | 374.084 | 25.728 | 0 |
| 12 | 2.854 | -0.004 | 0.064 | NA | -0.280 | NA | NA | NA | 4 | -183.432 | 374.898 | 26.542 | 0 |
| 85 | 7.650 | NA | NA | -0.017 | NA | -0.241 | NA | -0.092 | 4 | -184.972 | 377.980 | 29.624 | 0 |
| 21 | 5.952 | NA | NA | -0.015 | NA | -0.257 | NA | NA | 3 | -188.641 | 383.302 | 34.946 | 0 |
| 75 | 6.040 | NA | -0.008 | NA | -0.333 | NA | NA | -0.157 | 4 | -197.941 | 403.916 | 55.560 | 0 |
| 73 | 5.041 | NA | NA | NA | -0.330 | NA | NA | -0.121 | 3 | -199.734 | 405.489 | 57.133 | 0 |
| 82 | 7.846 | -0.002 | NA | NA | NA | -0.268 | NA | -0.093 | 4 | -199.018 | 406.071 | 57.716 | 0 |
| 18 | 5.939 | -0.001 | NA | NA | NA | -0.278 | NA | NA | 3 | -202.418 | 410.857 | 62.501 | 0 |
| 10 | 2.817 | -0.001 | NA | NA | -0.307 | NA | NA | NA | 3 | -204.386 | 414.792 | 66.436 | 0 |
| 9 | 2.519 | NA | NA | NA | -0.323 | NA | NA | NA | 2 | -208.132 | 420.274 | 71.918 | 0 |
| 11 | 2.468 | NA | 0.002 | NA | -0.323 | NA | NA | NA | 3 | -208.020 | 422.061 | 73.705 | 0 |
| 19 | 5.884 | NA | -0.007 | NA | NA | -0.313 | NA | NA | 3 | -217.442 | 440.905 | 92.549 | 0 |
| 17 | 5.638 | NA | NA | NA | NA | -0.311 | NA | NA | 2 | -219.195 | 442.400 | 94.044 | 0 |
| 81 | 4.874 | NA | NA | NA | NA | -0.310 | NA | 0.038 | 3 | -218.326 | 442.672 | 94.316 | 0 |
| 97 | 4.874 | NA | NA | NA | NA | NA | -0.310 | -0.272 | 3 | -218.326 | 442.672 | 94.316 | 0 |
| 83 | 5.556 | NA | -0.006 | NA | NA | -0.312 | NA | 0.015 | 4 | -217.348 | 442.730 | 94.374 | 0 |
| 99 | 5.556 | NA | -0.006 | NA | NA | NA | -0.312 | -0.298 | 4 | -217.348 | 442.730 | 94.374 | 0 |
| 68 | 4.684 | -0.007 | 0.087 | NA | NA | NA | NA | -0.171 | 4 | -237.073 | 482.180 | 133.824 | 0 |
| 71 | 4.588 | NA | 0.031 | -0.036 | NA | NA | NA | -0.161 | 4 | -238.559 | 485.152 | 136.796 | 0 |
| 35 | -1.033 | NA | 0.010 | NA | NA | NA | -0.260 | NA | 3 | -258.723 | 523.467 | 175.111 | 0 |
| 69 | 5.250 | NA | NA | -0.027 | NA | NA | NA | -0.185 | 3 | -259.255 | 524.531 | 176.175 | 0 |
| 7 | 0.875 | NA | 0.037 | -0.034 | NA | NA | NA | NA | 3 | -260.103 | 526.227 | 177.871 | 0 |
| 4 | 0.751 | -0.007 | 0.086 | NA | NA | NA | NA | NA | 3 | -260.504 | 527.028 | 178.672 | 0 |
| 33 | -0.721 | NA | NA | NA | NA | NA | -0.245 | NA | 2 | -263.905 | 531.820 | 183.464 | 0 |
| 5 | 0.951 | NA | NA | -0.021 | NA | NA | NA | NA | 2 | -292.561 | 589.133 | 240.777 | 0 |
| 66 | 4.059 | -0.003 | NA | NA | NA | NA | NA | -0.153 | 3 | -317.702 | 641.425 | 293.069 | 0 |
| 2 | 0.425 | -0.002 | NA | NA | NA | NA | NA | NA | 2 | -343.323 | 690.657 | 342.301 | 0 |
| 65 | -2.909 | NA | NA | NA | NA | NA | NA | 0.059 | 2 | -457.796 | 919.602 | 571.246 | 0 |
| 67 | -2.749 | NA | -0.002 | NA | NA | NA | NA | 0.054 | 3 | -457.499 | 921.020 | 572.664 | 0 |
| 3 | -1.656 | NA | -0.005 | NA | NA | NA | NA | NA | 2 | -462.680 | 929.370 | 581.014 | 0 |
| 1 | -1.833 | NA | NA | NA | NA | NA | NA | NA | 1 | -465.380 | 932.764 | 584.408 | 0 |
El mejor modelo
options(na.action = "na.fail")
Selected <- dredge(FitGlob, subset = smat,
m.lim = c(0, K))
bestmodel <- get.models(Selected,
delta < 2)[[1]]
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 7.010 | 0.939 | 7.462 | 0 |
| PPAnual | -0.004 | 0.001 | -5.971 | 0 |
| PPMesHum | 0.056 | 0.012 | 4.732 | 0 |
| TempMedia | -0.257 | 0.027 | -9.546 | 0 |
| TempRangoAnual | -0.192 | 0.039 | -4.965 | 0 |
Hagamos cosas con el modelo
library(raster)
MapLM <- predict(SA, bestmodel,
type = "response")
plot(MapLM)
Respuestas?
NewData <- sp2 %>% dplyr::select(PPAnual,
PPMesHum, TempMedia, TempRangoAnual)
### Para temperatura media
NewDataTempMedia <- NewData %>%
mutate(PPAnual = mean(PPAnual),
PPMesHum = mean(PPMesHum),
TempRangoAnual = mean(TempRangoAnual))
Sigamos
NewDataTempMedia$TempMedia <- seq(from = min(NewDataTempMedia$TempMedia),
to = max(NewDataTempMedia$TempMedia),
length.out = nrow(NewDataTempMedia))
NewDataTempMedia$Prob <- predict(bestmodel,
NewDataTempMedia, type = "response")
NewDataTempMedia$se <- predict(bestmodel,
NewDataTempMedia, type = "response",
se.fit = TRUE)$se.fit
Que da esto?
Que variables explican que la gente salga a andar en bicicleta?
| X1 | Date | Day | High Temp (°F) | Low Temp (°F) | Precipitation | Brooklyn Bridge | Manhattan Bridge | Williamsburg Bridge | Queensboro Bridge | Total |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2016-04-01 | 2016-04-01 | 78.1 | 66.0 | 0.01 | 1704 | 3126 | 4115 | 2552 | 11497 |
| 1 | 2016-04-02 | 2016-04-02 | 55.0 | 48.9 | 0.15 | 827 | 1646 | 2565 | 1884 | 6922 |
| 2 | 2016-04-03 | 2016-04-03 | 39.9 | 34.0 | 0.09 | 526 | 1232 | 1695 | 1306 | 4759 |
| 3 | 2016-04-04 | 2016-04-04 | 44.1 | 33.1 | 0.47 (S) | 521 | 1067 | 1440 | 1307 | 4335 |
| 4 | 2016-04-05 | 2016-04-05 | 42.1 | 26.1 | 0 | 1416 | 2617 | 3081 | 2357 | 9471 |
| 5 | 2016-04-06 | 2016-04-06 | 45.0 | 30.0 | 0 | 1885 | 3329 | 3856 | 2849 | 11919 |
| 6 | 2016-04-07 | 2016-04-07 | 57.0 | 53.1 | 0.09 | 1276 | 2581 | 3282 | 2457 | 9596 |
| 7 | 2016-04-08 | 2016-04-08 | 46.9 | 44.1 | 0.01 | 1982 | 3455 | 4113 | 3194 | 12744 |
| 8 | 2016-04-09 | 2016-04-09 | 43.0 | 37.9 | 0.09 | 504 | 997 | 1507 | 1502 | 4510 |
| 9 | 2016-04-10 | 2016-04-10 | 48.9 | 30.9 | 0 | 1447 | 2387 | 3132 | 2160 | 9126 |
| 10 | 2016-04-11 | 2016-04-11 | 62.1 | 46.0 | 0.01 | 2005 | 3791 | 4334 | 3182 | 13312 |
| 11 | 2016-04-12 | 2016-04-12 | 57.0 | 45.0 | 0.2 | 1045 | 2178 | 2762 | 2082 | 8067 |
| 12 | 2016-04-13 | 2016-04-13 | 57.0 | 39.9 | 0 | 2840 | 5395 | 5995 | 4192 | 18422 |
| 13 | 2016-04-14 | 2016-04-14 | 62.1 | 44.6 | 0 | 2861 | 5309 | 6030 | 4115 | 18315 |
| 14 | 2016-04-15 | 2016-04-15 | 64.0 | 44.1 | 0 | 2770 | 5072 | 5816 | 3912 | 17570 |
| 15 | 2016-04-16 | 2016-04-16 | 66.0 | 45.0 | 0 | 2384 | 4316 | 5624 | 4051 | 16375 |
| 16 | 2016-04-17 | 2016-04-17 | 73.9 | 46.0 | 0 | 3147 | 4969 | 5867 | 4197 | 18180 |
| 17 | 2016-04-18 | 2016-04-18 | 81.0 | 52.0 | 0 | 3871 | 6823 | 7432 | 4964 | 23090 |
| 18 | 2016-04-19 | 2016-04-19 | 71.1 | 63.0 | 0 | 3501 | 6951 | 7834 | 5032 | 23318 |
| 19 | 2016-04-20 | 2016-04-20 | 68.0 | 50.0 | 0 | 3450 | 6574 | 7639 | 4928 | 22591 |
Machine learning
Predecir la calidad del vino
- Predecir quality type
| fixed acidity | volatile acidity | citric acid | residual sugar | chlorides | free sulfur dioxide | total sulfur dioxide | density | pH | sulphates | alcohol | quality |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 7.4 | 0.700 | 0.00 | 1.9 | 0.076 | 11 | 34 | 0.998 | 3.51 | 0.56 | 9.4 | 5 |
| 7.8 | 0.760 | 0.04 | 2.3 | 0.092 | 15 | 54 | 0.997 | 3.26 | 0.65 | 9.8 | 5 |
| 11.2 | 0.280 | 0.56 | 1.9 | 0.075 | 17 | 60 | 0.998 | 3.16 | 0.58 | 9.8 | 6 |
| 7.4 | 0.700 | 0.00 | 1.9 | 0.076 | 11 | 34 | 0.998 | 3.51 | 0.56 | 9.4 | 5 |
| 7.4 | 0.660 | 0.00 | 1.8 | 0.075 | 13 | 40 | 0.998 | 3.51 | 0.56 | 9.4 | 5 |
| 7.9 | 0.600 | 0.06 | 1.6 | 0.069 | 15 | 59 | 0.996 | 3.30 | 0.46 | 9.4 | 5 |
| 7.8 | 0.580 | 0.02 | 2.0 | 0.073 | 9 | 18 | 0.997 | 3.36 | 0.57 | 9.5 | 7 |
| 7.5 | 0.500 | 0.36 | 6.1 | 0.071 | 17 | 102 | 0.998 | 3.35 | 0.80 | 10.5 | 5 |
| 7.5 | 0.500 | 0.36 | 6.1 | 0.071 | 17 | 102 | 0.998 | 3.35 | 0.80 | 10.5 | 5 |
| 5.6 | 0.615 | 0.00 | 1.6 | 0.089 | 16 | 59 | 0.994 | 3.58 | 0.52 | 9.9 | 5 |
| 7.8 | 0.610 | 0.29 | 1.6 | 0.114 | 9 | 29 | 0.997 | 3.26 | 1.56 | 9.1 | 5 |
| 8.9 | 0.620 | 0.19 | 3.9 | 0.170 | 51 | 148 | 0.999 | 3.17 | 0.93 | 9.2 | 5 |
| 8.5 | 0.280 | 0.56 | 1.8 | 0.092 | 35 | 103 | 0.997 | 3.30 | 0.75 | 10.5 | 7 |
| 8.1 | 0.560 | 0.28 | 1.7 | 0.368 | 16 | 56 | 0.997 | 3.11 | 1.28 | 9.3 | 5 |
| 7.4 | 0.590 | 0.08 | 4.4 | 0.086 | 6 | 29 | 0.997 | 3.38 | 0.50 | 9.0 | 4 |
| 7.9 | 0.320 | 0.51 | 1.8 | 0.341 | 17 | 56 | 0.997 | 3.04 | 1.08 | 9.2 | 6 |
| 8.9 | 0.220 | 0.48 | 1.8 | 0.077 | 29 | 60 | 0.997 | 3.39 | 0.53 | 9.4 | 6 |
| 7.6 | 0.390 | 0.31 | 2.3 | 0.082 | 23 | 71 | 0.998 | 3.52 | 0.65 | 9.7 | 5 |
| 8.5 | 0.490 | 0.11 | 2.3 | 0.084 | 9 | 67 | 0.997 | 3.17 | 0.53 | 9.4 | 5 |
| 6.9 | 0.400 | 0.14 | 2.4 | 0.085 | 21 | 40 | 0.997 | 3.43 | 0.63 | 9.7 | 6 |
Predicción del destino de perros en refugio
- Predecir outcome type
| animal_id | animal_type | breed | color | datetime | name | outcome_type | sex_upon_outcome | Age |
|---|---|---|---|---|---|---|---|---|
| A666430 | Dog | Beagle Mix | White/Brown | 2013-11-07 11:47:00 | Lucy | Transfer | Spayed Female | 1.00 |
| A675708 | Dog | Pit Bull | Blue/White | 2014-06-03 14:20:00 | *Johnny | Adoption | Neutered Male | 1.18 |
| A680386 | Dog | Miniature Schnauzer Mix | White | 2014-06-15 15:50:00 | Monday | Transfer | Neutered Male | 9.04 |
| A692618 | Dog | Chihuahua Shorthair Mix | Brown | 2014-12-08 15:55:00 | *Ella | Transfer | Spayed Female | 3.05 |
| A673652 | Dog | Papillon/Border Collie | Black/White | 2014-03-28 14:39:00 | Fancy | Transfer | Neutered Male | 2.08 |
| A677679 | Dog | Chihuahua Shorthair/Pomeranian | Black | 2014-05-26 19:10:00 | Kash | Adoption | Neutered Male | 0.22 |
| A640655 | Dog | Miniature Schnauzer/Miniature Poodle | White | 2014-04-25 11:17:00 | Sandy | Return to Owner | Spayed Female | 5.00 |
| A690350 | Dog | Labrador Retriever Mix | Black | 2014-10-26 18:20:00 | Shy | Return to Owner | Neutered Male | 8.03 |
| A674298 | Dog | Pit Bull Mix | Brown Brindle/White | 2014-04-16 12:51:00 | *Newt | Transfer | Neutered Male | 1.10 |
| A692378 | Dog | German Shepherd/Labrador Retriever | Black/White | 2014-12-21 18:20:00 | *Bonnie | Adoption | Spayed Female | 0.17 |
| A666660 | Dog | Beagle Mix | Tan/White | 2014-12-11 17:52:00 | Dolly | Adoption | Spayed Female | 2.60 |
| A689952 | Dog | Mastiff Mix | Brown Brindle | 2014-10-19 17:54:00 | Olive | Adoption | Spayed Female | 1.02 |
| A669621 | Dog | Plott Hound Mix | Brown Brindle/Black | 2013-12-25 09:47:00 | NA | Transfer | Intact Female | 0.34 |
| A690699 | Dog | Chihuahua Shorthair Mix | Blue/Tan | 2014-11-04 18:03:00 | Minnie | Transfer | Intact Female | 0.39 |
| A669918 | Dog | Labrador Retriever Mix | Tan/White | 2014-01-03 17:47:00 | NA | Adoption | Spayed Female | 0.26 |
| A678098 | Dog | Labrador Retriever Mix | Black/White | 2014-07-09 11:54:00 | Shades | Adoption | Neutered Male | 12.18 |
| A677038 | Dog | Chihuahua Shorthair Mix | Buff | 2014-04-26 13:00:00 | *Penny | Adoption | Spayed Female | 2.01 |
| A665344 | Dog | Chihuahua Shorthair Mix | Red/White | 2013-11-02 17:47:00 | Ginger | Adoption | Spayed Female | 4.05 |
| A675394 | Dog | Miniature Pinscher Mix | Black/White | 2014-04-03 13:26:00 | *Josh | Transfer | Intact Male | 1.52 |
| A673439 | Dog | Cardigan Welsh Corgi/English Setter | White/Black | 2014-02-28 10:47:00 | Aria | Adoption | Spayed Female | 0.26 |
Tareas
Que explica la felicidad de la gente
| Country | Happiness.Rank | Happiness.Score | Whisker.high | Whisker.low | Economy..GDP.per.Capita. | Family | Health..Life.Expectancy. | Freedom | Generosity | Trust..Government.Corruption. | Dystopia.Residual |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Norway | 1 | 7.537 | 7.594 | 7.480 | 1.616 | 1.534 | 0.797 | 0.635 | 0.362 | 0.316 | 2.277 |
| Denmark | 2 | 7.522 | 7.582 | 7.462 | 1.482 | 1.551 | 0.793 | 0.626 | 0.355 | 0.401 | 2.314 |
| Iceland | 3 | 7.504 | 7.622 | 7.386 | 1.481 | 1.611 | 0.834 | 0.627 | 0.476 | 0.154 | 2.323 |
| Switzerland | 4 | 7.494 | 7.562 | 7.426 | 1.565 | 1.517 | 0.858 | 0.620 | 0.291 | 0.367 | 2.277 |
| Finland | 5 | 7.469 | 7.528 | 7.410 | 1.444 | 1.540 | 0.809 | 0.618 | 0.245 | 0.383 | 2.430 |
| Netherlands | 6 | 7.377 | 7.427 | 7.327 | 1.504 | 1.429 | 0.811 | 0.585 | 0.470 | 0.283 | 2.295 |
| Canada | 7 | 7.316 | 7.384 | 7.248 | 1.479 | 1.481 | 0.835 | 0.611 | 0.436 | 0.287 | 2.187 |
| New Zealand | 8 | 7.314 | 7.380 | 7.248 | 1.406 | 1.548 | 0.817 | 0.614 | 0.500 | 0.383 | 2.046 |
| Sweden | 9 | 7.284 | 7.344 | 7.224 | 1.494 | 1.478 | 0.831 | 0.613 | 0.385 | 0.384 | 2.098 |
| Australia | 10 | 7.284 | 7.357 | 7.211 | 1.484 | 1.510 | 0.844 | 0.602 | 0.478 | 0.301 | 2.065 |
| Israel | 11 | 7.213 | 7.280 | 7.146 | 1.375 | 1.376 | 0.838 | 0.406 | 0.330 | 0.085 | 2.802 |
| Costa Rica | 12 | 7.079 | 7.168 | 6.990 | 1.110 | 1.416 | 0.760 | 0.580 | 0.215 | 0.100 | 2.899 |
| Austria | 13 | 7.006 | 7.071 | 6.941 | 1.487 | 1.460 | 0.815 | 0.568 | 0.316 | 0.221 | 2.139 |
| United States | 14 | 6.993 | 7.075 | 6.911 | 1.546 | 1.420 | 0.774 | 0.506 | 0.393 | 0.136 | 2.218 |
| Ireland | 15 | 6.977 | 7.043 | 6.911 | 1.536 | 1.558 | 0.810 | 0.573 | 0.428 | 0.298 | 1.774 |
| Germany | 16 | 6.951 | 7.005 | 6.897 | 1.488 | 1.473 | 0.799 | 0.563 | 0.336 | 0.277 | 2.016 |
| Belgium | 17 | 6.891 | 6.956 | 6.826 | 1.464 | 1.462 | 0.818 | 0.540 | 0.232 | 0.251 | 2.124 |
| Luxembourg | 18 | 6.863 | 6.924 | 6.802 | 1.742 | 1.458 | 0.845 | 0.597 | 0.283 | 0.319 | 1.620 |
| United Kingdom | 19 | 6.714 | 6.784 | 6.644 | 1.442 | 1.496 | 0.805 | 0.508 | 0.493 | 0.265 | 1.704 |
| Chile | 20 | 6.652 | 6.739 | 6.565 | 1.253 | 1.284 | 0.819 | 0.377 | 0.327 | 0.082 | 2.510 |
| United Arab Emirates | 21 | 6.648 | 6.722 | 6.574 | 1.626 | 1.266 | 0.727 | 0.608 | 0.361 | 0.324 | 1.735 |
| Brazil | 22 | 6.635 | 6.725 | 6.545 | 1.107 | 1.431 | 0.617 | 0.437 | 0.162 | 0.111 | 2.769 |
| Czech Republic | 23 | 6.609 | 6.684 | 6.534 | 1.353 | 1.434 | 0.754 | 0.491 | 0.088 | 0.037 | 2.452 |
| Argentina | 24 | 6.599 | 6.690 | 6.508 | 1.185 | 1.440 | 0.695 | 0.495 | 0.109 | 0.060 | 2.614 |
| Mexico | 25 | 6.578 | 6.671 | 6.485 | 1.153 | 1.211 | 0.710 | 0.413 | 0.121 | 0.133 | 2.837 |
Recomendación de cervezas
- Predecir review_overall
| brewery_id | brewery_name | review_overall | review_profilename | beer_style | beer_name | beer_abv | beer_beerid |
|---|---|---|---|---|---|---|---|
| 10325 | Vecchio Birraio | 1.5 | stcules | Hefeweizen | Sausa Weizen | 5.0 | 47986 |
| 10325 | Vecchio Birraio | 3.0 | stcules | English Strong Ale | Red Moon | 6.2 | 48213 |
| 10325 | Vecchio Birraio | 3.0 | stcules | Foreign / Export Stout | Black Horse Black Beer | 6.5 | 48215 |
| 1075 | Caldera Brewing Company | 4.0 | johnmichaelsen | American Double / Imperial IPA | Cauldron DIPA | 7.7 | 64883 |
| 1075 | Caldera Brewing Company | 3.0 | oline73 | Herbed / Spiced Beer | Caldera Ginger Beer | 4.7 | 52159 |
| 1075 | Caldera Brewing Company | 3.0 | alpinebryant | Herbed / Spiced Beer | Caldera Ginger Beer | 4.7 | 52159 |
| 1075 | Caldera Brewing Company | 4.5 | augustgarage | Herbed / Spiced Beer | Caldera Ginger Beer | 4.7 | 52159 |
| 1075 | Caldera Brewing Company | 5.0 | MadeInOregon | Herbed / Spiced Beer | Caldera Ginger Beer | 4.7 | 52159 |
| 1075 | Caldera Brewing Company | 4.0 | rawthar | Herbed / Spiced Beer | Caldera Ginger Beer | 4.7 | 52159 |
| 1075 | Caldera Brewing Company | 4.0 | Halcyondays | Herbed / Spiced Beer | Caldera Ginger Beer | 4.7 | 52159 |
| 1075 | Caldera Brewing Company | 3.0 | Beerandraiderfan | Oatmeal Stout | Caldera Oatmeal Stout | 7.2 | 10789 |
| 1075 | Caldera Brewing Company | 4.5 | UCLABrewN84 | Rauchbier | Rauch Ür Bock | 7.4 | 58046 |
| 1075 | Caldera Brewing Company | 4.0 | zaphodchak | Rauchbier | Rauch Ür Bock | 7.4 | 58046 |
| 1075 | Caldera Brewing Company | 4.0 | Tilley4 | Rauchbier | Rauch Ür Bock | 7.4 | 58046 |
| 1075 | Caldera Brewing Company | 4.5 | mikedrinksbeer2 | Rauchbier | Rauch Ür Bock | 7.4 | 58046 |
| 1075 | Caldera Brewing Company | 4.5 | titosupertramp | Rauchbier | Rauch Ür Bock | 7.4 | 58046 |
| 1075 | Caldera Brewing Company | 5.0 | optimator13 | Rauchbier | Rauch Ür Bock | 7.4 | 58046 |
| 1075 | Caldera Brewing Company | 4.5 | Blakaeris | Rauchbier | Rauch Ür Bock | 7.4 | 58046 |
| 1075 | Caldera Brewing Company | 4.5 | Klym | Rauchbier | Rauch Ür Bock | 7.4 | 58046 |
| 1075 | Caldera Brewing Company | 4.5 | flexabull | Rauchbier | Rauch Ür Bock | 7.4 | 58046 |