Airline Pricing Analysis

This is a mini project on the analysis of the difference between the pricing of premium economy and economy class airline tickets.

1. Set the working directory.

setwd("~/Desktop/Data Analytics Internship/Airline")

2. Read the dataset using the read.csv() function.

air<-read.csv(file="SixAirlinesDataV2.csv")

3. Use the View() function to view the dataframe created in the previous step.

View(air)

4. Describing the Dataset

library(psych)
describe(air)
##                     vars   n    mean      sd  median trimmed     mad   min
## Airline*               1 458    3.01    1.65    2.00    2.89    1.48  1.00
## Aircraft*              2 458    1.67    0.47    2.00    1.71    0.00  1.00
## FlightDuration         3 458    7.58    3.54    7.79    7.57    4.81  1.25
## TravelMonth*           4 458    2.56    1.17    3.00    2.58    1.48  1.00
## IsInternational*       5 458    1.91    0.28    2.00    2.00    0.00  1.00
## SeatsEconomy           6 458  202.31   76.37  185.00  194.64   85.99 78.00
## SeatsPremium           7 458   33.65   13.26   36.00   33.35   11.86  8.00
## PitchEconomy           8 458   31.22    0.66   31.00   31.26    0.00 30.00
## PitchPremium           9 458   37.91    1.31   38.00   38.05    0.00 34.00
## WidthEconomy          10 458   17.84    0.56   18.00   17.81    0.00 17.00
## WidthPremium          11 458   19.47    1.10   19.00   19.53    0.00 17.00
## PriceEconomy          12 458 1327.08  988.27 1242.00 1244.40 1159.39 65.00
## PricePremium          13 458 1845.26 1288.14 1737.00 1799.05 1845.84 86.00
## PriceRelative         14 458    0.49    0.45    0.36    0.42    0.41  0.02
## SeatsTotal            15 458  235.96   85.29  227.00  228.73   90.44 98.00
## PitchDifference       16 458    6.69    1.76    7.00    6.76    0.00  2.00
## WidthDifference       17 458    1.63    1.19    1.00    1.53    0.00  0.00
## PercentPremiumSeats   18 458   14.65    4.84   13.21   14.31    2.68  4.71
##                         max   range  skew kurtosis    se
## Airline*               6.00    5.00  0.61    -0.95  0.08
## Aircraft*              2.00    1.00 -0.72    -1.48  0.02
## FlightDuration        14.66   13.41 -0.07    -1.12  0.17
## TravelMonth*           4.00    3.00 -0.14    -1.46  0.05
## IsInternational*       2.00    1.00 -2.91     6.50  0.01
## SeatsEconomy         389.00  311.00  0.72    -0.36  3.57
## SeatsPremium          66.00   58.00  0.23    -0.46  0.62
## PitchEconomy          33.00    3.00 -0.03    -0.35  0.03
## PitchPremium          40.00    6.00 -1.51     3.52  0.06
## WidthEconomy          19.00    2.00 -0.04    -0.08  0.03
## WidthPremium          21.00    4.00 -0.08    -0.31  0.05
## PriceEconomy        3593.00 3528.00  0.51    -0.88 46.18
## PricePremium        7414.00 7328.00  0.50     0.43 60.19
## PriceRelative          1.89    1.87  1.17     0.72  0.02
## SeatsTotal           441.00  343.00  0.70    -0.53  3.99
## PitchDifference       10.00    8.00 -0.54     1.78  0.08
## WidthDifference        4.00    4.00  0.84    -0.53  0.06
## PercentPremiumSeats   24.69   19.98  0.71     0.28  0.23

Summary

summary(air)
##       Airline      Aircraft   FlightDuration   TravelMonth
##  AirFrance: 74   AirBus:151   Min.   : 1.250   Aug:127    
##  British  :175   Boeing:307   1st Qu.: 4.260   Jul: 75    
##  Delta    : 46                Median : 7.790   Oct:127    
##  Jet      : 61                Mean   : 7.578   Sep:129    
##  Singapore: 40                3rd Qu.:10.620              
##  Virgin   : 62                Max.   :14.660              
##       IsInternational  SeatsEconomy    SeatsPremium    PitchEconomy  
##  Domestic     : 40    Min.   : 78.0   Min.   : 8.00   Min.   :30.00  
##  International:418    1st Qu.:133.0   1st Qu.:21.00   1st Qu.:31.00  
##                       Median :185.0   Median :36.00   Median :31.00  
##                       Mean   :202.3   Mean   :33.65   Mean   :31.22  
##                       3rd Qu.:243.0   3rd Qu.:40.00   3rd Qu.:32.00  
##                       Max.   :389.0   Max.   :66.00   Max.   :33.00  
##   PitchPremium    WidthEconomy    WidthPremium    PriceEconomy 
##  Min.   :34.00   Min.   :17.00   Min.   :17.00   Min.   :  65  
##  1st Qu.:38.00   1st Qu.:18.00   1st Qu.:19.00   1st Qu.: 413  
##  Median :38.00   Median :18.00   Median :19.00   Median :1242  
##  Mean   :37.91   Mean   :17.84   Mean   :19.47   Mean   :1327  
##  3rd Qu.:38.00   3rd Qu.:18.00   3rd Qu.:21.00   3rd Qu.:1909  
##  Max.   :40.00   Max.   :19.00   Max.   :21.00   Max.   :3593  
##   PricePremium    PriceRelative      SeatsTotal  PitchDifference 
##  Min.   :  86.0   Min.   :0.0200   Min.   : 98   Min.   : 2.000  
##  1st Qu.: 528.8   1st Qu.:0.1000   1st Qu.:166   1st Qu.: 6.000  
##  Median :1737.0   Median :0.3650   Median :227   Median : 7.000  
##  Mean   :1845.3   Mean   :0.4872   Mean   :236   Mean   : 6.688  
##  3rd Qu.:2989.0   3rd Qu.:0.7400   3rd Qu.:279   3rd Qu.: 7.000  
##  Max.   :7414.0   Max.   :1.8900   Max.   :441   Max.   :10.000  
##  WidthDifference PercentPremiumSeats
##  Min.   :0.000   Min.   : 4.71      
##  1st Qu.:1.000   1st Qu.:12.28      
##  Median :1.000   Median :13.21      
##  Mean   :1.633   Mean   :14.65      
##  3rd Qu.:3.000   3rd Qu.:15.36      
##  Max.   :4.000   Max.   :24.69

Structure of Dataset

str(air)  
## 'data.frame':    458 obs. of  18 variables:
##  $ Airline            : Factor w/ 6 levels "AirFrance","British",..: 2 2 2 2 2 2 2 2 2 2 ...
##  $ Aircraft           : Factor w/ 2 levels "AirBus","Boeing": 2 2 2 2 2 2 2 2 2 2 ...
##  $ FlightDuration     : num  12.25 12.25 12.25 12.25 8.16 ...
##  $ TravelMonth        : Factor w/ 4 levels "Aug","Jul","Oct",..: 2 1 4 3 1 4 3 1 4 4 ...
##  $ IsInternational    : Factor w/ 2 levels "Domestic","International": 2 2 2 2 2 2 2 2 2 2 ...
##  $ SeatsEconomy       : int  122 122 122 122 122 122 122 122 122 122 ...
##  $ SeatsPremium       : int  40 40 40 40 40 40 40 40 40 40 ...
##  $ PitchEconomy       : int  31 31 31 31 31 31 31 31 31 31 ...
##  $ PitchPremium       : int  38 38 38 38 38 38 38 38 38 38 ...
##  $ WidthEconomy       : int  18 18 18 18 18 18 18 18 18 18 ...
##  $ WidthPremium       : int  19 19 19 19 19 19 19 19 19 19 ...
##  $ PriceEconomy       : int  2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 ...
##  $ PricePremium       : int  3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 ...
##  $ PriceRelative      : num  0.38 0.38 0.38 0.38 0.67 0.67 0.67 1.03 1.03 0.75 ...
##  $ SeatsTotal         : int  162 162 162 162 162 162 162 162 162 162 ...
##  $ PitchDifference    : int  7 7 7 7 7 7 7 7 7 7 ...
##  $ WidthDifference    : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ PercentPremiumSeats: num  24.7 24.7 24.7 24.7 24.7 ...

Plots to visualize the distribution of each variable independently

Flight Duration

boxplot(air$FlightDuration, horizontal=TRUE,
        main="Flight duration of all the airlines")

Economic Seats

boxplot(air$SeatsEconomy, horizontal=TRUE,
        main="Economic seats of all airlines")

Premium seats

boxplot(air$SeatsPremium, horizontal=TRUE,
        main="Flight durations of all the airlines")

Economy Price

boxplot(air$PriceEconomy, horizontal=TRUE,
        main="Economic prices of all airlines")

Premium Price

boxplot(air$PricePremium, horizontal=TRUE,
        main="Premium prices of all airlines")

Boxplot for relative price for each pitch difference

boxplot(air$PriceRelative ~ air$PitchDifference, horizontal=TRUE,
        xlab = "Price Relative", ylab = "Pitch Difference",
        main = "Relative price changes according to pitch difference",
        col=c("red","blue","green","yellow","brown")
          )

boxplot(air$PriceRelative ~ air$WidthDifference, horizontal=TRUE,
        xlab = "Price Relative", ylab = "Width Difference",
        main = "Relative price changes according to width difference",
        col=c("red","blue","green","yellow","brown")
          )

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

Economic pricing variables

library(car)
## 
## Attaching package: 'car'
## The following object is masked from 'package:psych':
## 
##     logit
 scatterplotMatrix(formula = ~ FlightDuration + SeatsEconomy + PitchEconomy + WidthEconomy + PriceEconomy + PriceRelative, data = air, diagonal="histogram")
## Warning in smoother(x, y, col = col[2], log.x = FALSE, log.y = FALSE,
## spread = spread, : could not fit negative part of the spread
## Warning in smoother(x, y, col = col[2], log.x = FALSE, log.y = FALSE,
## spread = spread, : could not fit smooth

Premium pricing dependent variables

scatterplotMatrix(formula = ~ FlightDuration + SeatsPremium + PitchPremium + WidthPremium + PricePremium + PriceRelative, data = air, diagonal="histogram")
## Warning in smoother(x, y, col = col[2], log.x = FALSE, log.y = FALSE,
## spread = spread, : could not fit smooth

Corrgram

Create a Variance-Covariance Matrix

library(corrgram)
## Warning: replacing previous import by 'magrittr::%>%' when loading
## 'dendextend'
corrgram(air, order=FALSE, 
         lower.panel=panel.shade,
         upper.panel=panel.pie, 
         diag.panel=panel.minmax,
         text.panel=panel.txt,
         main="Corrgram of airlines intercorrelations")

Correlation

cor.test(air$PriceRelative, air$PitchDifference)
## 
##  Pearson's product-moment correlation
## 
## data:  air$PriceRelative and air$PitchDifference
## t = 11.331, df = 456, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3940262 0.5372817
## sample estimates:
##       cor 
## 0.4687302

As p<0.05, these 2 variables are strongly corelated.

cor.test(air$PriceRelative, air$WidthDifference)
## 
##  Pearson's product-moment correlation
## 
## data:  air$PriceRelative and air$WidthDifference
## t = 11.869, df = 456, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4125388 0.5528218
## sample estimates:
##       cor 
## 0.4858024

As p<0.05, these 2 variables are strongly corelated.

Regression Model

Pitchpremium,widthpremium,pitchdifference,pricerelative,widtheconomy,flightduration,seatseconomy has a strong positive correlation with one another. Also, There is a strong positive correlation between pitchpremium and widthpremium , widthpremium and width difference, etc.

Premium Pricing

Dependent variable: PricePremium Independent variable: Flightduration,PitchPremium and WidthPremium Independent variables are affecting/influencing the Pricing of Premium class tickets.

Following linear regression model considered, PricePremium = x0 + x1FlightDuration + x2WidthPremium + x3PitchPremium

prem <- lm(air$PricePremium~air$FlightDuration+air$WidthPremium+air$PitchPremium)
summary(prem)
## 
## Call:
## lm(formula = air$PricePremium ~ air$FlightDuration + air$WidthPremium + 
##     air$PitchPremium)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2325.5  -609.2   -75.1   780.3  4061.1 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        -1174.19    1353.41  -0.868    0.386    
## air$FlightDuration   235.60      13.04  18.063   <2e-16 ***
## air$WidthPremium     -60.84      63.39  -0.960    0.338    
## air$PitchPremium      63.81      52.90   1.206    0.228    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 981.9 on 454 degrees of freedom
## Multiple R-squared:  0.4227, Adjusted R-squared:  0.4189 
## F-statistic: 110.8 on 3 and 454 DF,  p-value: < 2.2e-16

There is a significant effect of width and pitch on the pricing of the premium class tickets. Now, assuming the same for economic class.

Economic Pricing

Dependent variable: PriceEconomy Independent variable: Flightduration,PitchEconomy and WidthEconomy Independent variables are affecting/influencing the Pricing of Economy class tickets.

eco <- lm(air$PriceEconomy~air$FlightDuration+air$WidthEconomy+air$PitchEconomy)
summary(eco)
## 
## Call:
## lm(formula = air$PriceEconomy ~ air$FlightDuration + air$WidthEconomy + 
##     air$PitchEconomy)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1561.82  -547.83    15.19   643.88  1425.23 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        -3489.63    1946.19  -1.793   0.0736 .  
## air$FlightDuration   173.43      11.28  15.369  < 2e-16 ***
## air$WidthEconomy    -525.11      71.71  -7.323 1.11e-12 ***
## air$PitchEconomy     412.24      56.80   7.258 1.71e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 746.7 on 454 degrees of freedom
## Multiple R-squared:  0.4328, Adjusted R-squared:  0.4291 
## F-statistic: 115.5 on 3 and 454 DF,  p-value: < 2.2e-16

Hence, Flight duration, width of the seat and the pitch of the seat are the factors that influence the pricing of airline tickets.