HousePricing-R.R

#import dataset
df<- read.csv("House_Price.csv", header=TRUE)

#view data
head(df)

#get structure of data
str(df)

#univariate analysi
summary(df)

#plot histogram
hist(df$crime_rate) #did not give any major info

pairs(~price + crime_rate + n_hot_rooms + rainfall, data = df) 
#shows n_hot_rooms and rainfal has outliers
#crime rate has a different relationship wit price, hence needs to be modified 
  #to get a linear relationship with price

#get barplot of the 3 categorical variables
barplot(table(df$airport)) #nothing suspicious
barplot(table(df$waterbody)) #nothing suspicious
barplot(table(df$bus_ter)) #only 1 value hence not useful in the data set

#observations
#1: n_hot_rooms has outliers
#2: hos_beds has missing values
#3: bus terminal is useless value
#4: crime rate has some functional relationship with price

#Outlier treatment
quantile(df$n_hot_rooms,0.99)
uv = quantile(df$n_hot_rooms,0.99)
df$n_hot_rooms[df$n_hot_rooms>uv] <- uv 

#checking changes
summary(df$n_hot_rooms)

lv = 0.3 * quantile(df$rainfall, 0.01)
df$rainfall[df$rainfall < lv] <- lv

#checking changes
summary(df$rainfall)

#handling missing values with mean
mean(df$n_hos_beds,na.rm = TRUE)

#positions with na values
which(is.na(df$n_hos_beds))

df$n_hos_beds[is.na(df$n_hos_beds)] <- mean(df$n_hos_beds,na.rm = TRUE)

#checking changes
summary(df$n_hos_beds)

#plot pair plot of price and crime data
pairs(~price+crime_rate, data=df)
plot(df$price, df$crime_rate)

#transform to logrithmic format
df$crime_rate = log(1+df$crime_rate)

#get new variable to represent dist 1,2,3,4
df$avg_dist = (df$dist1 + df$dist2 + df$dist3 + df$dist4)/4

#view the new data
View(df)

#delete dist1,2,3,4
df2 <- df[ ,-7:-10] 
df <- df2   #reassign back to df
rm(df2)     #delete df2

#remove unnecessary column bus_terminal
df <- df[ , -14]

#create dummy variables for all the categorical variable
df$airport <- ifelse(df$airport == "YES", 1,0)
df$river <- ifelse(df$waterbody == "River" | df$waterbody == "Lake and River", 1,0)
df$lake <- ifelse(df$waterbody == "Lake"  | df$waterbody == "Lake and River", 1,0)

#remove water body column
df <- df[ , -12]

#get correlation matrix rounded to 2 dp
round(cor(df),2)

#park and air quality will lead to multi-collinearity
#we will remove park as it has higher correlation
df <- df[ , -13]

#get linear regression model
simple_model <- lm(price~room_num, data = df)
summary(simple_model)

#plot rrom number nd price
plot(df$room_num, df$price)
abline (simple_model)

multiple_model <- lm(price~.,data = df)
summary(multiple_model)

#split data into test and train
#install required package
install.packages("caTools")
library("caTools")

#set seed
set.seed(0)

#split data
split = sample.split(df,SplitRatio = 0.8)
training_set = subset(df,split==TRUE)
test_set = subset(df, split==FALSE)

#run linear model on training dataset
lm_a = lm(price~., data = training_set)
summary(lm_a)

#predict value of price
train_a = predict(lm_a, training_set)
test_a = predict(lm_a, test_set)

mean((training_set$price - train_a)^2)
mean((test_set$price - test_a)^2)

#subset selectin
#install requred library
install.packages("leaps")
library("leaps")

lm_best = regsubsets(price~., data = df, nvmax = 15)
summary(lm_best)

#get adjusted r2 value
summary(lm_best)$adjr2

#get which dot max value\
which.max(summary(lm_best)$adjr2)

coef(lm_best, 8)

#run forward and backward selection
lm_forward = regsubsets(price~., data = df, nvmax = 15, method = "forward")
summary(lm_forward)
lm_backward = regsubsets(price~., data = df, nvmax = 15, method = "backward")
summary(lm_backward)