Tarea 6

Investigando el mercado de computadoras.

Suponga que Ud. está interesado/a en entrar al mercado de las computadoras. Usted cuenta con una base de datos cuyas observaciones corresponden a computadoras vendidas durante un cierto período de tiempo. Cuenta con el precio (price) de cada computadora vendida y con las siguientes características de la misma:

1. Velocidad en MhZ (speed)
2. Capacidad del disco duro (hd)
3. Memoria RAM (ram)
4. Tamaño de la pantalla (screen)
5. Entrada de cd (cd)
6. Categoría Premium (premium)
7. Año de la venta (ads)
8. Marca del computador (brand)

P1

Cargue todos los paquetes que necesita para trabajar con data.table, modelos de predicción de regresión, CART, cluster y Cross-Validation. Además, cargue la base de datos. 1 punto

install.packages("rpart")
install.packages("rpart.plot")
install.packages("factoextra")
install.packages("dbscan")

library(data.table)
library(caret)
library(ggplot2)
library(rpart)
library(rpart.plot)
library(factoextra)
library(dbscan)

base <- fread("base_tarea6.csv")

P2

Plantee dos modelos de regresión distintos que logren predecir el precio de las computadoras. 4 puntos

reg1<-lm(data=base,formula =price~speed + hd + ram)
summary(reg1)

## 
## Call:
## lm(formula = price ~ speed + hd + ram, data = base)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1103.48  -288.10   -52.59   206.38  2778.65 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1492.17753   15.53085   96.08   <2e-16 ***
## speed          5.81119    0.28274   20.55   <2e-16 ***
## hd            -0.53572    0.03578  -14.97   <2e-16 ***
## ram           78.23572    1.56848   49.88   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 437.3 on 6255 degrees of freedom
## Multiple R-squared:  0.4335, Adjusted R-squared:  0.4332 
## F-statistic:  1595 on 3 and 6255 DF,  p-value: < 2.2e-16

reg2<-lm(data=base,formula =price~speed + ads + hd + ram + screen)
summary(reg2)

## 
## Call:
## lm(formula = price ~ speed + ads + hd + ram + screen, data = base)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -907.35 -281.57  -71.97  201.70 2472.20 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -405.73236   88.39492   -4.59 4.52e-06 ***
## speed          5.75103    0.27172   21.17  < 2e-16 ***
## ads            1.44617    0.07502   19.28  < 2e-16 ***
## hd            -0.40739    0.03522  -11.56  < 2e-16 ***
## ram           73.59740    1.50119   49.03  < 2e-16 ***
## screen       107.19445    6.01382   17.82  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 415.3 on 6253 degrees of freedom
## Multiple R-squared:  0.489,  Adjusted R-squared:  0.4886 
## F-statistic:  1197 on 5 and 6253 DF,  p-value: < 2.2e-16

P3

Evalúe cual modelo predice mejor el precio de las computadoras. Sea claro/a con su criterio de evaluación. Recuerde que no es lo mismo predecir dentro de muestra que fuera de muestra. 8 puntos Pista: utilice set.seed(12345)

base[,Pred_Precio1:=predict(reg1)]
base[,Pred_Precio2:=predict(reg2)]

base[,Residuo1:=resid(reg1)]
base[,Residuo2:=resid(reg2)]


# Se hará el Cross Validation mediante el método k-folds
set.seed(12345)

setupKCV <- trainControl(method = "cv" , number = 5)

predkfolds1<-train(price~speed + hd + ram,data=base,method="lm",trControl= setupKCV)
predkfolds2<-train(price~speed + ads + hd + ram + screen,data=base,method="lm",trControl= setupKCV)

print(predkfolds1)

## Linear Regression 
## 
## 6259 samples
##    3 predictor
## 
## No pre-processing
## Resampling: Cross-Validated (5 fold) 
## Summary of sample sizes: 5007, 5007, 5007, 5007, 5008 
## Resampling results:
## 
##   RMSE      Rsquared   MAE     
##   437.1965  0.4335173  320.0107
## 
## Tuning parameter 'intercept' was held constant at a value of TRUE

print(predkfolds2)

## Linear Regression 
## 
## 6259 samples
##    5 predictor
## 
## No pre-processing
## Resampling: Cross-Validated (5 fold) 
## Summary of sample sizes: 5007, 5006, 5007, 5007, 5009 
## Resampling results:
## 
##   RMSE      Rsquared   MAE     
##   415.4366  0.4885896  307.4366
## 
## Tuning parameter 'intercept' was held constant at a value of TRUE

#El segundo modelo es mejor, debido a que el error de estimación es menor que el del primer modelo.

Ahora queremos predecir la calidad del computador, es decir, si el computador es premium (yes o no) utilizando árboles de decisión.

P4

Para esto plantee un modelo utilizando la información de la base de datos. Muestre su modelo. 6 puntos Pista: No utilice todas las variables para crear el modelo CART, seleccione algunas.

# Convertir a dummy la variable calidad del computador (premium)
base[premium=="yes",calidad:=1]
base[premium=="no",calidad:=0]

arb1 <- rpart(premium~hd+ram, data = base,method="class")
rpart.plot(arb1)

P5

Explique los resultados de su modelo. 4 puntos Importante: No es necesario explicar todos los nodos, pero si debe explicar al menos 2 nodos.

# En el primer nodo, existe un 90% de certeza de que el 51% de los computadores tengan una capacidad de menos de 365 y un 49% que sea superior a 365. Luego, con un 83% de certeza, todos los computadores (el 51%) tendrán una capacidad superior o igual a 124 hd. Y así sucesivamente se lee cada nodo...

Ahora utilizaremos el método de validación cruzada para estimar la precisión de su modelo CART.

P7

Cree el set de entrenamiento y de prueba. Utilice set.seed(12345). Muestre los resultados del set de entrenamiento. 8 puntos

set.seed(12345)

ind<-createDataPartition(base$price,times = 1,p = 0.8,list = F) 

train<-base[ind,] # datos de entrenamiento
test<-base[-ind,] # datos de prueba

train

##         V1 price speed   hd ram screen  cd multi premium ads trend brand
##    1:    1  1499    25   80   4     14  no    no     yes  94     1   Win
##    2:    2  1795    33   85   2     14  no    no     yes  94     1   Mac
##    3:    3  1595    25  170   4     15  no    no     yes  94     1   Win
##    4:    4  1849    25  170   8     14  no    no      no  94     1   Win
##    5:    5  3295    33  340  16     14  no    no     yes  94     1   Mac
##   ---                                                                   
## 5005: 6255  1690   100  528   8     15  no    no     yes  39    35   Mac
## 5006: 6256  2223    66  850  16     15 yes   yes     yes  39    35   Mac
## 5007: 6257  2654   100 1200  24     15 yes    no     yes  39    35   Mac
## 5008: 6258  2195   100  850  16     15 yes    no     yes  39    35   Win
## 5009: 6259  2490   100  850  16     17 yes    no     yes  39    35   Win
##       Pred_Precio1 Pred_Precio2     Residuo1   Residuo2 calidad
##    1:     1907.542     1636.504 -408.5424816 -137.50404       1
##    2:     1794.882     1533.281    0.1180574  261.71946       1
##    3:     1859.328     1707.034 -264.3276508 -112.03369       1
##    4:     2172.271     1894.229 -323.2705302  -45.22885       0
##    5:     2753.573     2459.761  541.4266668  835.23943       1
##   ---                                                          
## 5005:     2416.322     2207.367 -726.3217327 -517.36690       1
## 5006:     2672.125     2469.433 -449.1251564 -246.43261       1
## 5007:     3308.089     3111.161 -654.0891807 -457.16149       1
## 5008:     2869.706     2664.968 -674.7055415 -469.96760       1
## 5009:     2869.706     2879.357 -379.7055415 -389.35651       1

P8

Calcule la predicción del modelo 4 puntos

arb2 <- rpart(price~speed,data=train,method="class")
rpart.plot(arb2,box.palette = "#F7FCF5")

pred<-predict(arb2,newdata = test,type="class")
pred

##    [1] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##   [15] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##   [29] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##   [43] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##   [57] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##   [71] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##   [85] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##   [99] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [113] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [127] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [141] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [155] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [169] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [183] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [197] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [211] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [225] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [239] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [253] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [267] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [281] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [295] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [309] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [323] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [337] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [351] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [365] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [379] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [393] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [407] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [421] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [435] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [449] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [463] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [477] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [491] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [505] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [519] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [533] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [547] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [561] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [575] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [589] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [603] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [617] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [631] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [645] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [659] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [673] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [687] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [701] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [715] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [729] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [743] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [757] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [771] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [785] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [799] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [813] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [827] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [841] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [855] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [869] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [883] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [897] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [911] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [925] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [939] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [953] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [967] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [981] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
##  [995] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
## [1009] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
## [1023] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
## [1037] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
## [1051] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
## [1065] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
## [1079] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
## [1093] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
## [1107] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
## [1121] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
## [1135] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
## [1149] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
## [1163] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
## [1177] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
## [1191] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
## [1205] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
## [1219] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
## [1233] 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999
## [1247] 1999 1999 1999 1999
## 768 Levels: 949 999 1049 1088 1099 1119 1139 1149 1195 1199 1238 1239 ... 5399

P9

Calcule la matriz de confusión y la precisión del modelo. Muestre y explique ambos. 7 puntos

matconf<-table(test$price,pred)
matconf

##       pred
##        949 999 1049 1088 1099 1119 1139 1149 1195 1199 1238 1239 1245 1249 1259
##   1049   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1099   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1149   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1175   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1195   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1199   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1249   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1268   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1275   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1288   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1295   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1299   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1345   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1349   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1354   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1368   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1369   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1379   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1390   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1395   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1398   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1399   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1404   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1408   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1418   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1425   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1438   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1444   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1445   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1449   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1454   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1468   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1469   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1488   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1490   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1494   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1495   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1499   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1509   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1515   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1519   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1529   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1538   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1539   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1540   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1544   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1545   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1548   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1554   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1559   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1578   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1579   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1589   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1590   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1594   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1595   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1599   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1618   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1625   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1629   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1640   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1644   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1645   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1649   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1654   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1659   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1660   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1669   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1670   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1675   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1679   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1680   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1685   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1689   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1690   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1694   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1695   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1698   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1699   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1704   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1708   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1709   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1720   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1723   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1724   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1728   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1735   0   0    0    0    0    0    0    0    0    0    0    0    0    0    0
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##       pred
##        1878 1879 1880 1881 1888 1889 1890 1891 1893 1894 1895 1898 1899 1904
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##       pred
##        1970 1973 1974 1975 1979 1983 1985 1988 1989 1990 1994 1995 1998 1999
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##  [ reached getOption("max.print") -- omitted 309 rows ]

precis<-sum(diag(matconf))/sum(matconf)
precis

## [1] 0.0048

# Existe una precisión del modelo de 0,0048.

Un compañero de trabajo le ha sugerido que la demanda por computadoras tiene que ver con la velocidad de las mismas, por lo que debería ser posible encontrar una buena segmentación al organizar los datos en clusters por precio y velocidad. Usted, intrigado/a, decide comprobarlo. Para esto:

P10

Cree un nuevo objeto que contenta solo el precio y la velocidad (speed). 3 puntos

base2 <- base[,.(price, speed)]

P11

Obtenga 5 clusters para esta segmentación a partir del objeto anterior y agregue estos clusters a la base de datos original. 7 puntos

ggplot(data = base2,aes(x=price,y=speed))+ geom_point()

?kmeans
k5<-kmeans(x=base2,centers=5,nstart=15)

base$clkme<-k5$cluster

P12

Visualice estos clusters en un gráfico. 5 puntos

fviz_cluster(k5,data=base2,geom = "point")