1. Predicting the performance of US Airline carriers
Applied Business Analytics & Business Intelligence (BIT 5534)
Submitted by:
Group 4
Akash Yadav & Suresh Malhotra

2. Agenda Problem Definition Data Preparation Data Exploration1
Problem Definition 2
Data Preparation 3
Data Exploration 4
Modeling & Analysis 5
Modeling Selection & Comparison 6
Recommendation 7
Customized Models & Future work
Virginia Tech

3. Model Comparison & Selection
Business Problem
Problem: Insufficient information availability on Flight carriers & their flights.
Traveler’s concern:
Which Flight carrier is better & what are the chances of a flight delay.
Our Goal:
Predict flight carriers performance based on delay time in future.
Determine the main causes of flight delay & suggest improvements.
What’s in it for customers or traveler
Able to make flight reservations for time-crunch business meetings
Ease of choosing inter-connected flights and airports ensuring better services
Educate themselves to make better decision while making a flight reservation.
Note: Data Source: (
Define Problem
Data Preparation
Data Exploration
Modeling
Model Comparison & Selection
Recommendation
Virginia Tech

4. Data Definition & Preparation
Remove Missing Value Cases
Scrub-off Outliers
Training / Validation
Data Exploration(next step)
Multivariate Analytical Models
Variable Dictionary
Attribute
Data Type
Description
year
Nominal
Time horizon of data.
month
Period of Year.
carrier
Airline company code.
carrier Name
Name of Airline carrier.
airport
Airport code.
airport Name
Name of Airport
arr-flights
Continuous
No. of flights arriving.
carrier_ct
Flights delayed due to
airline’s own issue
weather_ct
weather issues.
nas_ct
National Aviation system
issues/problems.
security_ct
security issues.
late_aircraft_ct
previous flight delay.
arr_delay
Delay time (Target variable)
Missing values ?
Independent Variables
Define Problem
Data Preparation
Data Exploration
Modeling
Model Comparison & Selection
Recommendation
Virginia Tech

5. Model Comparison & Selection
Data Exploration
1) Carrier_ct, nas_ct & later_aircraft_ct: Better predictors of flight delay. (Red Boxes)
Reason: Small variation from the mean (red trend lines) & more compact variation of data between arr_delay and above variables.
2) weather_ct & security_ct: Least contributors in prediction objective.
Reason: Large variation from the mean (red trend lines) & more scattered pattern of data between arr_delay and above variables.
3) Small or No Redundancy:
Reason: Since the plots among independent variables are more scattered or widespread in space which indicates weak correlation. (This is a good indication as it validates independency of variables).
Define Problem
Data Preparation
Data Exploration
Modeling
Model Comparison & Selection
Recommendation
Virginia Tech

6. Model Comparison & Selection
Models & Analysis
Linear Regression Used a linear combination of 5 independent variables for predicting the target. Plot (below) shows predicted values (using regression) of avg. flight delay against actual values of delay.
PCA Analysis
Created new variables from given variables.
2 new variables sufficient for analysis as they cover 79% of variation in data.
Cluster Analysis
Better for data mining.
Clusters data with similar attributes/characteristics.
Hierarchical: estimated no. of clusters = 20
K-means: Optimal Number of clusters = 21
Decision Tree
Creates groups of data with similar attributes. Data split is based on some threshold value of variables.
No. of Split = 35
Neural Network
Created a hidden layer of new variables which receive data from current variables.
Utilizes a regression like approach and gives prediction values as output.
Below plot: Output vs Actual values of delay.
Diff
Groups
Define Problem
Data Preparation
Data Exploration
Modeling
Model Comparison & Selection
Recommendation
Virginia Tech

7. Model & Analysis (continued…)
R2 : Higher value depicts that model is capable of accounting or explaining most of the variation in the data which is important. (Refer plots below)
RMSE: Root mean square error Low RMSE value means predicted values are close to the actual values or the deviation from actual value is small.
Note 1: All the models perform quite well as evident from significantly high R2 and low RMSE values.
Note 1: Consistent performance on both Training & Validation set which ensures that data preparation was good and models are acceptable.
Linear Regression & Neural Network Model - Best
Since already R2 is high, so 5-6% increase matters a lot. Further, RMSE is also low in comparison for these two models.
Modeling Technique
Modeling Highlight
Training
Validation
RSquare
RMSE
Linear Regression
5 Independent Variables
95.5
1851
95.6
1805
Principle Component Analysis
2 Principle Components
90.5
2688
90.8
2617
Cluster Analysis
21 Clusters
87.8
3053
87.5
3042
Decision Trees
35 Splits
90.9
2639
90.3
2694
Neural Networks
1 Hidden Layer, 6 Nodes, Learning Rate = 0.1, Transform Covariates
96.2
1703
96.02
1728
PCA
Neural
Define Problem
Data Preparation
Data Exploration
Modeling
Model Comparison & Selection
Recommendation
Virginia Tech

8. Model Comparison & Selection
Neural Network Model - Selected
Regression
Neural
- neural
- linear regression
Why Neural Network !!!
In comparison to linear regression model, Neural Network Model is slightly better on R2 and RMSE values.
Profiler depicts that variables variation profiles match much closely with the desired profiles in case of Neural Model.
Define Problem
Data Preparation
Data Exploration
Modeling
Model Comparison & Selection
Recommendation
Virginia Tech

9. Recommendation & Key points
Good to know:
- Although we have focused more on Delay time, the model has the potential to predict flight cancellations and other performance metrics.
How to ensure an efficient model
- Its critical to explore and prepare the data efficiently. - Treating missing values and removing outliers is of utmost importance for model stability. - Model should be created on training data and then tested on Validation data set. (Modify model if needed)
What else could be done or added to scope
- Additional predictor variables can be included like distance between arrival & departure airports, air traffic etc. - Use data from different source and different time horizon. - Focus on specific airports or air carriers. - Using Forecast models for prediction.
Define Problem
Data Preparation
Data Exploration
Modeling
Model Comparison & Selection
Recommendation
Virginia Tech

10. Customized Models and Future Work
Air Carrier R2
RMSE
Logworth
Comment & LogWorth AA
American Airlines
97.38
1554
A - 125, B - 152, C - 181
All significant. Security - 4 DL
Delta Airways
95.79
1568
A - 555, B - 334, C - 135
All significant. Security - 3.4 WN
Southwest Airlines
95.68
1485
All significant. Security - 4.2 AS
United Airlines
98.07
2554
A - 737, B - 409, C - 114
Security_ct insignificant. B6
JetBlue
96.82
1784
A - 318, B - 165, C - 71
All significant. Security - 20
Busiest Airport
ATL
Atlanta, GA
96.72
1378
A - 190, B - 179, C - 55
LAX
Los Angeles, CA
97.19
2002
A - 344, B - 246, D - 123
All significant. Security - 31
ORD
Chicago, IL
96.07
2819
A - 177, B - 145, D – 31
Weather and security - Low
DFW
Dallas, TX
97.12
776
A - 95, B - 480, D - 126
JFK
New York, NY
96.10
2287
A - 156, B - 125, D – 55
All significant. Security - 4.9
Best Airport (US)
SLC
Salt Lake City
97.6
1157
A - 201, B - 292, D - 135
All significant. Security - 17
DCA
Washington
95.4
1179
A - 118, B - 235, C - 117
All significant. Security – 2.2
SEA
Seattle-Tacoma
97.5
1026
A - 228, B - 290, D - 128
All significant. Security - 1.4
PDX
Portland
95.3
810
A - 157, B - 283, D - 123
All significant. Security – 4.5
MSP
Minneapolis
97.1
1297
A - 180, B - 221, D - 64
All significant. Security – 4.7
Delta & Airports
96.28
1120
A - 18, B - 23, D - 10
Security insignificant
88.29
1065
A - 25, B - 3.7, D - 4.4
Security - Zeroed
94.5
1868
A - 7.5, B - 17, D - 8.4
All significant.
88.28
1140
A - 11, B - 5.6, D - 10
98.4
2183
B - 4.8, C - 8.1, D - 7.4
Nas_ct Insignificant.
A = nas_ct; B = late_aircraft_ct; C = weather_ct; D = carrier_ct
The Dashboard Story
(Future Scope of Work)
Individual models to be prepared for each flight carrier, airport and a combination of flight carrier + airport for monthly predictions.
Prediction from the model will be used to provide information to the travelers.
Description Models will inform the current performance of flight carriers and airports based on historical data.
Interactive Visual Information Delivery is the goal !!
Virginia Tech

11. Thank You
Virginia Tech