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cars.R
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cars.R
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library(e1071)
library("nnet")
library(VGAM)
library(caret)
# load cars data
car_eval<-read.csv("cars-train.csv")
head(car_eval)
id<-car_eval[,1]
car_eval<-car_eval[,-1]
# look at data
summary(car_eval)
str(car_eval)
# find number of missing in each variable
apply(is.na(car_eval), 2, sum) # no missing values
#**********************************************GLM*****************************************************
# build vector generalized linear model
model1<-vglm(class~buying+maint+doors+persons+lug_boot+safety,family = "multinomial",data=car_eval)
# select only the predictors: update with new data
x<-car_eval[,1:6]
# select only the target: need to be updated with new data
y<-car_eval[,7]
probability<-predict(model1,x,type="response")
car_eval$pred_log_reg<-apply(probability,1,which.max)
car_eval$pred_log_reg[which(car_eval$pred_log_reg=="1")]<-levels(car_eval$class)[1]
car_eval$pred_log_reg[which(car_eval$pred_log_reg=="2")]<-levels(car_eval$class)[2]
car_eval$pred_log_reg[which(car_eval$pred_log_reg=="3")]<-levels(car_eval$class)[3]
car_eval$pred_log_reg[which(car_eval$pred_log_reg=="4")]<-levels(car_eval$class)[4]
mtab<-table(car_eval$pred_log_reg,car_eval$class)
confusionMatrix(mtab)
model1_acc<-0.9402 # training set accuracy
# validate model with hold out data
car_eval_test<-read.csv("cars-test.csv")
id_test<-car_eval_test[,1]
car_eval_test<-car_eval_test[,-1]
# select only the predictors: update with new data
x_test<-car_eval_test[,1:6]
# select only the target: need to be updated with new data
y_test<-car_eval_test[,7]
probability<-predict(model1,x_test,type="response")
car_eval_test$pred_log_reg<-apply(probability,1,which.max)
car_eval_test$pred_log_reg[which(car_eval_test$pred_log_reg=="1")]<-levels(car_eval_test$class)[1]
car_eval_test$pred_log_reg[which(car_eval_test$pred_log_reg=="2")]<-levels(car_eval_test$class)[2]
car_eval_test$pred_log_reg[which(car_eval_test$pred_log_reg=="3")]<-levels(car_eval_test$class)[3]
car_eval_test$pred_log_reg[which(car_eval_test$pred_log_reg=="4")]<-levels(car_eval_test$class)[4]
mtab_test<-table(car_eval_test$pred_log_reg,car_eval_test$class)
confusionMatrix(mtab_test)
model2_acc_test<-0.9198
valid<-read.csv("cars-hold-validate.csv")
id<-valid[,1]
valid<-valid[,-1]
valid$class<-NA
x<-valid[,-7]
probability<-predict(model1,valid,type="response")
valid$pred_log_reg<-apply(probability,1,which.max)
valid$pred_log_reg[which(valid$pred_log_reg=="1")]<-"acc"
valid$pred_log_reg[which(valid$pred_log_reg=="2")]<-"good"
valid$pred_log_reg[which(valid$pred_log_reg=="3")]<-"unacc"
valid$pred_log_reg[which(valid$pred_log_reg=="4")]<-"vgood"
predictions <- data.frame(car.id=id, class=valid$pred_log_reg)
write.csv(predictions, "vglm_output.csv")
#*******************************************NNET**********************************************************
# load cars data
car_eval<-read.csv("cars-train.csv")
head(car_eval)
id<-car_eval[,1]
car_eval<-car_eval[,-1]
model2<-nnet(class~., data=car_eval, size=4)
# select only the predictors: update with new data
x<-car_eval[,1:6]
# select only the target: need to be updated with new data
y<-car_eval[,7]
car_eval$pred_nnet<-predict(model2,x,type="class")
#Accuracy of the model
mtab<-table(car_eval$pred_nnet,car_eval$class)
confusionMatrix(mtab)
model2_acc<-0.9887
car_test<-read.csv("cars-test.csv")
id<-car_test[,1]
car_test<-car_test[,-1]
# select only the predictors: update with new data
x<-car_test[,1:6]
# select only the target: need to be updated with new data
y<-car_test[,7]
car_test$pred_nnet<-predict(model2,x,type="class")
#Accuracy of the model
mtab<-table(car_test$pred_nnet,car_test$class)
confusionMatrix(mtab)
model2_acc_test<-0.9444
car_valid<-read.csv("cars-hold-validate.csv")
id<-car_valid[,1]
car_valid<-car_valid[,-1]
car_valid$class<-NA
# select only the predictors: update with new data
x<-car_valid[,1:6]
# select only the target: need to be updated with new data
y<-car_valid[,7]
car_valid$pred_nnet<-predict(model2,x,type="class")
predictions <- data.frame(car.id=id, class=car_valid$pred_nnet)
write.csv(predictions, "nnet_output.csv")
#***********************************************
# load cars data
car_eval<-read.csv("cars-train.csv")
head(car_eval)
id<-car_eval[,1]
car_eval<-car_eval[,-1]
library(RWeka)
#Build the model
model3<-J48(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval)
# select only the predictors: update with new data
x<-car_eval[,1:6]
# select only the target: need to be updated with new data
y<-car_eval[,7]
car_eval$pred_j48<-predict(model3,x)
#Accuracy of the model
mtab<-table(car_eval$pred_j48,car_eval$class)
confusionMatrix(mtab)
model3_acc<-0.9247
car_test<-read.csv("cars-test.csv")
id<-car_test[,1]
car_test<-car_test[,-1]
# select only the predictors: update with new data
x<-car_test[,1:6]
# select only the target: need to be updated with new data
y<-car_test[,7]
car_test$pred_j48<-predict(model3,x,type="class")
#Accuracy of the model
mtab<-table(car_test$pred_j48,car_test$class)
confusionMatrix(mtab)
model3_acc_test<-0.8981
car_valid<-read.csv("cars-hold-validate.csv")
id<-car_valid[,1]
car_valid<-car_valid[,-1]
car_valid$class<-NA
# select only the predictors: update with new data
x<-car_valid[,1:6]
# select only the target: need to be updated with new data
y<-car_valid[,7]
car_valid$pred_j48<-predict(model3,x,type="class")
predictions <- data.frame(car.id=id, class=car_valid$pred_j48)
write.csv(predictions, "c4.5_output.csv")
#*************************************************************
library(RWeka)
# load cars data
car_eval<-read.csv("cars-train.csv")
head(car_eval)
id<-car_eval[,1]
car_eval<-car_eval[,-1]
library(RWeka)
#Build the model
model4<-PART(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval)
# select only the predictors: update with new data
x<-car_eval[,1:6]
# select only the target: need to be updated with new data
y<-car_eval[,7]
car_eval$pred_part<-predict(model4,x)
part<-predict(model4,x)
#Accuracy of the model
mtab<-table(car_eval$pred_part,car_eval$class)
confusionMatrix(mtab)
model4_acc<-0.9732
car_test<-read.csv("cars-test.csv")
id<-car_test[,1]
car_test<-car_test[,-1]
# select only the predictors: update with new data
x<-car_test[,1:6]
# select only the target: need to be updated with new data
y<-car_test[,7]
car_test$pred_part<-predict(model4,x,type="class")
part_test<-predict(model4,x,type="class")
#Accuracy of the model
mtab<-table(car_test$pred_part,car_test$class)
confusionMatrix(mtab)
model4_acc_test<-0.9568
car_valid<-read.csv("cars-hold-validate.csv")
id<-car_valid[,1]
car_valid<-car_valid[,-1]
car_valid$class<-NA
# select only the predictors: update with new data
x<-car_valid[,1:6]
# select only the target: need to be updated with new data
y<-car_valid[,7]
car_valid$pred_part<-predict(model4,x,type="class")
part_valid<-predict(model4,x,type="class")
predictions <- data.frame(car.id=id, class=car_valid$pred_part)
write.csv(predictions, "part_output.csv")
#**************************************************************
library(ipred)
car_eval<-read.csv("cars-train.csv")
head(car_eval)
id<-car_eval[,1]
car_eval<-car_eval[,-1]
#Build the model
model5<-bagging(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval, nbagg=2000)
# select only the predictors: update with new data
x<-car_eval[,1:6]
# select only the target: need to be updated with new data
y<-car_eval[,7]
car_eval$pred_bag<-predict(model5,x)
bag<-predict(model5,x)
#Accuracy of the model
mtab<-table(car_eval$pred_bag,car_eval$class)
confusionMatrix(mtab)
model5_acc<-1
car_test<-read.csv("cars-test.csv")
id<-car_test[,1]
car_test<-car_test[,-1]
# select only the predictors: update with new data
x<-car_test[,1:6]
# select only the target: need to be updated with new data
y<-car_test[,7]
car_test$pred_bag<-predict(model5,x,type="class")
bag_test<-predict(model5,x,type="class")
#Accuracy of the model
mtab<-table(car_test$pred_bag,car_test$class)
confusionMatrix(mtab)
model5_acc_test<-0.9815
car_valid<-read.csv("cars-hold-validate.csv")
id<-car_valid[,1]
car_valid<-car_valid[,-1]
car_valid$class<-NA
# select only the predictors: update with new data
x<-car_valid[,1:6]
# select only the target: need to be updated with new data
y<-car_valid[,7]
car_valid$pred_bag<-predict(model5,x,type="class")
bag_valid<-predict(model5,x,type="class")
predictions <- data.frame(car.id=id, class=car_valid$pred_bag)
write.csv(predictions, "bag_output2.csv")
#******************************************************************
library(randomForest)
car_eval<-read.csv("cars-train.csv")
head(car_eval)
id<-car_eval[,1]
car_eval<-car_eval[,-1]
#Build the model
model6<-randomForest(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval, ntree=6000)
# select only the predictors: update with new data
x<-car_eval[,1:6]
# select only the target: need to be updated with new data
y<-car_eval[,7]
car_eval$pred_rf<-predict(model6,x)
#Accuracy of the model
mtab<-table(car_eval$pred_rf,car_eval$class)
confusionMatrix(mtab)
model6_acc<-0.9979
car_test<-read.csv("cars-test.csv")
id<-car_test[,1]
car_test<-car_test[,-1]
# select only the predictors: update with new data
x<-car_test[,1:6]
# select only the target: need to be updated with new data
y<-car_test[,7]
car_test$pred_rf<-predict(model6,x,type="class")
#Accuracy of the model
mtab<-table(car_test$pred_rf,car_test$class)
confusionMatrix(mtab)
model6_acc_test<-0.9691
car_valid<-read.csv("cars-hold-validate.csv")
id<-car_valid[,1]
car_valid<-car_valid[,-1]
car_valid$class<-NA
# select only the predictors: update with new data
x<-car_valid[,1:6]
# select only the target: need to be updated with new data
y<-car_valid[,7]
car_valid$pred_rf<-predict(model6,x,type="class")
predictions <- data.frame(car.id=id, class=car_valid$pred_rf)
write.csv(predictions, "rf_output.csv")
#****************************************************************
# load cars data
car_eval<-read.csv("cars-train.csv")
head(car_eval)
id<-car_eval[,1]
car_eval<-car_eval[,-1]
car_final<-car_eval
car_final$part<-part
car_final$bag<-bag
names(car_final)
# build vector generalized linear model
model7<-vglm(class~buying+maint+doors+persons+lug_boot+safety+part+bag,family = "multinomial",data=car_final)
# select only the predictors: update with new data
x<-car_final[,-7]
# select only the target: need to be updated with new data
y<-car_final[,7]
probability<-predict(model7,x,type="response")
car_final$pred_log_reg<-apply(probability,1,which.max)
car_final$pred_log_reg[which(car_final$pred_log_reg=="1")]<-levels(car_final$class)[1]
car_final$pred_log_reg[which(car_final$pred_log_reg=="2")]<-levels(car_final$class)[2]
car_final$pred_log_reg[which(car_final$pred_log_reg=="3")]<-levels(car_final$class)[3]
car_final$pred_log_reg[which(car_final$pred_log_reg=="4")]<-levels(car_final$class)[4]
mtab<-table(car_final$pred_log_reg,car_final$class)
confusionMatrix(mtab)
model1_acc<-1
# validate model with hold out data
car_eval_test<-read.csv("cars-test.csv")
car_eval_test$part<-part_test
car_eval_test$bag<-bag_test
names(car_eval_test)
id_test<-car_eval_test[,1]
car_eval_test<-car_eval_test[,-1]
# select only the predictors: update with new data
x_test<-car_eval_test[,-7]
# select only the target: need to be updated with new data
y_test<-car_eval_test[,7]
probability<-predict(model7,x_test,type="response")
car_eval_test$pred_log_reg<-apply(probability,1,which.max)
car_eval_test$pred_log_reg[which(car_eval_test$pred_log_reg=="1")]<-levels(car_eval_test$class)[1]
car_eval_test$pred_log_reg[which(car_eval_test$pred_log_reg=="2")]<-levels(car_eval_test$class)[2]
car_eval_test$pred_log_reg[which(car_eval_test$pred_log_reg=="3")]<-levels(car_eval_test$class)[3]
car_eval_test$pred_log_reg[which(car_eval_test$pred_log_reg=="4")]<-levels(car_eval_test$class)[4]
mtab_test<-table(car_eval_test$pred_log_reg,car_eval_test$class)
confusionMatrix(mtab_test)
model2_acc_test<-0.9815
valid<-read.csv("cars-hold-validate.csv")
valid$part<-part_valid
valid$bag<-bag_valid
id<-valid[,1]
valid<-valid[,-1]
valid$class<-NA
x<-valid[,-7]
probability<-predict(model7,valid,type="response")
valid$pred_log_reg<-apply(probability,1,which.max)
valid$pred_log_reg[which(valid$pred_log_reg=="1")]<-"acc"
valid$pred_log_reg[which(valid$pred_log_reg=="2")]<-"good"
valid$pred_log_reg[which(valid$pred_log_reg=="3")]<-"unacc"
valid$pred_log_reg[which(valid$pred_log_reg=="4")]<-"vgood"
predictions <- data.frame(car.id=id, class=valid$pred_log_reg)
write.csv(predictions, "ensemble_output.csv")