car_eval_analysis.Rmd

---
title: "Car Evaluation Analysis"
author: "Suraj Vidyadaran"
date: "Sunday, February 21, 2016"
output: html_document
---




###1)Load the data
```{r}
car_eval <- read.csv("F:/Analysis_Practice/Classification/Car Evaluation/car.data.txt", header=FALSE)
colnames(car_eval)<-c("buying","maint","doors","persons","lug_boot","safety","class")
head(car_eval)
```

###2)Exploratory Data Analysis
```{r}
summary(car_eval)
str(car_eval)
```

##3)Classification Analysis
###A)Linear Classification
####1)Logistic Regression
```{r,warning=FALSE,message=FALSE}
library(VGAM)

#Build the model
model1<-vglm(class~buying+maint+doors+persons+lug_boot+safety,family = "multinomial",data=car_eval)

#Summarize the model
summary(model1)

#Predict using the model
x<-car_eval[,1:6]
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]

#Accuracy of the model
mtab<-table(car_eval$pred_log_reg,car_eval$class)
library(caret)
confusionMatrix(mtab)
```

####2)Linear Discriminant Analysis
```{r,warning=FALSE,message=FALSE}
library(MASS)

#Build the model
model2<-lda(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval)

#Summarize the model
summary(model2)

#Predict using the model
car_eval$pred_lda<-predict(model2,x)$class

#Accuracy of the model
mtab<-table(car_eval$pred_lda,car_eval$class)
confusionMatrix(mtab)
```

###B)Non-Linear Classification
####1)Mixture Discriminant Analysis
```{r,warning=FALSE,message=FALSE}
library(mda)

#Build the model
model3<-mda(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval)

#Summarize the model
summary(model3)

#Predict using the model
car_eval$pred_mda<-predict(model3,x)

#Accuracy of the model
mtab<-table(car_eval$pred_mda,car_eval$class)
confusionMatrix(mtab)
```

####2)Quadratic Discriminant Analysis

```{r,warning=FALSE,message=FALSE,eval=FALSE}

library(MASS)

#Build the model
model4<-qda(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval)

#Summarize the model
summary(model4)

#Predict using the model
car_eval$pred_qda<-predict(model4,x)

#Accuracy of the model
mtab<-table(car_eval$pred_qda,car_eval$class)
confusionMatrix(mtab)
````

####3)Regularized Discriminant Analysis

````{r,warning=FALSE,message=FALSE}
library(klaR)

#Build the model
model5<-rda(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval,gamma = 0.05,lambda = 0.01)

#Summarize the model
summary(model5)

#Predict using the model
car_eval$pred_rda<-predict(model5,x)$class

#Accuracy of the model
mtab<-table(car_eval$pred_rda,car_eval$class)
confusionMatrix(mtab)
```

####4)Neural Network
```{r,warning=FALSE,message=FALSE}
library(nnet)

#Build the model
model6<-nnet(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval,size = 4,decay = 0.0001,maxit = 500)

#Summarize the model
summary(model6)

#Predict using the model
car_eval$pred_nnet<-predict(model6,x,type="class")

#Accuracy of the model
mtab<-table(car_eval$pred_nnet,car_eval$class)
confusionMatrix(mtab)
`````

###5)Flexible Discriminant Analysis
```{r,message=FALSE,warning=FALSE}
library(mda)

#Build the model
model7<-fda(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval)

#Summarize the model
summary(model7)

#Predict using the model
car_eval$pred_fda<-predict(model7,x,type="class")

#Accuracy of the model
mtab<-table(car_eval$pred_fda,car_eval$class)
confusionMatrix(mtab)
```

####6)Support Vector Machine

```{r,message=FALSE,warning=FALSE}
library(kernlab)

#Build the model
model8<-ksvm(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval)

#Summarize the model
summary(model8)

#Predict using the model
car_eval$pred_svm<-predict(model8,x,type="response")

#Accuracy of the model
mtab<-table(car_eval$pred_svm,car_eval$class)
confusionMatrix(mtab)
```

####7)k-Nearest Neighbors

```{r,message=FALSE,warning=FALSE}
library(caret)

#Build the model
model9<-knn3(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval,k=5)

#Summarize the model
summary(model9)

#Predict using the model
car_eval$pred_knn<-predict(model9,x,type="class")

#Accuracy of the model
mtab<-table(car_eval$pred_knn,car_eval$class)
confusionMatrix(mtab)
```

####8)Naive Bayes
```{r,message=FALSE,warning=FALSE}
library(e1071)

#Build the model
model10<-naiveBayes(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval,k=5)

#Summarize the model
summary(model10)

#Predict using the model
car_eval$pred_naive<-predict(model10,x)

#Accuracy of the model
mtab<-table(car_eval$pred_naive,car_eval$class)
confusionMatrix(mtab)
```

###C)Non-Linear Classification with Decision Trees

####1)Classification and Regression Trees(CART)

```{r,message=FALSE,warning=FALSE}
library(rpart)

#Build the model
model11<-rpart(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval)

#Summarize the model
summary(model11)

#Predict using the model
car_eval$pred_cart<-predict(model11,x,type="class")

#Accuracy of the model
mtab<-table(car_eval$pred_cart,car_eval$class)
confusionMatrix(mtab)
```

####2)C4.5

```{r,message=FALSE,warning=FALSE}
library(RWeka)

#Build the model
model12<-J48(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval)

#Summarize the model
summary(model12)

#Predict using the model
car_eval$pred_c45<-predict(model12,x)

#Accuracy of the model
mtab<-table(car_eval$pred_c45,car_eval$class)
confusionMatrix(mtab)
```

####3)PART

```{r,message=FALSE,warning=FALSE}
library(RWeka)

#Build the model
model13<-PART(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval)

#Summarize the model
summary(model13)

#Predict using the model
car_eval$pred_part<-predict(model13,x)

#Accuracy of the model
mtab<-table(car_eval$pred_part,car_eval$class)
confusionMatrix(mtab)
```

####4)Bagging CART

```{r,message=FALSE,warning=FALSE}
library(ipred)

#Build the model
model14<-bagging(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval)

#Summarize the model
summary(model14)

#Predict using the model
car_eval$pred_bagging<-predict(model14,x)

#Accuracy of the model
mtab<-table(car_eval$pred_bagging,car_eval$class)
confusionMatrix(mtab)
```

####5)Random Forest

```{r,message=FALSE,warning=FALSE}
library(randomForest)

#Build the model
model15<-randomForest(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval)

#Summarize the model
summary(model15)

#Predict using the model
car_eval$pred_randomforest<-predict(model15,x)

#Accuracy of the model
mtab<-table(car_eval$pred_randomforest,car_eval$class)
confusionMatrix(mtab)
```

####6)Gradient Boosted Machine

```{r,message=FALSE,warning=FALSE}
library(gbm)

#Build the model
model16<-gbm(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval,distribution="multinomial")

#Summarize the model
summary(model16)

#Predict using the model
probability<-predict(model16,x,n.trees=1)
car_eval$pred_gbm<-colnames(probability)[apply(probability,1,which.max)]

#Accuracy of the model
mtab<-table(car_eval$pred_gbm,car_eval$class)
mtab
```

####7)Boosted C5.0

```{r,message=FALSE,warning=FALSE}
library(C50)

#Build the model
model17<-C5.0(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval,trials=10)

#Summarize the model
summary(model17)

#Predict using the model
car_eval$pred_c50<-predict(model17,x)

#Accuracy of the model
mtab<-table(car_eval$pred_c50,car_eval$class)
confusionMatrix(mtab)
```