PROBLEM 3: Car Evaluation
PROBLEM 3: Car Evaluation

Yuting Tian

Yu Zhu

Xinyu Wu

Code

# Create the training and testing data samples

df_car <- read.csv("https://raw.githubusercontent.com/jcbonilla/BusinessAnalytics/master/BAData/car-data.csv", stringsAsFactors = T)

head(df_car)

df_car$Boot.Space <- as.factor(df_car$Boot.Space)

df_car$Safety <- as.factor(df_car$Safety)

car_model <- glm(Car.Acceptability ~ Car.Price+Main..Price+Doors+Persons+Boot.Space+Safety, data = df_car, family = "binomial"(link="logit"))

summary(car_model)

set.seed(100)

split <- (0.8)

trainingRowIndex <- sample(1:nrow(df_car),(split)*nrow(df_car))

trainingData <- df_car[trainingRowIndex, ]

testData <- df_car[-trainingRowIndex, ]

# Develop the model on training data and make prediction on testing data

library(rpart)

formula <- Car.Acceptability ~ Car.Price+Main..Price+Doors+Persons+Boot.Space+Safety

car.rpart <- rpart(formula, data = trainingData, control = rpart.control(minsplit = 2))

install.packages("rattle")

library(rattle)

fancyRpartPlot(car.rpart,tweak=1.2)

#Select the tree with the minimum prediction error

opt <- which.min(car.rpart$cptable[, "xerror"])

cp <- car.rpart$cptable[opt, "CP"]

# Prune tree

car.prune <- prune(car.rpart, cp=cp)

fancyRpartPlot(car.prune,tweak=1)

# Make prediction and calculate accuracy

car.predict <- predict(car.prune, testData, type="class")

accuracy <- sum(testData$Car.Acceptability==car.predict)/length(car.predict)

accuracy

Results

Decision tree after the prune

⁠

Conclusion

The classification model has an accuracy of 97.98%.

The bestseller is the car that has 0 or 1 safety, can fit at least 3 people, with low or medium price, 0 or 1 boot space. The car sellers should have more cars like can meet these requirements.

Safety is a very important criterion when people decide whether to buy the car or not, so the car sellers should pay more attention to the safety of cars, make sure that the car’s safety indicator is at least 0.

Customers preferred cars with at least 3 seats, so sedans and SUVs are more acceptable than roadsters.

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PROBLEM 3: Car EvaluationPROBLEM 3: Car Evaluation

Code

Results

Conclusion

PROBLEM 3: Car Evaluation
PROBLEM 3: Car Evaluation