Airtrip Data Business Intelligence Data Mining SPSS

1. BIDM Assignment – AIRLINE DATA
Assignment by Group A (PGPMX 2015-17 Batch)
 Jayant Shenoy(2015PGPMX007)
 Lata Srijit(2015PGPMX013)
 Mandar Risbud(2015PGPMX014)
 Parineeta Katgaonkar(2015PGPMX018)
 Samir Shah(2015PGPMX023)
 Sanmeet Dhokay(2015PGPMX 025)

2. Contents
1. Problem Statement.....................................................................................................................3
2. Preparation of Data.....................................................................................................................3
3. Identify Top 4 Airports by TrafficVolume......................................................................................3
4. Identify Top 5 Airlines................................................................................................................19
5. Seasonalityin terms of Delay and Operating Performance...........................................................21
6. Conclusion................................................................................................................................24

3. 1.Problem Statement
Datasetfor this problemconsistsof arrival anddeparture dataof flightsacrossall the airports withinUS
for 25 years.Your taskis to identifytop4airportsinterms of trafficvolume.Alsoidentifytop5airline
carriersthat operate onthose routes(intermsof numberof flights).Selectonlythose flightsof the
carriersthat flewinor outor betweenanyof these top4 airports.Please trytoanswerfollowing
questionsusingthisdataset.
 Is there anyseasonalityintermsof flightdelay? (Hint:Use atleast10 yearsof data to findany
seasonal effect)
 Excludingsystemicdelays(i.e.whenall flightsin/outof an airportare delayeddue tosome
commonproblemlike weatherorcomputerglitches),compare the operatingperformanceof
the top 5 airline players
We have identifiedthe belowmentionedobjectivesforthe problem.
Objectivesof the problem:
 Top 4 Airportsintermsof trafficvolume
 Top 5 Airline Carriersonthose airportsintermsof numberof flights
 Seasonalityintermsof flightdelay?
 Compare the operatingperformance of the top5 airline players
2.Preparation of Data
Overall datasize was31.7 GB. We usedHypothesisTestingonthe 303 Individual Datasets(.csvfiles) i.e.
Taking1% sample fromeach of the filesandthenmergingall the datainto one file.
We usedLinux batchcommandsto take 1% sample andmerge all the recordsinto 1 file.
- In all, there were 303 files (1 file per month, data for around 25 years)
- Each file had around 80+ MB of data, having around 500,000 data records
- The approach was to take 1% of data records from each file and at the end, merge all the
records from 303 files into a single data file
- For extracting 1% data from each individual file and then merging all extracted data, we used
Linux commands, as they come as handy utilities for data movement and merging
- Since the overall datavolume wasverylarge,we usedMSAzure Cloudenvironmentfor moving,
storing and handling data
3.Identify Top 4 Airports by Traffic Volume
StepsFollowed :-
1) Findthe frequency (count) of each of the AirportCodesinthe OriginColumn

4. 2) Findthe frequency (count) of eachof the AirportCodesinthe DestinationColumn
3) Addthe countsof bothOriginandDestination
4) Sort the data from highestcounttothe lowestcount
5) Top 4 rowsgive the top 4 busiestairports
Findthe Frequency(Count)
Selectthe ORIGIN/DESTColumn

5. OutputdisplayedbyIBMSPSSStatistics
OriginCounts
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid
ABE 1600 .1 .1 .1
ABQ 10439 .7 .7 .8
ABR 5 .0 .0 .8
ADQ 312 .0 .0 .8
AEX 11 .0 .0 .8
ALB 736 .0 .0 .9
ALO 295 .0 .0 .9
AMA 4900 .3 .3 1.2
ANC 7043 .5 .5 1.7
ATL 47556 3.1 3.1 4.8
ATW 1523 .1 .1 4.9
AUS 11657 .8 .8 5.7
AVL 1802 .1 .1 5.8
AVP 1575 .1 .1 5.9
AZO 841 .1 .1 5.9
BDL 2464 .2 .2 6.1
BET 372 .0 .0 6.1

6. BFL 235 .0 .0 6.1
BGM 939 .1 .1 6.2
BGR 1296 .1 .1 6.3
BHM 2396 .2 .2 6.4
BIL 17 .0 .0 6.4
BIS 681 .0 .0 6.5
BJI 79 .0 .0 6.5
BLI 55 .0 .0 6.5
BMI 474 .0 .0 6.5
BNA 9026 .6 .6 7.1
BOI 4 .0 .0 7.1
BOS 32053 2.1 2.1 9.2
BRW 40 .0 .0 9.2
BTR 1016 .1 .1 9.3
BTV 246 .0 .0 9.3
BUF 1413 .1 .1 9.4
BUR 850 .1 .1 9.5
BWI 3634 .2 .2 9.7
BZN 21 .0 .0 9.7
CAE 370 .0 .0 9.7
CAK 558 .0 .0 9.8
CCR 88 .0 .0 9.8
CDV 85 .0 .0 9.8
CHA 283 .0 .0 9.8
CHO 162 .0 .0 9.8
CHS 1150 .1 .1 9.9
CID 2886 .2 .2 10.1
CLE 2699 .2 .2 10.3
CLT 7412 .5 .5 10.7
CMH 2294 .2 .2 10.9
CMI 122 .0 .0 10.9
CMX 298 .0 .0 10.9
COS 1341 .1 .1 11.0
COU 531 .0 .0 11.0
CPR 65 .0 .0 11.1
CRP 272 .0 .0 11.1
CRW 208 .0 .0 11.1

7. CVG 13714 .9 .9 12.0
CWA 280 .0 .0 12.0
DAB 29 .0 .0 12.0
DAL 22189 1.5 1.5 13.5
DAY 1981 .1 .1 13.6
DCA 11647 .8 .8 14.4
DEN 22343 1.5 1.5 15.8
DET 1238 .1 .1 15.9
DFW 131597 8.7 8.7 24.6
DLG 117 .0 .0 24.6
DLH 1205 .1 .1 24.7
DSM 3180 .2 .2 24.9
DTW 72738 4.8 4.8 29.7
DUT 87 .0 .0 29.7
EAU 9 .0 .0 29.7
ECP 55 .0 .0 29.7
ELM 495 .0 .0 29.7
ELP 21799 1.4 1.4 31.1
ERI 787 .1 .1 31.2
EUG 121 .0 .0 31.2
EVV 701 .0 .0 31.2
EWR 13293 .9 .9 32.1
FAI 20 .0 .0 32.1
FAR 945 .1 .1 32.2
FAT 165 .0 .0 32.2
FAY 287 .0 .0 32.2
FCA 27 .0 .0 32.2
FLL 928 .1 .1 32.3
FNT 1083 .1 .1 32.3
FSD 1202 .1 .1 32.4
FSM 652 .0 .0 32.5
FWA 1909 .1 .1 32.6
GEG 114 .0 .0 32.6
GFK 926 .1 .1 32.7
GPT 366 .0 .0 32.7
GRB 1278 .1 .1 32.8
GRR 1474 .1 .1 32.9

8. GSO 1862 .1 .1 33.0
GSP 1577 .1 .1 33.1
GTF 95 .0 .0 33.1
HDN 7 .0 .0 33.1
HLN 235 .0 .0 33.1
HNL 36487 2.4 2.4 35.5
HOU 13360 .9 .9 36.4
HPN 1760 .1 .1 36.5
HRL 685 .0 .0 36.6
HSV 2168 .1 .1 36.7
HTS 1 .0 .0 36.7
IAD 16247 1.1 1.1 37.8
IAH 16152 1.1 1.1 38.8
ICT 1562 .1 .1 38.9
IDA 369 .0 .0 39.0
IND 7401 .5 .5 39.4
INL 149 .0 .0 39.4
ISP 576 .0 .0 39.5
ITH 606 .0 .0 39.5
JAC 72 .0 .0 39.5
JAN 2037 .1 .1 39.7
JAX 1642 .1 .1 39.8
JFK 83291 5.5 5.5 45.3
KOA 3137 .2 .2 45.5
LAN 1406 .1 .1 45.6
LAS 24665 1.6 1.6 47.2
LAX 155745 10.2 10.2 57.4
LBB 8822 .6 .6 58.0
LEX 708 .0 .0 58.1
LFT 212 .0 .0 58.1
LGA 39707 2.6 2.6 60.7
LGB 2769 .2 .2 60.9
LIH 3240 .2 .2 61.1
LIT 696 .0 .0 61.1
LNK 751 .0 .0 61.2
LSE 557 .0 .0 61.2
MAF 6695 .4 .4 61.6

9. MBS 1590 .1 .1 61.8
MCI 10222 .7 .7 62.4
MCO 15518 1.0 1.0 63.4
MDT 7845 .5 .5 64.0
MDW 5090 .3 .3 64.3
MEM 24423 1.6 1.6 65.9
MFE 750 .0 .0 66.0
MFR 25 .0 .0 66.0
MGM 142 .0 .0 66.0
MHT 93 .0 .0 66.0
MIA 27435 1.8 1.8 67.8
MKE 1216 .1 .1 67.9
MLB 59 .0 .0 67.9
MLI 1297 .1 .1 67.9
MLU 1 .0 .0 67.9
MOB 880 .1 .1 68.0
MOT 54 .0 .0 68.0
MQT 71 .0 .0 68.0
MRY 87 .0 .0 68.0
MSN 881 .1 .1 68.1
MSO 2 .0 .0 68.1
MSP 32077 2.1 2.1 70.2
MSY 5198 .3 .3 70.5
MYR 138 .0 .0 70.5
OAK 4182 .3 .3 70.8
OGG 14899 1.0 1.0 71.8
OKC 6577 .4 .4 72.2
OMA 1709 .1 .1 72.3
ONT 2027 .1 .1 72.5
ORD 105448 6.9 6.9 79.4
ORF 1082 .1 .1 79.5
PBI 565 .0 .0 79.5
PDX 2748 .2 .2 79.7
PFN 427 .0 .0 79.7
PHF 107 .0 .0 79.7
PHL 12752 .8 .8 80.6
PHX 63416 4.2 4.2 84.7

10. PIA 528 .0 .0 84.8
PIR 3 .0 .0 84.8
PIT 21008 1.4 1.4 86.2
PLN 382 .0 .0 86.2
PNS 1193 .1 .1 86.3
PSP 393 .0 .0 86.3
PVD 1192 .1 .1 86.4
PWM 2945 .2 .2 86.6
RAP 35 .0 .0 86.6
RDU 6895 .5 .5 87.0
RHI 131 .0 .0 87.0
RIC 1216 .1 .1 87.1
RNO 1072 .1 .1 87.2
ROA 343 .0 .0 87.2
ROC 1489 .1 .1 87.3
RST 1306 .1 .1 87.4
RSW 928 .1 .1 87.4
SAN 33840 2.2 2.2 89.7
SAT 6509 .4 .4 90.1
SAV 1611 .1 .1 90.2
SBA 146 .0 .0 90.2
SBN 2637 .2 .2 90.4
SCE 791 .1 .1 90.4
SCK 44 .0 .0 90.4
SDF 2137 .1 .1 90.6
SEA 8783 .6 .6 91.2
SFO 49979 3.3 3.3 94.5
SGF 1065 .1 .1 94.5
SHV 1396 .1 .1 94.6
SJC 17829 1.2 1.2 95.8
SJU 5332 .4 .4 96.1
SLC 8138 .5 .5 96.7
SMF 1130 .1 .1 96.7
SNA 4008 .3 .3 97.0
SRQ 325 .0 .0 97.0
STL 22758 1.5 1.5 98.5
STT 138 .0 .0 98.5

11. STX 50 .0 .0 98.5
SUX 234 .0 .0 98.6
SWF 2483 .2 .2 98.7
SYR 838 .1 .1 98.8
TLH 1058 .1 .1 98.8
TOL 29 .0 .0 98.8
TPA 2710 .2 .2 99.0
TRI 64 .0 .0 99.0
TUL 5342 .4 .4 99.4
TUS 3775 .2 .2 99.6
TVC 1146 .1 .1 99.7
TYS 2812 .2 .2 99.9
VPS 164 .0 .0 99.9
XNA 1503 .1 .1 100.0
Total 1519758 100.0 100.0
DestinationCounts
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid ABE 40964 2.7 2.7 2.7

12. ABI 1 .0 .0 2.7
ABQ 229381 15.1 15.1 17.8
ABR 5 .0 .0 17.8
ACK 104 .0 .0 17.8
ADQ 3903 .3 .3 18.1
AEX 1 .0 .0 18.1
AGS 10903 .7 .7 18.8
AKN 2470 .2 .2 18.9
ALB 69076 4.5 4.5 23.5
ALO 509 .0 .0 23.5
AMA 10533 .7 .7 24.2
ANC 1703 .1 .1 24.3
ATL 21282 1.4 1.4 25.7
ATW 920 .1 .1 25.8
AUS 12041 .8 .8 26.6
AVL 1428 .1 .1 26.7
AVP 1488 .1 .1 26.8
AZO 1286 .1 .1 26.8
BDL 3379 .2 .2 27.1
BFL 119 .0 .0 27.1
BGM 549 .0 .0 27.1
BGR 1172 .1 .1 27.2
BHM 3410 .2 .2 27.4
BIL 17 .0 .0 27.4
BIS 359 .0 .0 27.4
BJI 3 .0 .0 27.4
BLI 4 .0 .0 27.4
BMI 364 .0 .0 27.5
BNA 11772 .8 .8 28.2
BOI 31 .0 .0 28.2
BOS 37544 2.5 2.5 30.7
BTR 745 .0 .0 30.8
BTV 590 .0 .0 30.8
BUF 1112 .1 .1 30.9
BUR 568 .0 .0 30.9
BWI 1684 .1 .1 31.0
BZN 2 .0 .0 31.0

13. CAE 602 .0 .0 31.1
CAK 504 .0 .0 31.1
CCR 79 .0 .0 31.1
CHA 217 .0 .0 31.1
CHO 248 .0 .0 31.1
CHS 1068 .1 .1 31.2
CID 2350 .2 .2 31.4
CLE 2496 .2 .2 31.5
CLT 1317 .1 .1 31.6
CMH 1676 .1 .1 31.7
CMX 131 .0 .0 31.7
COS 55 .0 .0 31.7
COU 746 .0 .0 31.8
CPR 95 .0 .0 31.8
CRP 141 .0 .0 31.8
CRW 64 .0 .0 31.8
CVG 2960 .2 .2 32.0
CWA 497 .0 .0 32.0
DAB 31 .0 .0 32.0
DAL 418 .0 .0 32.1
DAY 1400 .1 .1 32.1
DCA 4452 .3 .3 32.4
DEN 4052 .3 .3 32.7
DET 1893 .1 .1 32.8
DFW 99047 6.5 6.5 39.3
DLH 1732 .1 .1 39.5
DSM 3180 .2 .2 39.7
DTW 56742 3.7 3.7 43.4
EAU 9 .0 .0 43.4
ECP 73 .0 .0 43.4
ELM 397 .0 .0 43.4
ELP 10928 .7 .7 44.2
ERI 463 .0 .0 44.2
EVV 990 .1 .1 44.2
EWR 17748 1.2 1.2 45.4
FAR 886 .1 .1 45.5
FAT 118 .0 .0 45.5

14. FCA 27 .0 .0 45.5
FLL 3022 .2 .2 45.7
FNT 1065 .1 .1 45.8
FSD 1379 .1 .1 45.8
FSM 671 .0 .0 45.9
FWA 1920 .1 .1 46.0
GEG 49 .0 .0 46.0
GFK 452 .0 .0 46.0
GPT 289 .0 .0 46.1
GRB 975 .1 .1 46.1
GRR 1229 .1 .1 46.2
GSO 2010 .1 .1 46.3
GSP 1850 .1 .1 46.5
GTF 136 .0 .0 46.5
GUC 21 .0 .0 46.5
HDN 9 .0 .0 46.5
HLN 262 .0 .0 46.5
HNL 37368 2.5 2.5 49.0
HOU 13106 .9 .9 49.8
HPN 2008 .1 .1 49.9
HRL 559 .0 .0 50.0
HSV 1714 .1 .1 50.1
HTS 1 .0 .0 50.1
IAD 16890 1.1 1.1 51.2
IAH 3954 .3 .3 51.5
ICT 1836 .1 .1 51.6
IDA 392 .0 .0 51.6
IND 6745 .4 .4 52.1
INL 146 .0 .0 52.1
ISP 111 .0 .0 52.1
ITH 196 .0 .0 52.1
JAN 1720 .1 .1 52.2
JAX 2556 .2 .2 52.4
JFK 83805 5.5 5.5 57.9
KOA 3132 .2 .2 58.1
LAN 1241 .1 .1 58.2
LAS 10606 .7 .7 58.9

15. LAX 140072 9.2 9.2 68.1
LBB 1610 .1 .1 68.2
LEX 998 .1 .1 68.3
LFT 7 .0 .0 68.3
LGA 23233 1.5 1.5 69.8
LGB 2124 .1 .1 69.9
LIH 3239 .2 .2 70.1
LIT 1334 .1 .1 70.2
LNK 882 .1 .1 70.3
LSE 502 .0 .0 70.3
MAF 2617 .2 .2 70.5
MBS 1426 .1 .1 70.6
MCI 6490 .4 .4 71.0
MCO 9624 .6 .6 71.6
MDT 1125 .1 .1 71.7
MDW 5147 .3 .3 72.1
MEM 23721 1.6 1.6 73.6
MFE 408 .0 .0 73.6
MFR 25 .0 .0 73.6
MGM 179 .0 .0 73.7
MHT 112 .0 .0 73.7
MIA 26965 1.8 1.8 75.4
MKE 497 .0 .0 75.5
MLB 61 .0 .0 75.5
MLI 1957 .1 .1 75.6
MLU 36 .0 .0 75.6
MOB 801 .1 .1 75.7
MOT 39 .0 .0 75.7
MQT 69 .0 .0 75.7
MRY 37 .0 .0 75.7
MSN 1050 .1 .1 75.7
MSO 1 .0 .0 75.7
MSP 28722 1.9 1.9 77.6
MSY 3730 .2 .2 77.9
MYR 72 .0 .0 77.9
OAK 2159 .1 .1 78.0
OGG 15364 1.0 1.0 79.0

16. OKC 5393 .4 .4 79.4
OMA 1745 .1 .1 79.5
ONT 4161 .3 .3 79.8
ORD 95167 6.3 6.3 86.0
ORF 884 .1 .1 86.1
PBI 393 .0 .0 86.1
PDX 749 .0 .0 86.2
PFN 251 .0 .0 86.2
PHF 105 .0 .0 86.2
PHL 4554 .3 .3 86.5
PHX 24508 1.6 1.6 88.1
PIA 253 .0 .0 88.1
PIR 2 .0 .0 88.1
PIT 3669 .2 .2 88.4
PLN 388 .0 .0 88.4
PNS 965 .1 .1 88.5
PSP 185 .0 .0 88.5
PVD 1128 .1 .1 88.5
PWM 752 .0 .0 88.6
RAP 165 .0 .0 88.6
RDM 24 .0 .0 88.6
RDU 8905 .6 .6 89.2
RHI 95 .0 .0 89.2
RIC 934 .1 .1 89.3
RNO 2781 .2 .2 89.4
ROA 454 .0 .0 89.5
ROC 1541 .1 .1 89.6
RST 1253 .1 .1 89.7
RSW 1391 .1 .1 89.7
SAN 25772 1.7 1.7 91.4
SAT 6071 .4 .4 91.8
SAV 1277 .1 .1 91.9
SBA 540 .0 .0 92.0
SBN 2222 .1 .1 92.1
SCE 608 .0 .0 92.1
SCK 51 .0 .0 92.1
SDF 2312 .2 .2 92.3

17. SEA 9261 .6 .6 92.9
SFO 46946 3.1 3.1 96.0
SGF 818 .1 .1 96.1
SHV 1294 .1 .1 96.1
SJC 15912 1.0 1.0 97.2
SJU 2594 .2 .2 97.4
SLC 455 .0 .0 97.4
SMF 747 .0 .0 97.4
SNA 6233 .4 .4 97.8
SRQ 253 .0 .0 97.9
STL 8254 .5 .5 98.4
STT 202 .0 .0 98.4
STX 50 .0 .0 98.4
SUX 300 .0 .0 98.4
SWF 2123 .1 .1 98.6
SYR 907 .1 .1 98.6
TLH 1163 .1 .1 98.7
TOL 1 .0 .0 98.7
TPA 4142 .3 .3 99.0
TRI 78 .0 .0 99.0
TUL 6052 .4 .4 99.4
TUS 2897 .2 .2 99.6
TVC 1630 .1 .1 99.7
TYS 2217 .1 .1 99.8
VPS 111 .0 .0 99.8
XNA 2356 .2 .2 100.0
Total 1519758 100.0 100.0

18. Top 4 BusiestAirports:-
Airport
Code
Airport
Name ORIGIN DEST Total
LAX Los Angeles 155745 140072 295817
ABQ Albuquerque 10439 229381 239820
DFW DallasFort
Worth 131597 99047 230644
ORD O’hare
International 105448 95167 200615
ExtractedData and Calculationsare presentinthe below attachedexcel sheet
TOPAIRPORTOUTPU
T.xlsx

19. 4. Identify Top 5 Airlines
Nowthat we have got the top 4 busiestairports,we filteredthe datatoget the 4 busiestairportsinthe
ORIGIN and DEST Columns
We usedFrequencyanalysisonUNIQUE_CARRIERcolumntofindthe top 5 airlinesoperatingonthe 4
busiestairports.

21. 5. Seasonality in terms of Delay and Operating Performance
We have usedthe filtereddatasetfortop4 busiestairportssince the seasonalityintermsof delayhasto
be plottedforthose airports
Stepsfollowed:-
1)Filteredthe datafortop 4 busiestairports
Belowisthe screenshotforScriptto filterthe dataforthe top5 Airlinesandcreate anew dataset(.sav)
file :-
2)UsedDEP_DELAY_GROUP and ARR_DELAY_GROUP as the parameters forDelayas it containssimple
integercategorical datamappedagainstminutesof delay.Foreg:- anythingupto15 minutesdelay
maps to1, 37 minutesdelayis2, 105 minutesdelayis7and so on.
3)Createda newcolumnnamedTOTAL_DELAYwhichcontainsthe sum of DEP_DELAY_GROUP and
ARR_DELAY_GROUP parameters
4)UsedClusteredScatterplot asfollows

22. TOTAL_DELAY is plottedonY Axis,MONTH onX AxisandsplitbasedonUNIQUE_CARRIER to compare
the performance of the topairline playersandalsocheckthe seasonalityof delay

23. We getthe belowScatterPlot .
* Chart Builder.
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=MONTH TOTAL_DELAY
UNIQUE_CARRIER MISSING=LISTWISE
REPORTMISSING=NO
/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource(id("graphdataset"))
DATA: MONTH=col(source(s), name("MONTH"), unit.category())
DATA: TOTAL_DELAY=col(source(s), name("TOTAL_DELAY"), unit.category())
DATA: UNIQUE_CARRIER=col(source(s), name("UNIQUE_CARRIER"),
unit.category())
GUIDE: axis(dim(1), label("MONTH"))
GUIDE: axis(dim(2), label("TotalDelay"))
GUIDE: legend(aesthetic(aesthetic.color.exterior), label("UNIQUE_CARRIER"))
ELEMENT: point(position(MONTH*TOTAL_DELAY), color.exterior(UNIQUE_CARRIER))
END GPL.

24. Performance of WN Airline isthe bestintermsof ontime arrival and departure andthat of UA and AA
beingthe worst
As we can observe fromthe charts,duringthe secondand thirdquarterof the yearwe see a lot of
delaysascomparedto the firstand the last quarter.
6. Conclusion
We have noteddown a fewobservationsandfindingsaftercompletingthe assignmentwhichare as
follows
 Wheneverwe have averylarge datasetto deal with,HypothesisTestingisthe bestapproach
and itconvertsthe data intoa practical sample toworkupon.
 Understandingthe sample datawell isanintegral partof the BusinessIntelligenceandData
MiningProcess.
 Top 4 busiestairportsfromthe assignmentdataare LAX,ABQ,DFW,ORD.
 Top 5 airline carriersoperatingonthose 4 busiestairportsare AA ,NW,PS,UA,WN
 WN Airline isthe bestintermsof ontime arrival and departure.AirlinesUA andAA are the
worstin termsof delay.
 Duringthe secondand thirdquarterof the yearwe see a lotof delaysas comparedtothe first
and the lastquarter.
Thisassignmenthashelpedustogaina detailedinsightinto the applicationof BusinessIntelligence and
Data Mining.