README.md

Analysis-on-Airline-Ticket-Prices

This project was done to study the factors affecting the difference of price in Premium Economy and Economy seats in Airline.

Read data into R

SixAirline <- read.csv(paste("SixAirlinesDataV2.csv", sep="")) View(SixAirline)

Summarize the data to understand the mean, median, standard deviation of each variable

summary(SixAirline)

Draw Box Plots to visualize the distribution of Price of Economy tickets in different Airlines

boxplot(SixAirline$PriceEconomy ~ SixAirline$Airline, data=SixAirline, horizontal=TRUE, yaxt="n", ylab="Airline", xlab="PriceEconomy", main="Comparison of Price in different Airlines") axis(side=2, at=c(1,2,3,4,5,6), labels=c("AirFrance", "British "," Delta","Jet","Singapore","Virgin"))

Draw Box Plots to visualize the distribution of Price of Premium tickets in different Airlines

boxplot(SixAirline$PricePremium ~ SixAirline$Airline, data=SixAirline, horizontal=TRUE, yaxt="n", ylab="Airline", xlab="PricePremium", main="Comparison of Price in different Airlines") axis(side=2, at=c(1,2,3,4,5,6), labels=c("AirFrance", "British "," Delta","Jet","Singapore","Virgin"))

Draw Bar Plots to visualize the distribution of Pitch size in different Airlines

Pitch size in Economy class

seate.mean <- aggregate(PitchEconomy ~ Airline, data=SixAirline, mean)

Pitch size in Premium class

seatp.mean <- aggregate(PitchPremium ~ Airline, data=SixAirline, mean)

Pitch size in Economy class

seate.mean <- aggregate(PitchEconomy ~ Airline, data=SixAirline, mean)

Pitch size in Premium class

seatp.mean <- aggregate(PitchPremium ~ Airline, data=SixAirline, mean) Hide

Visualizing them

library(lattice) barchart(PitchEconomy ~ Airline, data=seate.mean, col="grey") barchart(PitchPremium ~ Airline, data=seatp.mean, col="grey")

To visualize the distribution of Width size in different Airlines

Width size in Economy class

widthe.mean <- aggregate(WidthEconomy ~ Airline, data=SixAirline, mean)

Width size in Premium class

widthp.mean <- aggregate(WidthPremium ~ Airline, data=SixAirline, mean) Hide

visualizing them

barchart(WidthEconomy ~ Airline, data=widthe.mean, col="grey") barchart(WidthPremium ~ Airline, data=widthp.mean, col="grey")

Making scatter plot matrix to see the coorelation between different variables

scatterplotMatrix( SixAirline[ ,c("PitchPremium","PitchEconomy")], spread=FALSE, smoother.args=list(lty=2), main="Scatter Plot Matrix") scatterplotMatrix( SixAirline[ ,c("WidthPremium","WidthEconomy")], spread=FALSE, smoother.args=list(lty=2), main="Scatter Plot Matrix")
scatterplotMatrix( SixAirline[ ,c("PriceEconomy","PricePremium")], spread=FALSE, smoother.args=list(lty=2), main="Scatter Plot Matrix") scatterplotMatrix( SixAirline[ ,c("SeatsPremium","SeatsEconomy")], spread=FALSE, smoother.args=list(lty=2), main="Scatter Plot Matrix") scatterplotMatrix( SixAirline[ ,c("PitchDifference","WidthDifference")], spread=FALSE, smoother.args=list(lty=2), main="Scatter Plot Matrix")

To make Scatter Plots to understand how are the variables correlated pair-wise

Correlation between Price of Premium Ticket and Pitch Difference

library(car) scatterplot(PricePremium ~ PitchDifference, data=Airline, spread=FALSE, smoother.args=list(lty=2), pch=19, main="Scatterplot of Pitch Diff", xlab="PitchDifference", ylab="PricePremium")

Correlation between Price of Economy Ticket and Pitch Difference

scatterplot(PriceEconomy ~ PitchDifference, data=Airline, spread=FALSE, smoother.args=list(lty=2), pch=19, main="Scatterplot of Pitch Diff", xlab="PitchDifference", ylab="PriceEconomy")

Correlation between Price of Economy Ticket and Width Difference

scatterplot(PriceEconomy ~ WidthDifference, data=Airline, spread=FALSE, smoother.args=list(lty=2), pch=19, main="Scatterplot of Width Diff", xlab="WidthDifference", ylab="PriceEconomy")

Correlation between Price of Premium Ticket and Width Difference

scatterplot(PricePremium ~ WidthDifference, data=Airline, spread=FALSE, smoother.args=list(lty=2), pch=19, main="Scatterplot of Width Diff", xlab="WidthDifference", ylab="PricePremium")

Draw a Corrgram

library(corrgram) corrgram(SixAirline, order=FALSE, lower.panel=panel.shade, upper.panel=panel.pie, diag.panel=panel.minmax, text.panel=panel.txt, main="Corrgram of Six Airline intercorrelations")

Create a Variance-Covariance Matrix

options(digits=2) cor(SixAirline$PitchPremium, SixAirline$PricePremium) cor(SixAirline$WidthPremium, SixAirline$PricePremium) cor(SixAirline$PitchEconomy, SixAirline$PriceEconomy) cor(SixAirline$WidthEconomy, SixAirline$PriceEconomy)

Perform a Pearson Test for coorelation

resP <- cor.test(SixAirline$WidthPremium, SixAirline$PricePremium, method = "pearson") resP

Perform a Pearson Test for coorelation

resE <- cor.test(SixAirline$WidthEconomy, SixAirline$PriceEconomy, method = "pearson") resE

Articulate a Hypothesis (or two) that you could test using a Regression Model

1.NULL HYPOTHESIS: Increase in PitchPremium does not contribute in increase in PricePremium

2.NULL HYPOTHESIS: Increase in WidthPremium does not contribute in increase in Price Premium

Run T-Tests to test the first hypothesis

t.test(SixAirline$PitchPremium, SixAirline$PricePremium)

Run T-Tests to test the second hypothesis

t.test(SixAirline$WidthPremium, SixAirline$PricePremium)

Formulate a Regression Model

m2 <- lm(PricePremium ~ PitchPremium + WidthPremium, data=SixAirline) summary(m2)

According to the result, this is made clear that increase in Pitch size and Width size are the major factors contributing to increase in price of Premium ticket.